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Transforming Healthcare with Multimodal AI

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AI
Artificial Intelligence
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data series
Machine Learning
MedTech
multimodal AI
  • Healthcare is flooded with diverse data from multiple sources, including imaging, genomic sequences, lifestyle factors, and clinical records
  • The volume and diversity of healthcare data pose challenges for medical practitioners and hinder the delivery of quality care
  • Relying solely on healthcare professionals to manage this diverse data is impractical
  • Multimodal AI can amalgamate, analyse, and utilise complex healthcare data, offering transformative potential across delivery systems
  
Transforming Healthcare with Multimodal AI

On April 1, 2024, Peter Arduini, President, and CEO of GE Healthcare, announced the acquisition of MIM Software, a leading provider of medical imaging analysis and artificial intelligence (AI) solutions in fields such as radiation oncology, molecular radiotherapy, diagnostic imaging, and urology, serving diverse healthcare settings worldwide. "We are excited to welcome MIM Software, recognised for its innovation in multimodal image analytics and workflow," said Arduini.
 
Multimodal AI

Multimodal AI is at the forefront of modern methodologies, synthesising diverse AI technologies to concurrently interpret various data types, a capability commonly referred to as handling multiple modalities. This approach has the potential to transform processes and enhance patient care. In today's healthcare environment, the emergence of multimodal AI signifies a leap forward, particularly within medical technology. The inundation of data from various sources such as imaging, time series, genomic sequences, lifestyle factors, and clinical records pose a challenge for individual healthcare professionals to merge and interpret. The expectation for clinicians to proficiently manage and utilise such diverse datasets alongside their primary medical specialisation is unrealistic. Multimodal AI offers a solution. Tailored for medical applications, it harnesses the power of sophisticated algorithms and machine learning techniques, to integrate and interpret disparate data streams. By doing so, the technology furnishes healthcare providers with insights and actionable intelligence, thus empowering them to make informed decisions and drive improved patient outcomes.
 
In this Commentary

This Commentary explores the complexities of healthcare data, encompassing a broad spectrum from imaging to clinical records. Multimodal AI emerges as a pragmatic solution, harmonising disparate data sources to provide insights and streamline healthcare delivery. The recent acquisition of MIM Software by GE Healthcare underscores the increasing significance of this approach. Through a historical lens, we examine the evolution of multimodal AI and its progress in deciphering various data formats. In healthcare contexts, multimodal AI has the potential to transform patient care by combining data to formulate personalised diagnoses and treatment strategies. In tackling data complexities, the technology equips healthcare professionals with efficient tools for managing intricate datasets. Furthermore, its adoption yields tangible benefits for MedTech companies by expediting innovation cycles and enhancing operational efficiency. Ultimately, multimodal AI instigates a shift in healthcare delivery and administration, fostering improved health outcomes.
 
A Brief History

Multimodal AI has evolved through advancements in AI, data science, and interdisciplinary research. The foundation of AI was established in the mid-20th century by pioneers like Alan Turing and John McCarthy, focusing on symbolic logic and rule-based reasoning. However, early AI systems had limited capabilities to process diverse data types. The 1980s witnessed the rise of machine learning as an area within AI research. Techniques such as neural networks, decision trees, and Bayesian methods emerged, enabling systems to learn from data and make predictions.
 
During the 1990s and early 2000s, progress was made in computer vision and natural language processing (NLP), laying the foundation for multimodal AI by enabling the processing and understanding of visual and textual data. The early 21st century saw a growing interest in integrating multiple data approaches within AI systems. Researchers explored techniques to combine information from sources such as text, images, audio, and sensor data to enhance analyses.
The advent of deep learning in the 2010s transformed AI, fuelled by advances in neural network architectures and computational resources. Deep learning techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), enabled progress in processing multimodal data. In recent years, AI fusion technology has become increasingly prevalent across various domains, including healthcare, finance, autonomous vehicles, and multimedia analysis. These applications leverage sophisticated AI models capable of integrating and interpreting data from diverse sources to extract actionable insights.

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The development of multimodal AI continues to be driven by interdisciplinary collaboration between researchers in AI, computer science, neuroscience, cognitive science, and other fields. This collective effort aims to advance the capabilities of AI systems to understand and interact with complex, poly modal environments more effectively.

Multimodal AI in a Healthcare Setting

To illustrate the application of multimodal AI in healthcare, envision a scenario where a patient communicates symptoms through a voice-to-text interface with a medical practitioner’s office. The text is then managed by a computer utilising natural language processing (NLP), empowering machines to understand and interpret human language. Simultaneously, the patient's recent medical images and electronic health records (EHR) are accessed and undergo examination by computer algorithms. Consider that these EHRs are derived from speech recognition processes, transcribing spoken notes from prior examinations conducted by healthcare professionals. These disparate data sources are amalgamated to construct a health profile, offering insights into the patient's medical history and current condition. By harnessing machine learning algorithms, this profile, developed in split seconds, lays the groundwork for crafting personalised diagnoses and treatment plans that surpass the limitations of singular modal approaches. Moreover, the system remains dynamic, evolving alongside the patient's treatment journey. It continuously learns and adapts, aligning with the patient's status to ensure the delivery of optimal therapies. The insights obtained from this multimodal AI approach can be shared with healthcare providers to facilitate informed decision-making and encourage collaborative patient care. In an era marked by vast and rapidly growing healthcare demands, escalating healthcare costs and constrained resources, the significance of this approach cannot be overstated. By encapsulating the complexities inherent in medical diagnoses and treatment plans, multimodal AI offers a superior alternative to traditional singular methods.
 
Healthcare's Data Challenges and Multimodal AI

Let us examine the current situation in a little more detail. In today's digital age, the healthcare industry is a prolific generator of data, contributing ~30% of the world's data volume. This figure is projected to surge further, with a compound annual growth rate (CAGR) of ~36% expected by 2025. Such growth outpaces key sectors like manufacturing, financial services, and media & entertainment by significant margins, emphasising the pace of data expansion within healthcare.
 
The challenges inherent in managing vast amounts of data are not solely due to their abundance; difficulties also arise from their diverse formats, ranging from structured data to unstructured datasets encompassing text, images, graphs, videos, and more. Despite the potential held within such data, significant portions remain untapped. The primary reason for this underutilisation is the inadequacy of conventional tools to unlock the latent insights embedded within diverse data types. Traditional technologies falter in efficiently searching, processing, and analysing these massive and heterogeneous datasets. As a result, there is a need for specialised methodologies and advanced technologies capable of extracting actionable intelligence from this wealth of information.
 
Enter multimodal AI: a transformative solution poised to unlock the value in unstructured datasets. By synthesising advanced algorithms with diverse data modalities, this technology offers a comprehensive approach to data analysis, transcending the limitations of traditional tools. Through techniques like natural language processing, computer vision, and deep learning, multimodal AI empowers healthcare professionals to navigate the complexities of data with unprecedented precision and efficiency. By leveraging this technology, healthcare providers can overcome the challenges of data and pave the way for innovative advancements in patient care, research, and beyond.
 
Navigating the Data Deluge

Medical practitioners encounter obstacles in their efforts to provide optimal care, improve patient outcomes, and manage costs effectively through data amalgamation and analysis.

Real-time data generation intensifies the pressure on healthcare professionals, demanding rapid analysis to extract actionable insights. However, ensuring data quality and reliability remains an issue due to the prevalence of errors, inconsistencies, and missing values, which can compromise both analytical validity and clinical outcomes.

Interoperability problems further exacerbate the situation, as disparate healthcare systems often employ incompatible technologies and standards, hindering data exchange. The absence of standardised formats and protocols impedes integration and sharing across platforms and organisations, thwarting efforts to leverage data for comprehensive patient care.
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Moreover, privacy and security regulations, such as the American Health Insurance Portability and Accountability Act (HIPAA) and the EU’s General Data Protection Regulation (GDPR), necessitate a balance between safeguarding patient privacy and facilitating data access and sharing. The digital transformation of healthcare increases these concerns, underscoring the urgency of compliance with regulatory standards and robust data protection measures.
Multimodal AI solutions have the capabilities to address these challenges by leveraging advanced encryption techniques, anomaly detection algorithms, and robust audit trails, which strengthen data security and prevent unauthorised access. These AI-powered systems also play a role in ensuring regulatory compliance by identifying potential violations and monitoring adherence to guidelines, thus mitigating compliance risks within healthcare organisations.

Furthermore, effective data interpretation hinges upon domain-specific expertise and a nuanced understanding of clinical contexts. Healthcare professionals must contextualise data within individual patient characteristics, medical histories, and clinical guidelines to make informed decisions, thereby optimising patient care. However, biases inherent in healthcare data pose an obstacle, potentially skewing AI models and predictions. Mitigating biases and promoting equitable healthcare outcomes require a concerted effort towards fairness, transparency, and generalisability in AI model development and deployment.

Addressing these challenges necessitates collaborative efforts among healthcare professionals, data scientists, policymakers, and technology providers. Implementing strategies such as data standardisation, interoperability frameworks, advanced analytics techniques, and robust data governance policies are imperative for overcoming obstacles and unlocking the full potential of healthcare data to enhance patient care and outcomes.
 
Multimodal AI and MedTech Innovation

Multimodal AI extends beyond traditional healthcare practices and has the potential to reshape how MedTech companies tackle healthcare challenges and develop solutions and services for patients. The technology holds promise to accelerate innovation cycles by expediting the development and refinement of novel medical devices and technologies. By integrating various data modalities, including imaging, genomic, and clinical data, it enables firms to uncover insights, leading to the creation of more effective diagnostic tools and treatment solutions. This not only improves the competitive edge of enterprises but also translates into tangible benefits for healthcare providers and patients by offering faster, more accurate diagnostics and therapies.
 
Furthermore, in the realm of personalised care, multimodal AI empowers corporations to tailor interventions to individual patient profiles, encompassing genetic predispositions, lifestyle factors, and treatment responses. Such tailored approaches improve patient outcomes and have the potential to drive market differentiation for MedTech products, which cater to the growing demand for customised healthcare solutions.

Moreover, the integration of multimodal AI into MedTech solutions and services fosters interoperability and connectivity across various healthcare systems and devices. This boosts the efficiency of remote patient monitoring and telemedicine platforms, allowing enterprises to reach underserved populations and geographies more effectively. By leveraging data from wearables, sensors, and remote monitoring platforms, the technology enables proactive healthcare interventions, detecting early warning signs of deterioration, facilitating timely interventions, thus improving patient outcomes, and reducing healthcare disparities.

In addition to driving innovation in product development, multimodal AI contributes to optimising operational efficiency and resource allocation within enterprises. By automating administrative tasks, streamlining work, and analysing data on patient flow and resource utilisation, the technology empowers MedTechs to allocate resources more effectively, reduce costs, and strengthen overall operational performance. This not only translates into improved bottom-line results but also enhances resource allocation for healthcare providers, which ultimately benefits patient care delivery.

The integration of multimodal AI into the medical technology sector catalyses a shift in how healthcare is delivered and managed, paving the way for more efficient, personalised, and accessible healthcare solutions. As corporations continue to harness the power of this technology, the potential for transformative innovation in healthcare delivery and management becomes increasingly possible, promoting better health outcomes and experiences for individuals and populations worldwide.
 
Takeaways

GE Healthcare's acquisition of MIM Software highlights the company's strategic foresight in leveraging MIM's extensive product portfolio, utilised by >3,000 institutions worldwide. Also, it exemplifies Peter Arduini's astuteness in navigating the evolving healthcare technology landscape and emphasises the importance of integrating multimodal AI tools to achieve sustainable growth and gain a competitive edge in today's dynamic healthcare ecosystem. As technology progresses and data complexity increases, multimodal AI's importance is poised to escalate, transforming healthcare's trajectory. The technology’s integration optimises diagnostic and treatment procedures, streamlines administrative functions, and enhances operational efficiency within healthcare systems. Despite challenges such as data complexity and privacy concerns, the ability of multimodal AI to synthesise data and provide actionable insights empowers healthcare professionals, leading to improved patient outcomes. As this technology evolves, it promises to reshape the delivery and management of medical services globally. Multimodal AI has the capacity to reinforce GE Healthcare's leadership in innovation and enhance its competitive position.
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3 months, 2 weeks ago

Is MedTech Entering a New Era of Leadership and Purpose-Driven Innovation?

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Medical Technology
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Artificial Intelligence
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Medtech
  • Effective MedTech leadership in the next decade requires adept navigation of companies through evolving markets, technological advancements, and simultaneous management of established legacy businesses
  • Historically, MedTech leaders have been drawn from a limited pool, potentially slowing effective adaptation to new technologies, and markets
  • This has allowed tech giants to disrupt the sector, emphasising a shift from the development of physical devices to integrated healthcare solutions
  • The 4th industrial revolution (Industry 4.0) is crucial in facilitating the transformation, breaking down traditional boundaries between medical devices, pharmaceuticals, software, and patient data
  • Executives with experience in service-based sectors adjacent to MedTech may be better equipped to lead, leveraging their tech-centric background to capitalise on digital technologies and big data strategies for successful adaptation and thriving in the evolving healthcare ecosystem
 
Is MedTech Entering a New Era of Leadership and Purpose-Driven Innovation?
 
MedTech leadership is at a crossroads, demanding a strategic overhaul to tackle unprecedented sector changes anticipated over the next decade. Navigating this evolving landscape requires reconciling traditional manufacturing expertise and cutting-edge digital capabilities. A forward-thinking CEO with digital acumen is pivotal for innovation, yet the complexities of manufacturing and stringent regulatory frameworks remain crucial. In response, it seems reasonable to suggest that a collaborative leadership approach would be optimal, pairing a visionary CEO with digital expertise alongside a seasoned COO well-versed in manufacturing and regulatory compliance. This, would not only alleviate the burden on a single leader but also combine the strengths of both domains, fostering a more resilient leadership model. By strategically aligning these skill sets, MedTech companies would be better positioned to adeptly bridge the gap between tradition and digital evolution amid the complexities of an increasingly competitive market.

Historically, MedTech leadership, drawn from a limited pool of individuals, may fall short in ensuring commercial success in the coming decade. The sector's reluctance to swiftly embrace emerging technologies has created an opening for tech giants to disrupt it, mirroring the upheavals witnessed in financial markets. To thrive, MedTech companies must shift from developing physical devices to strategically promoting integrated healthcare solutions and services. The 4th Industrial Revolution, (Industry 4.0) plays a pivotal role in this evolution, breaking down traditional boundaries between medical devices, pharmaceuticals, software, and patient data. It reshapes connections among the physical, biological, and digital realms within the healthcare sector, emphasising advanced data and digitalisation strategies.

In this paradigm shift, traditional MedTech executives may find themselves ill-equipped to lead effectively. Executives from adjacent service-based sectors, with a tech-centric background, seem better positioned to spearhead this transformation. Leveraging their expertise, these leaders can adeptly capitalise on digital technologies and utilise big data strategies to navigate and adapt business models. Strategic leadership from executives with a tech-centric background is essential for MedTech companies to survive and thrive in the future.
 
In this Commentary
 
This Commentary has two parts. Part 1: The MedTech Market describes opportunities and challenges within the evolving dynamic global market. Part 2: Navigating MedTech’s Evolutionary Challenges, examines the limitations of current MedTech leadership, suggesting a shift towards diverse skills, backgrounds, and perspectives. Future MedTech leaders need expertise in digital technologies, data analytics, and innovative business models, coupled with an understanding of global markets and a compelling sense of purpose to engage and inspire Generation Zs. Takeaways raise the likelihood that existing MedTech executives may be ill-equipped for upcoming industry transformations, highlighting the potential of leaders from service-based sectors with proven strategic agility and innovation.
 
Part 1
The MedTech Market

Currently, MedTech is undergoing a transformation, and shedding its traditional conservative image. The industry's growth is driven by various factors, such as the aging global population, an uptick in chronic diseases, and an increasing trust in medical devices among clinicians and consumers, which has fostered stronger collaborations between MedTech and pharmaceutical companies. Although the US and the EU continue to be significant contributors to MedTech markets, they face hurdles, including increasingly stringent regulations, shifts in reimbursement policies, and elevated costs linked to advanced medical technologies.
 
About two decades ago, foreseeing constraints, some large MedTechs like Johnson & Johnson (J&J), Abbott, and Medtronic, strategically established manufacturing and research and development (R&D) centres in emerging markets such as Brazil, China, and India. Back then, these markets were undergoing substantial growth, fuelled by burgeoning middle-class populations with an increasing demand for improved healthcare services. This situation not only presented strategic opportunities for continuous expansion but also served as a buffer against the escalating difficulties experienced by MedTechs in the more mature Western markets.
 
Despite facing challenges, the global MedTech market continues to be a promising arena for growth and innovation, extending its reach across diverse sectors and geographies. Projections indicate that its global revenues will reach ~US$610bn in 2024, with an anticipated compound annual growth rate (CAGR) of ~5.2%. This trajectory points towards a substantial market volume of ~US$748bn by 2028. The US stands as the primary revenue contributor, expected to reach ~US$216bn in 2024. Historically, MedTech business models have predominantly targeted affluent markets in the US, Western Europe, and Japan, comprising only ~13% of the world's population but holding a significant market share. This historical skew allowed MedTech leaders to focus their marketing efforts on healthcare providers in prosperous developed regions, benefitting from favourable fee for service reimbursement policies. Notwithstanding, recent years have witnessed a tightening of the wealthy Western markets.
In the coming decade, MedTech sectors in emerging regions are set to experience significant growth. For example, in 2024 China's MedTech revenues are anticipated to realise ~US$46bn, with a projected CAGR to 2028 of ~7.5%. This growth trajectory is expected to culminate in a market volume of ~US$61bn in the near term. In the face of dynamic shifts, MedTech leaders are confronted with the challenge of recalibrating their strategies to ensure sustained success amid challenging global politico-economic conditions and the use of more demanding outcome-based healthcare reimbursement models in traditional wealthy Western markets.


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Following a peak in late 2021, MedTech stocks faced a setback around mid-2022, losing a significant portion of the gains accumulated during the Covid-19 pandemic. By July 2023, growth had slowed, with MedTech valuations showing only a modest increase of ~22% compared to January 2020. During this period, trading multiples experienced a decline, dropping from a peak of ~16x in September 2021 to ~7x by mid-2023, falling below the industry's 10-year average of ~8x.
 
Although there have been some recent improvements, the 2023 EY, Pulse of the MedTech Industry Report raised concerns about challenges ahead for the sector. In the post-Covid landscape, the industry is grappling with significant hurdles, including a notable decline in public valuations and ~30% decrease in financing. These challenges manifest in various aspects, such as a downturn in special-purpose acquisition company (SPAC) deals, a substantial decrease in the total value of initial public offerings (IPOs), and a slump of ~21% in venture capital (VC) funding. Compounding these issues is a decline of ~44% in merger and acquisition (M&A) activity.
 
Traditionally, M&A has played a crucial role for MedTechs, contributing to scale, operational leverage, financial performance, product portfolio diversification, improved therapeutic solutions, and international expansion - all while maintaining core manufacturing structures and strategies. Moreover, post-Covid, revenue growth has experienced a significant dip, dropping from ~16% in 2021 to ~3.5% in 2022, and remaining flat in 2023. The anticipated future growth of ~5% may encounter challenges due to a potential scarcity of new disruptive product offerings. These challenges have implications for equity investment, which hit a seven-year low in 2023, declining by ~27% to ~US$14bn. Notably this impacts smaller, innovation-driven firms.

A positive recent trend is the rapid growth of digital health with expected global revenues set to reach ~US$194bn by 2024, with a projected CAGR of ~9% from 2024 to 2028, which would deliver a market volume of ~US$275bn by 2028. China leads in global revenue generation for digital health, reaching ~US$53bn in 2024. However, many large diversified MedTechs with legacy products in slow-growing markets have yet to capitalise on this trend.
 
MedTech stands at a critical juncture, navigating challenges that necessitate a strategic overhaul for sustained success. The decline in key financial indicators and the sluggish pace of innovation pose significant threats, obliging leaders to embrace transformative strategies and capitalise on emerging trends, particularly in digital health, to secure a resilient future.
 
Part 2
Navigating MedTech’s Evolutionary Challenges

Changes in the MedTech landscape introduce difficulties for executives striving to stay abreast of technological advances and transformative shifts, particularly in emerging economies. Compounding these obstacles is the prevalence of middle-aged men in leadership roles, perpetuating traditional management styles that may impede the necessary adaptations required for growth.

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Despite women constituting >50% of the MedTech workforce and significantly influencing healthcare decisions, they are underrepresented in executive positions. Addressing these disparities is not just a moral obligation but a strategic imperative to unlock the full potential by embracing diverse perspectives and talents. The historical contributions of women in healthcare underscores the urgency of closing the gender gap in MedTech leadership.
Further complicating matters is the median age of C-suite executives; ~56. This demographic nearing retirement, suggests that many company leaders embarked on their professional journeys before the pervasive influence of the Internet, email, and the rise of social media platforms, creating a technological generation gap. The sector's historical reliance on affluent markets in the US and Europe, coupled with fee-for-service healthcare policies, poses challenges in adapting to emerging markets and reimbursement policies centred on patient outcomes.
 
The integration of artificial intelligence (AI) and machine learning (ML) into medical devices adds another layer of complexity, necessitating a paradigm shift. However, this transformation proves challenging for traditional leaders, given that these impactful changes unfolded during the mature phases of their careers. Notably, out of ~691 FDA-approved algorithms functioning as medical devices, ~35% received clearance in 2022 and 2023. Despite the urgent need for adaptation, persistent leadership obstacles hinder progress, particularly in understanding and aligning with the fluidity of rapidly evolving technologies in new markets.
 
MedTech leaders face challenges in understanding the dynamics of emerging markets, especially in economically vibrant regions like Brazil, India, China, and sizable African nations. These areas experience economic development and a growing middle class, leading to increased demand for advanced healthcare. The global acumen gap is further intensified by a lack of first-hand experience among these professionals in these regions, presenting a hurdle to effective guidance. Consequently, many MedTech executives seem to struggle with delivering impactful direction, given the disconnect with transformative trends in emerging markets and advancing technologies. Addressing these perspective and knowledge gaps requires more than incremental adjustments; it calls for a shift in mindset and a recalibration.
 
Significant changes in MedTech call for a departure from traditional top-down directives towards an empowering leadership style. The sector now demands a new breed of leaders - tech savvy individuals with global experience capable of understanding and connecting with the needs and aspirations of Generation Z employees. This demographic shift in the workforce requires leaders who not only comprehend evolving technologies but also align with the values and expectations of today's highly skilled, young professionals. Beyond the pursuit of shareholder value, this demographic craves purpose-driven leadership and seeks companies with a clear sense of mission and societal impact. In this context, MedTech companies face a stark choice: adapt to lead with purpose or risk being left behind.
 
Takeaways

The future leadership of MedTech companies stands at a critical juncture as it is potentially faced with unprecedented changes over the next decade. While the necessity of a forward-looking CEO with digital acumen is essential for strategic innovation, the persisting challenges of manufacturing and regulatory frameworks highlight the need for a more collaborative leadership approach. To address this, we have proposed a strategic collaboration between a visionary CEO equipped with digital expertise and a seasoned COO skilled in manufacturing and compliance. It seems reasonable to assume that this would not only ease the burden on a single individual but also harness the strengths of both, fostering a more resilient leadership approach. Further, it recognises that navigating change demands a balance between embracing digital evolution and maintaining a strong foundation in traditional manufacturing and regulatory compliance. Future MedTech leaders must be able to bridge knowledge and perspective gaps, align with emerging technologies, and connect with the aspirations of the evolving workforce. The shift towards a more empowering leadership style, coupled with an understanding of Industry 4.0 principles and the dynamics of emerging markets, is essential for sustained success in a rapidly evolving market.

The urgency for MedTech leaders to adopt a forward-thinking, adaptable, and purpose-driven approach cannot be overstressed. The industry's capacity to allure and retain talent, foster innovation, and make substantial contributions to global healthcare pivots on a commitment to purposeful leadership and the incorporation of transformative strategies. In this demanding journey, the judicious collaboration between a forward-looking CEO and a traditional COO emerges as a strategic imperative, ensuring a comprehensive and resilient leadership model that can thrive in the next decade. 
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5 months, 3 weeks ago

MedTechs Battle with AI for Sustainable Growth and Enhanced Value

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Medical Technology
Tags:
AI
Artificial Intelligence
commentary
Machine Learning
Medical Technology
MedTech
MedTechs Battle with AI for Sustainable Growth and Enhanced Value
 
Preface
 
The medical technology industry has experienced significant growth, consistently surpassing the S&P index by ~15 percentage points. This success is rooted in the early 1990s, a time when capital was costly, with interest rates ~10%. However, as we moved closer to 1998, interest rates began to recede, settling just below 7%. This early era of growth was not devoid of challenges. The US was still grappling with the aftermath of the oil embargo imposed in 1973 by the Organization of the Petroleum Exporting Countries (OPEC), which was a response to the American government's support for Israel during the Yom Kippur War and had lasting consequences. The oil crisis triggered hyperinflation, leading to a rapid escalation in the prices of goods and services. In response, the US Federal Reserve (Fed) raised interest rates to a historic high of 17% in 1981, which was aimed at curbing inflation but came at the price of increasing the cost of borrowing. As we entered the 1990s, the landscape shifted. The Fed’s monetary policies began to work, inflation started to decline, and interest rates fell to ~10%, eventually dipping below 7% in 1998. This created conditions for increased investments in research and development (R&D) and the American economy blossomed and benefitted from the internet becoming mainstream. It was during this period that many medical technology companies developed innovative medical devices, which were not only disruptive but also found a receptive global market characterized by significant unmet needs and substantial entry barriers. In the ensuing years, the industry thrived and matured. Fast-forward to the present (2023), and we find ourselves in a different scenario. Over the past five years, numerous large, diversified MedTechs have struggled to deliver value. One explanation for this is that growth of these enterprises over the past three decades, except for the early years, was primarily driven by mergers and acquisitions (M&A), often at the expense of prioritizing R&D. Consequently, many large MedTechs did not leverage evolving technologies to update and renew their offerings and are now heavily reliant on slow-growth markets and aging product portfolios. Navigating a successful path forward would be helped by a comprehensive embrace of artificial intelligence (AI) and machine learning (ML) strategies, since these technologies possess the potential to transform how MedTechs operate, innovate, and serve their stakeholders.
 
In this Commentary

This Commentary explores the role of artificial intelligence (AI) in reshaping the future landscape of the MedTech industry in pursuit of sustainable growth and added value. We focus on the impact AI can have on transforming operational methodologies, fostering innovation, and enhancing stakeholder services. Our aim is to address five key areas: (i) Defining Artificial Intelligence (AI): Describes how AI differs from any other technology in history and sheds light on its relevance within the MedTech sector. (ii) Highlighting AI-Driven MedTech Success: In this section, we preview three leading corporations that have utilized AI to gain access to new revenue streams. (iii) Showcasing a Disruptive AI-Powered Medical Device: Here, we provide an overview of the IDx-DR system, an innovation that has brought disruptive change to the field of ophthalmology. (iv) The Potential Benefits of Full AI Integration for MedTechs: This section briefly describes 10 potential benefits that can be expected from a comprehensive embrace of AI by MedTechs. (v) Potential Obstacles to the Adoption of AI by MedTechs: Finally, we describe some obstacles that help to explain some MedTechs reluctance to embrace AI strategies. Despite the substantial advantages that AI offers, not many large, diversified enterprises have fully integrated these transformative technologies into their operations. Takeaways outline the options facing enterprises.
 
Part 1

Defining Artificial Intelligence (AI)

Artificial Intelligence (AI) is a ground-breaking concept that transcends the simulation of human intelligence. Unlike human cognition, AI operates devoid of consciousness, emotions, and feelings. Thus, it is indifferent to victory or defeat, tirelessly working without rest, sustenance, or encouragement. AI empowers machines to perform tasks once exclusive to human intelligence, including deciphering natural language, recognizing intricate patterns, making complex decisions, and iterating towards self-improvement. AI is significantly different to any technology that precedes it. It is the first instance of a tool with the unique capabilities of autonomous decision making and the generation of novel ideas. While all predecessor technologies augment human capabilities, AI takes power away from individuals.
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AI employs various techniques, including machine learning (ML), neural networks, natural language processing, and robotics, enabling computers to autonomously tackle increasingly complex tasks. ML, a subset of AI, develops algorithms that learn, adapt, and improve through experience, rather than explicit programming. The technology’s versatile applications span image and speech recognition, recommendation systems, and predictive analytics. In the quest to comprehend the intersection of artificial and human intelligence, we encounter Large Language Models (LLMs), like ChatGPT, which recently have gained prominence in corporate contexts. These advanced AI models grasp and generate human-like text by discerning patterns and context from extensive textual datasets. LLMs excel in language translation, content generation, and engaging in human-like conversations, effectively harnessing our linguistic abilities.

Part 2

Highlighting AI-Driven MedTech Success

This section briefly describes three examples of MedTechs that have successfully leveraged AI technologies to illustrate how AI’s growing influence drives improvements in accuracy, efficiency, patient outcomes and in the reduction of costs, which together, and in time, are positioned to transform healthcare.
 
Merative, formally Watson Health, a division of IBM that specialised in applying AI and data analytics to healthcare. In 2022, the company was acquired by Francisco Partners, an American  private equity firm, and rebranded Merative. The company leverages AI, ML, and LLMs to analyse extensive medical datasets that encompass patient records, clinical trials, medical literature, and genomic information. These technologies empower healthcare professionals by facilitating more informed decisions, identifying potential treatment options, and predicting disease outcomes. For instance, Merative employs ML to offer personalised treatment recommendations for cancer patients based on their medical histories and the latest research. Integrating LLMs enables natural language processing to extract insights from medical literature, helping healthcare providers stay current with scientific and medical advancements.
 
Google Health, a subsidiary of Alphabet Inc., focuses on using AI and data analysis to improve healthcare services and patient outcomes. It employs AI and ML to develop predictive models that can identify patterns and trends in medical data, which improve early disease detection and prevention. One notable application is in medical imaging, where the company's algorithms can assist radiologists to identify anomalies in X-rays, MRIs, and other images. LLMs are used to interpret and summarize medical documents, making it easier for healthcare professionals to access relevant information quickly. Google Health also works on projects related to drug discovery and genomics, leveraging ML to analyze molecular structures and predict potential drug candidates.
Medtronic is a global leader in medical technology, specializing in devices and therapies to treat various medical conditions. The company incorporates AI, ML, and LLMs into their devices and systems to enhance patient care. For instance, in the field of cardiology, Medtronic's pacemakers and defibrillators collect data on a patient's heart rhythms, which are then analyzed using AI algorithms to detect irregularities and adjust device settings accordingly. This real-time analysis helps to optimize patient treatment. Medtronic also employs AI in insulin pumps for diabetes management that can learn from a patient's blood sugar patterns and adjust insulin delivery accordingly. Additionally, LLMs are used to extract insights from electronic health records (EHR) and clinical notes, which help healthcare providers to make more personalized treatment decisions.
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Part 3

Showcasing a Disruptive AI-Powered Medical Device

AI has been applied to various medical imaging tasks, including interpreting radiological images like X-rays, CT scans, and MRIs and there are numerous AI-driven medical devices and systems that have emerged and evolved in recent years. As of January 2023, the US Federal Drug Administration (FDA) has approved >520 AI medical algorithms, the majority of which are related to medical imaging. Here we describe just one, the IDx-DR system, which was developed by Digital Diagnostics. In 2018, it became the first FDA-approved AI-based diagnostic system for detecting diabetic retinopathy. If left untreated, the condition can lead to blindness. Globally, the prevalence of the disease among people living with diabetes is ~27% and every year, >0.4m people go blind from the disorder. In 2021, globally there were ~529m people with diabetes, which is expected to double to ~1.31bn by 2050.
 
The IDx-DR device utilizes AI algorithms to analyze retinal images taken with a specialized camera and accurately detects the presence of retinopathy that occurs in individuals with diabetes when high blood sugar levels cause damage to blood vessels in the retina. Significantly, the device produces decisions without the need for retinal images to be interpreted by either radiologists or ophthalmologists, which allows the system to be used outside specialist centres, such as in primary care clinics. Advantages of the system include: (i) Early detection, which can improve outcomes and quality of life for individuals with diabetes. (ii) Efficiency. The system analyzes images quickly and accurately, providing results within minutes, which allows healthcare providers to screen a larger number of patients in a shorter amount of time. (iii) Reduced healthcare costs. By detecting retinopathy at an early stage, the system helps prevent costly interventions, such as surgeries and treatments for advanced stages of the disease, which can lead to significant cost savings for healthcare systems. (iv) Patient convenience. Patients undergo retinal imaging as part of their regular diabetes check-ups, reducing the need for separate appointments with eye specialists, which encourages enhanced compliance.
 
Part 4

The Potential Benefits of Full AI Integration for MedTechs

Large, diversified MedTechs stand to gain significant benefits by fully embracing AI technologies that extend across all aspects of their operations, innovation, and overall value propositions. In this section we briefly describe 10 such advantages, which include enhanced innovation, improved patient outcomes, increased operational efficiency, cost savings, and access to new revenue streams. Companies that harness the full potential of AI will be better positioned to thrive in the highly competitive and rapidly evolving healthcare industry.
 
1. Enhanced innovation and product development
AI technologies have the potential to enhance R&D endeavours. They accomplish this through the ability to dig deep into vast repositories of complex medical data, identifying patterns, and forecasting outcomes. This translates into a shorter timeline for the conception and creation of novel medical technologies, devices, and therapies. In essence, AI quickens the pace of innovation in healthcare. The capabilities of AI-driven simulations and modeling further amplifies its impact. These virtual tools enable comprehensive testing in a digital environment, obviating the need for protracted physical prototyping and iterative cycles, which can shorten the development phase and conserve resources, making the innovation process more cost-effective, and environmentally sustainable.
 
2. Improved patient outcomes
Beyond improving the research landscape, AI improves the quality of patient care by enhancing diagnostic precision through the analysis of medical images, patient data, and clinical histories. Early detection of diseases becomes more precise and reliable, leading to timelier intervention and improved patient outcomes. Additionally, AI facilitates the personalization of treatment recommendations, tailoring them to individual patient profiles and current medical research. This optimizes therapies and increases the chances of successful outcomes and improved patient wellbeing.
 
3. Efficient clinical trials
Increasingly AI algorithms are being used in clinical studies to identify suitable patient cohorts for participation in trials, effectively addressing recruitment challenges and streamlining participant selection. Further, predictive analytics play a role in enhancing the efficiency of trial design. By providing insights into trial protocols and patient outcomes, AI reduces both the time and costs associated with bringing novel medical technologies to market, which speeds up the availability of treatments and facilitates the accessibility of healthcare innovations to a broader population.
 
4. Operational efficiency
Operational efficiency is improved with the integration of AI technologies by refining operations. AI-driven supply chains and inventory management systems play a significant role in optimizing procurement processes. They analyze demand patterns, reduce wastage, and ensure the timely availability of critical supplies. By doing so, companies can maintain uninterrupted operations, enhancing their overall efficiency and responsiveness. Another component of operational efficiency lies in predictive maintenance, which can be improved by AI. Through continuous monitoring and data analysis, AI can predict equipment failures before they occur. Such a proactive approach minimizes downtime and ensures manufacturing facilities remain compliant and in optimal working condition. Consequently, healthcare providers experience improved operational efficiency, strengthened compliance, and a reduction in costly disruptions. The automation of routine tasks and processes via AI relieves healthcare professionals from repetitive duties and frees up resources that can be redirected towards more strategic and patient-centric initiatives. This reallocation reduces operational costs while enhancing the quality of care provided.
 
5. Cost savings
Beyond automation, AI-driven insights further uncover cost efficiencies within healthcare organizations. AI identifies areas where resource allocation and utilization can be optimized, which can result in cost reduction strategies that are both data-informed and effective. AI's potential extends to the generation of innovative revenue streams. Corporations can develop data-driven solutions and services that transcend traditional medical devices. For instance, offering AI-driven diagnostic services or remote patient monitoring solutions provides access to new revenue streams. Such services improve patient care and contribute to the financial sustainability of enterprises. Further, AI-enabled healthcare services lend themselves to subscription-based models, ensuring consistent and reliable revenue over time. Companies can offer subscription services that provide access to AI-powered diagnostics, personalized treatment recommendations, or remote monitoring, which have the capacity to diversify revenue streams and enhance longer-term financial stability.
 
6. New revenue streams
AI's ability to analyze vast datasets positions MedTechs to unravel the interplay of genetic, environmental, and lifestyle factors that shape individual health profiles. With such knowledge, personalized treatment plans and interventions can be developed, ensuring that medical care is tailored to each patient's unique needs and characteristics. This level of customization optimizes outcomes and minimizes potential side effects and complications. AI's ability to process vast amounts of patient data and detect patterns, anomalies, and correlations, equips healthcare professionals with the knowledge needed to make more informed decisions. Such insights extend beyond individual care, serving as the basis for effective population health management and proactive disease prevention strategies. In short, AI transforms data into actionable intelligence, creating a basis for more proactive and efficient healthcare practices.
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7. Regulatory compliance and safety
In an era of stringent healthcare regulations, AI is a reliable ally to ensure compliance and enhance safety standards. Through automation, AI streamlines documentation, data tracking, and quality control processes, reducing the risk of errors and oversights. Also, AI-powered systems excel in the early detection of anomalies and potential safety issues, which increase patient safety and the overall quality of healthcare solutions and services. This safeguards patient wellbeing and protects the reputation and credibility of companies.
8. Competitive advantage
MedTechs that are early adopters of AI stand to gain a distinct competitive advantage. They can offer AI-powered solutions and services that deliver superior clinical outcomes and improve overall patient experience. By harnessing the potential of AI, companies can position themselves as leaders in innovation and technological capabilities, likely drawing a loyal customer base, valuable partnerships, collaborations, and investments.
 
9. Talent attraction and retention
Embracing AI technologies also has an impact on talent attraction and retention. The allure of working on novel AI projects that improve lives attracts scarce tech-savvy professionals who seek to be part of dynamic, purposeful, and forward-thinking teams. Such talent contributes to a skilled workforce capable of extending the boundaries of AI innovation within MedTech companies. Further, fostering a culture of innovation through AI adoption encourages employee engagement and job satisfaction, leading to improved talent retention.
 
10. Long-term sustainability
The integration of AI goes beyond immediate advantages; it positions MedTechs for longer-term strategic growth and resilience. As the healthcare landscape continues to evolve, adaptability and innovation become more important. AI enables companies to adapt to changing market dynamics, navigate regulatory challenges, and remain relevant amidst industry transformations. By staying at the forefront of technological advancements, companies ensure their relevance and contribute to shaping the future healthcare landscape.
 
Part 5

Potential Obstacles to the Adoption of AI by MedTechs

The integration of AI technologies into numerous industries has demonstrated its potential to significantly enhance operations, improve R&D, and create new revenue streams. However, despite AI’s potential to contribute significant benefits for business enterprises, its adoption by many large, diversified medical technology companies has been limited and slow. This section describes some factors that help to explain the reluctance of senior MedTech executives to fully embrace AI technologies, which include an interplay of organizational, technical, and industry-specific issues. Without overcoming these obstacles, MedTechs risk losing the growth and value creation they once experienced in an earlier era.

Demographics of senior leadership teams
According to Korn Ferry, an international consultancy and search firm, the average age for a C-suite member is 56 and their average tenure is 4.9 years, although the numbers vary depending on the industry. The average age of a CEO across all industries is 59. If we assume that the MedTech industry mirrors this demographic, it seems reasonable to suggest that many corporations have executives approaching retirement who may be more risk averse and oppose the comprehensive introduction of AI technologies due to a fear of losing benefits they stand to receive upon retirement.

Organizational inertia and risk aversion
Large medical technology companies often have well-established structures, processes, and cultures that resist rapid change. In such an environment, executives might be hesitant to introduce AI technologies due to concerns about disrupting existing workflows, employee resistance to learning new skills, and the fear of failure. The risk-averse nature of the medical technology industry, where patient safety is critical, further amplifies executives' cautious approach to implementing unproven AI solutions.
 
Technical challenges and skill gaps
AI implementation requires technical expertise and resources. Many MedTech executives might lack a deep understanding of AI's technical capabilities, making it difficult for them to evaluate potential applications. Further, attracting and retaining AI talent is highly competitive, and the scarcity of professionals skilled in both medical technology and AI can hinder successful implementation.
Regulatory and ethical concerns
The medical field is heavily regulated to ensure patient safety and data privacy. Incorporating AI technologies introduces additional layers of complexity in terms of regulatory compliance and ethical considerations. Executives might hesitate to navigate these legal frameworks, fearing potential liabilities and negative consequences if AI systems are not properly controlled or if they lead to adverse patient outcomes.
Long development cycles and uncertain ROI
The R&D cycle in the medical technology industry is prolonged due to rigorous testing, clinical trials, and regulatory approvals. Although AI technologies have the capabilities to enhance R&D efficiency, they can introduce additional uncertainty and complexity, potentially extending development timelines. Executives could be apprehensive about the time and resources required to integrate AI into their R&D processes, especially if the return on investment (ROI) remains uncertain or delayed.
 
Industry-specific challenges
The medical technology industry has unique challenges compared to other sectors. Patient data privacy concerns, interoperability issues, and the need for rigorous clinical validation can pose barriers to AI adoption. Executives might view these complexities as additional hurdles that could hinder the successful implementation and deployment of AI solutions.
  
Existing Revenue Streams and Incremental Innovation
Many large, diversified MedTechs generate substantial revenue from their existing products and services. Executives might be reluctant to divert resources towards AI-based ventures, fearing that these investments could jeopardize their core revenue streams. Additionally, a culture of incremental innovation prevalent in the industry might discourage radical technological shifts like those associated with AI.
 
Takeaways
 
Hesitation among MedTechs to integrate AI technologies poses the threat of missed opportunities, diminished competitiveness, and sluggish growth. This reluctance hinders innovation and limits the potential for enhanced patient care. Embracing AI is not an option but a strategic imperative. Failure to do so means missing opportunities to address unmet medical needs, explore new markets, and access new revenue streams. The potential for efficiency gains, streamlined operations, and cost reductions across R&D, manufacturing and supply chains is significant. Companies fully embracing AI gain a competitive advantage, delivering innovative solutions and services that improve patient outcomes and cut healthcare costs. Conversely, those resisting AI risk losing market share to more agile rivals. AI’s impact on analysing vast amounts of complex medical data, accelerating discovery, and enhancing diagnostics is well established. MedTechs slow to leverage AI may endure prolonged R&D cycles, fewer breakthroughs, and suboptimal resource allocation, jeopardising competitiveness and branding them as ‘outdated’. In today’s environment, attracting top talent relies on being perceived as innovative, a quality lacking in AI-resistant MedTechs. As AI disrupts industries, start-ups and smaller agile players can overtake established corporations failing to adapt. A delayed embrace of AI impedes progress in patient care, diagnosis, treatment, and outcomes, preventing companies from realising their full potential in shaping healthcare. The time to embrace AI is now to avoid irreversible setbacks in a rapidly evolving MedTech ecosystem.
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7 months, 3 weeks ago

Forging a path for digital excellence in the MedTech Industry

HealthPad
Directory:
Medical Technology
Tags:
AI
Artificial Intelligence
big data
commentary
cybersecurity
digitalization
Machine Learning
MedTech

 

  • The MedTech industry faces a pivotal moment as it confronts the challenge of adopting transformative technologies amidst a rapidly changing healthcare ecosystem
  • Despite progress in other sectors, MedTech has shown reluctance to fully integrate digitalization, potentially hindering its growth and competitiveness
  • There have been some notable exceptions such as Medtronic, Siemens Healthineers and Philips
  • Many large diversified MedTechs could unlock growth and value by capitalizing on the potential synergies between traditional medical devices and innovative digital solutions and services
  • The convergence of digital offerings with legacy medical devices provides opportunities for improved patient care, operational efficiency and R&D innovation
  • There is a pressing need for MedTechs to comprehensively embrace digitalization to avoid reduced competitiveness, limited growth, and diminished value enhancement
 
Forging a path for digital excellence in the MedTech Industry

In an era of rapid technological advancement, the medical technology (MedTech) industry is at a crossroads. While numerous other sectors have enthusiastically embraced digitalization and moved forward, the MedTech sector, barring a few notable exceptions, has been hesitant to embrace these transformative technologies. However, the time has come for large diversified MedTechs to recognize the opportunities that digitalization offers for growth and value creation. The convergence of traditional medical devices with digital solutions and services presents an opportunity for the industry to improve patient care, streamline operations, and drive innovation. Failing to fully integrate digitalization into their operations in a timely way may lead to unexpected consequences, including a shorter window of competitiveness and a struggle to enhance growth rates and augment value. The reluctance of many MedTechs to adapt now could translate into a significant handicap in the rapidly evolving landscape of healthcare technology.
 
In this Commentary

In this Commentary, we tackle four questions: (i) What is digitalization? (ii) Why is digitalization important for MedTechs? (iii) Which MedTechs have implemented successful digitalization strategies? and (iv) What defines an effective digitalization strategy? In addressing the fourth question, we present a strategy that encompasses 20 'essentials', which are not meant to follow a linear, sequential path. Instead, they are orchestrated by agile cross-functional teams, collaborating and pooling resources. Together, these teams oversee the execution of various elements of the strategy, while assuming responsibility for its overall effectiveness. This approach signals a departure from hierarchical departments and advocates a matrix-style organizational structure characterized by a web of interconnected reporting relationships. This structure goes beyond the confines of the conventional linear framework and incorporates specialized clusters, akin to "nests," each housing unique competencies, spanning multiple dimensions, and encompassing responsibility, authority, collaboration, and accountability.
 
1. What is digitalization?
 
Digitalization, also referred to as digital transformation, involves harnessing digital technologies to improve and refine business operations, processes, and services. By integrating digital tools across all facets of an organization, digitalization streamlines workflows, amplifies customer experiences, and achieves strategic goals. This includes automating tasks, utilizing data analytics for informed decision-making, and leveraging cloud computing for scalable and flexible operations. The Internet of Things (IoT) facilitates data exchange through connected devices, while artificial intelligence (AI), machine learning (ML) and large language models (LLM) empower computers to perform tasks requiring human-like intelligence. Virtual and augmented reality (VR/AR) enrich experiences, while cybersecurity measures are important to safeguard digital assets.
 
2. Why is digitalization important for MedTechs?
 
Digitalization is important for the MedTech industry since it acts as a driver for significant and positive change. By fully embracing this transformation, the industry develops the ability to use data and analytics to create innovative medical solutions and services. These are built on insights and predictions obtained from large amounts of information. Apart from these benefits, digitalization also affects the core of how clinical operations work. It makes workflows more efficient and frees-up healthcare professionals to focus more on taking care of patients. One significant development is the rise of collaborative telehealth platforms, which play a role in improving the quality and efficiency of healthcare delivery. Additionally, the power of technologies like AI, and ML becomes more evident. These advanced tools, driven by their ability to rapidly analyse vast data sets and make predictions, contribute to breakthroughs in care with the potential to improve patient outcomes while reducing costs.
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Is the digital transformation of MedTech companies a choice or a necessity?

 
The collaboration between smart devices and blockchain technology becomes important in a digital transformation, enhancing patient safety, and ensuring regulatory compliance. As the MedTech sector embraces digitalization, it enables companies to succeed in value-based healthcare environments, which results in quality care becoming more accessible and affordable. This is partly made possible through remote monitoring and proactive interventions that overcome distance. A distinctive aspect of digitalization is the ability to provide personalized care. Focusing on creating solutions and services tailored to individual needs helps to create an innovative environment within MedTechs, which can be leveraged to drive continuous growth and value creation. As digitalization becomes more influential, the MedTech industry should move closer to personalized health, which means care is centered around patients, innovation is continuous, and growth is more certain.
3. Which MedTechs have implemented successful digitalization strategies?
 
There are several large MedTechs that have successfully leveraged digitalization strategies to gain access to new revenue streams. Here we briefly describe just three. Philips is known for its diverse healthcare products and services, including imaging systems, patient monitoring, and home healthcare solutions and services. They have successfully utilized digitalization by creating a connected ecosystem of devices that capture and transmit patient data, enabling real-time monitoring and personalized care. Their strategy also includes software solutions for data analysis, predictive analytics, and telehealth, contributing to the creation of new revenue streams beyond traditional medical devices. Siemens Healthineers focuses on medical imaging, laboratory diagnostics, and advanced healthcare IT. Their digitalization strategy involves offering integrated solutions that connect medical devices, data analytics, and telemedicine platforms. For instance, their cloud-based platforms enable healthcare providers to store, share, and analyze medical images and patient data, resulting in streamlined workflows and new revenue opportunities through data-driven insights. Medtronic, a global leader in medical technology, offering a wide range of products and services in various medical specialties, has successfully embraced digitalization by incorporating smart technologies into their devices, such as pacemakers and insulin pumps, allowing remote monitoring and data collection. This has improved patient care and given the company access to new revenue streams through subscription-based services for data analytics and remote monitoring.
 
4. What defines an effective digitalization strategy?
 
In today’s business climate, developing an effective digital strategy has shifted from being a ‘nice to have’ to a necessity. As MedTechs navigate the dynamic technology landscape, digitalization has become a priority. In this section, we present a 20 'essentials' for crafting and implementing a digitalization strategy. These are not linear, but collectively constitute a path towards a digital transformation for a large diversified MedTech company.   

1. Crafting a Cohesive Vision
Digitalization starts with an evaluation of a company's existing products, services, processes, and technologies. This forms the basis upon which a vision and strategic goals are constructed. The main objective here is to align a company's aspirations with the dynamic MedTech landscape, creating a basis for innovation. Digitalization entails more than the integration of peripheral technologies. It is a paradigm shift. The initiation of a digitalization vision depends upon sound long-term strategic objectives. This involves not only envisioning the transformative potential of digitalization within an organization but also projecting its impact, whether that be improved patient experiences, data-driven operational enhancements, or the exploration of new revenue streams. As this vision takes shape, often in the form of a story that everyone in an organization can buy-into, it should steer decisions and guide investments throughout the entire digital transformation process. Further, it provides tangible benchmarks against which progress can be gauged and strategies can be refined. It is important that digitalization goals are aligned to the evolving needs of healthcare. MedTechs should harness the power of digitalization to meet the expectations of patients and adapt to dynamic clinical practices. This requires reconciling digital innovations with a company’s core values. A comprehensive and forward-looking vision (story) functions to safeguard a company's strengths against potential challenges. This first step toward a digitalization strategy serves to position a company for sustainable growth and enduring value creation.
2. Leadership commitment
The significance of securing buy-in from senior leadership teams lies in its assurance of resources, funding, and support, which are vital for the success of such an initiative. The endorsement from executives, beyond being a signal of change, serves as a catalyst for the allocation of both financial and human resources and has a substantial impact on the direction and depth of a digitalization strategy. By wholeheartedly supporting such an initiative, leaders disseminate not only a positive message about the importance attached to digitalization, but they also foster employee engagement, subsequently paving the way for the potential integration of digitalization across an entire company.

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3. Cross-functional synergy
Creating cross-functional teams is central for driving change, and should span departments like IT, R&D, operations, marketing, and regulatory affairs. The nature of a MedTech's digitalization strategy requires diverse expertise to successfully release technology's full potential. IT professionals contribute technical knowhow, which ensures the implementation and integration into existing infrastructure. R&D members provide visionary insights, encouraging innovative solutions and services. Operations specialists optimize processes for digital efficiency. Marketers strategize effective communications of digital progress. Regulatory experts ensure compliance and ethical considerations. Each contribution plays a distinct yet interconnected role, fostering collaborative brainstorming, shared goals, and pooled talents within a developing culture of agility and innovative. This approach breaks down silos, and aims to create a unified, technology-optimized future. Cross-functional teams act as the driving force to transform digital potential into a tangible reality.

4. Informed market insight
Market and consumer research is an important element of the strategy, as it uncovers customer needs, preferences, and pain points in digital healthcare. Such insights form the basis for tailored technologies that cater to specific needs, increasing patient engagement and satisfaction. Additionally, a successful digitalization strategy needs to identify and adapt to evolving trends in the digital MedTech sector. This entails monitoring emerging technologies, shifts in consumer behaviour, and advances in medical practices. Equally important is analyzing the competitive landscape to benchmark offerings and drive innovation. When companies are aligned to market dynamics, they are more likely to become digital leaders, fostering continuous improvement and innovation.

5. Technology assessment
Assessing a company's existing technology infrastructure helps to gauge whether a strategy can effectively leverage current investments and assets. Simultaneously, the assessment should uncover gaps and shortcomings. Identifying these informs targeted resource allocation for new technologies that support digital goals. Thus, a technology assessment allows organizations to strike a balance between leveraging existing capabilities and making targeted investments, in pursuit of their digital transformations.
6. Effective digital solutions
An essential aspect of a digitalization strategy involves identifying effective solutions and services. This process entails exploring various facets of an organization to integrate innovations; from improving customer engagement to optimizing workflows. Equally crucial is deploying technologies that improve patient outcomes, diagnoses, treatments, and monitoring. This stage also identifies potential revenue streams derived from new digital solutions and services, like remote patient monitoring, telemedicine, data analytics, and AI diagnostics, which strengthen existing offerings.
7. Partnerships
Engaging in collaborations with technology companies, start-ups, and various stakeholders creates opportunities for synergistic growth. Such partnerships enable enterprises to tap into diverse expertise, gain fresh perspectives, and access specialized resources, all of which support the development and implementation of digital solutions and services. Collaboration facilitates knowledge and resource pooling, enhancing innovation cycles and ensuring a comprehensive transformation of healthcare services. Simultaneously, acquisitions can enhance in-house capabilities. Exploring the acquisition of companies possessing relevant digital competencies or disruptive technologies offers a potential competitive edge. Such moves can help with assimilating novel technologies and developing a culture of innovation. Acquisitions can assist companies to position themselves as key players, advancing their digital health agenda and solidifying their position in an evolving industry.

8. Data management and security
Enhancing data management entails developing and implementing robust protocols. This involves refining data collection procedures, enforcing privacy and security measures, and adhering to healthcare regulations like the US Health Insurance Portability and Accountability Act (HIPAA) and the EU General Data Protection Regulation (GDPR), which safeguard patient data from breaches or misuse. Such measures establish a foundation for data management and security and help to foster stakeholder trust. Compliance with regulations like HIPAA and GDPR should not simply be viewed a legal obligation, but also as a moral commitment when handling sensitive patient data. Such a proactive stance strengthens a company's reputation for data integrity and helps to avoid legal repercussions.

9. Technology roadmap
A technology roadmap is a blueprint charting a course toward enhanced efficiency, patient-centric care, and heightened competitiveness. Beyond action planning, it provides clarity and purpose in navigating technological advancements. It consolidates an enterprise's digitalization efforts by integrating initiatives with timelines and resources, thereby establishing a framework for goal setting and assessment. Such planning assists timely project execution and supports the rationale for digitalization with measurable benefits. With a well-structured roadmap, stakeholders can appreciate how digital initiatives improve operations, trigger innovation, and enhance patient outcomes.

10. Pilot programmes
Pilot programmes serve as incubators and evidence-based validators for innovations, offering a means to test and enhance digital solutions before they are fully implemented. Such initiatives provide tangible evidence to support an enterprise's commitment to a digitalization strategy. Pilots offer concrete proof of an enterprise’s commitment to its digitalization strategy. Each programme should concentrate on specific solutions and establish a controlled setting for gathering user feedback, which constitutes an on-going effort to refine functionality. Additionally, pilots demonstrate a commitment to user-centric offerings by proactively tackling challenges, thereby improving the chances of successful, large-scale digital deployments.

11. Scalability and integration
Establishing scalability and integration capabilities is important for MedTechs to realize their digital transformation. As healthcare technology landscapes evolve and organizational needs change, the ability of digital solutions to scale and integrate with existing structures increases in importance. Ensuring these attributes contributes to a digital transformation. Scalability emphasizes a company’s adaptability to evolving demands. A scalable digital solution that expands in scope without sacrificing functionality invokes confidence. Further, integrating novel solutions and services with existing systems signals operational intelligence, which adds credibility to the digital transition. When digital solutions merge with legacy structures, they reflect an alignment of traditional expertise and cutting-edge technology. Emphasising scalability and integration involves anticipating future requirements and aligning digital strategies with longer-term organizational objectives.

12. Change management
By supporting a mindset that views digital technologies as enablers rather than disruptors, companies demonstrate their commitment to progress and cultural change. Implementing change management acknowledges the importance of cultural shifts and affirms an intent to embrace digital technologies holistically and sustainably. It acts as the vehicle, which guides an enterprise through transformation, and ensures stakeholder support for technological evolution. Through communication, training, and engagement policies, enterprises lay the groundwork for digital adoption, and smooth technology integration. This strengthens the case for change and demonstrates an organization's commitment to fostering an innovation-receptive environment.

13. Training and skill development
Central to a successful digitalization strategy is an investment in training and skill development. This underlines an organization's commitment to harnessing and effectively utilizing the transformative potential of technology. By training, corporations equip their employees with capabilities required to support digital solutions and services. Training bridges the gap between skill shortages and technological advancements. Empowering employees with the capacity to navigate digital technologies positions an enterprise for a successful transition, by a process that reconciles change with employee growth. Training reinforces the notion that digitalization is not just an operational enhancement but also a means to cultivate a workforce with capabilities, which contribute to operational excellence and sustainable expansion.

14. Regulatory adherence
Regulatory compliance is an important feature of a digital shift, as it demonstrates a company's commitment to upholding the highest standards of patient care and industry excellence. It shows that transformation is about embracing the future with integrity by ensuring that an enterprise’s  innovations are synchronized with the values underpinning medical practice. Adherence to regulatory standards is a declaration of an organization's commitment to patient safety and industry integrity. By ensuring all digital solutions and services adhere to rigorous medical regulations, corporations strengthen their case for digitalization within ethical and legal boundaries. Demonstrating adherence to medical regulations and industry benchmarks reinforces a new digital strategy as a responsible and trustworthy pursuit and showcases an organization's commitment to delivering technologies that both innovate and enhance patients' therapeutic journeys while respecting established medical protocols.

15. Market communication
Crafting a communication strategy is important as it underlines an organization’s commitment to transformation. Employing a variety of smart communication methods to describe the benefits of new digital offerings enables MedTechs to garner support from stakeholders and thereby strengthen their market position. By aiming at healthcare professionals, investors, payers, patients, providers and other stakeholders, these messages inform and persuade by highlighting the tangible benefits they bring to patient care, operational efficiency, and industry progress.

16. Feedback loop and iteration
Stakeholder feedback can be used to enhance digital solutions and services. By engaging users and patients, healthcare technologies can be tailored to cater to specific needs and preferences, fostering a user-centric design ethos. This collaborative approach identifies bottlenecks, deficiencies, and possible enhancements, which contribute to efficacious digital solutions and services. Moreover, stakeholder involvement helps to ensure a company's technological endeavours support broader healthcare goals, enhancing the overall quality of care. Iteration should be synonymous with evolution. Regularly integrating feedback to enhance the functionality of digital offerings enables an enterprise to adapt to market challenges and healthcare advancements.
17. Performance measurement
Effective evaluation of a company's digitalization strategy demands the use of key performance indicators (KPIs). These serve as a compass to assess the impact of digital solutions across patient outcomes, operational efficiency, and business expansion. By selecting relevant KPIs, MedTechs can show stakeholders the tangible effects of their digitalization strategy. These quantifiable metrics offer a lens to observe enhanced patient care, rectify operational inefficiencies, and decipher trends in business growth.
18. Fostering a culture of continuous innovation
An effective digitalization strategy relies on fostering a culture of perpetual innovation, which is essential to maintain a market-leading position. Such an approach encourages the creation, implementation and refinement of smart technological solutions and services. It equips MedTechs with the agility to quickly embrace emerging trends, capitalize on novel prospects, and tackle unforeseen challenges. Further, a culture of continuous innovation encourages an executive mindset that perceives setbacks as opportunities and views technology as evolving tools to improve patient care and operational efficacy.
 
19. Adaptation to market changes
MedTechs must rapidly adjust their digital strategies to match prevailing technological trends, regulations, and market dynamics. These ever-changing elements emphasize the need for a proactive, flexible digitalization approach that can swiftly adapt. By staying ahead of shifting trends, businesses are better positioned to leverage emerging technologies and provide solutions for evolving market needs. Navigating regulatory changes is equally important. Balancing compliance with innovative solutions ensures the integration of digital offerings in a dynamic healthcare setting. Flexibility should extend to market fluctuations, aligning digitalization strategies with customer demands and competition. This not only helps a company to navigate volatile markets but also positions it as an agile player, primed for change and enduring growth.

20. Embracing longer-term sustainability
For MedTechs, it is important that their digital strategies align with their principal longer-term objectives. Instead of solely pursuing immediate gains, this strategy should support a company's core purpose and future aspirations, which are embedded within its day-to-day operations. Such an approach establishes an innovative, adaptable, and resilient framework and strengthens the potential for growth. When a digitalization strategy is aligned with a company’s longer-term goals, it assumes the role of a catalyst for growth by optimizing the utilization of resources, improving brand resilience, and securing a distinct competitive advantage. During constantly evolving technologies and markets, such an alignment provides the capacity for a company to effectively confront challenges and capitalize on emerging opportunities, thereby either moving into, or securing, a leadership position within the rapidly changing market landscape.
 
Takeaways 
 
In the face of rapid technological evolution, the MedTech industry finds itself at a crucial juncture. While other sectors have embraced digitalization, many large diversified MedTechs have been hesitant in adopting these transformative tools. Yet, the imperative is clear: for sizable companies, the present demands recognition of digitalization's potential to drive growth and cultivate value. The fusion of conventional medical devices with digital innovations not only augments patient care but also streamlines operations and encourages innovation. The consequences of delaying this integration are significant. Without prompt action, corporations risk narrowing their competitive horizons and struggling to accelerate growth and enhance value. Failure to adapt may result in a substantial disadvantage in the rapidly changing arena of healthcare technology. It is important for MedTechs that have not already done so, to pivot towards digitalization and transform their challenges into opportunities, ensuring a dynamic and thriving future in an increasingly interconnected world.
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10 months, 1 week ago

Unleashing MedTech's Competitive Edge through Transformational Technologies

HealthPad
Directory:
Medical Technology
Tags:
AI
Artificial Intelligence
big data
commentary
digitalization
MedTech
transformational technologies
  • Digitalization, big data, and artificial intelligence (AI) are transformational technologies poised to shape the future of MedTech companies over the next decade
  • Fully embracing these technologies and integrating them in all aspects of a business will likely lead to growth, and competitive advantage while treating them as peripheral add-ons will likely result in stagnation and decline
  • MedTech executives’ analogue mindsets and resource constraints prevent them from fully embracing transformational technologies
  • There are also potential pushbacks from employees, patients, providers and investors
  • Notwithstanding, there are unstoppable structural trends forcing governments and payers throughout the world to oblige healthcare systems to leverage digitalization, big data, and AI to help reduce their vast and escalating healthcare burdens
  • Western MedTechs are responding to the rapidly evolving healthcare landscape by adopting transformational technologies and attempting to increase their presence in emerging markets, particularly China
  • To date, MedTech adoption and integration of digitalization, big data, and AI have been patchy
  • To remain relevant and enhance their value, Western MedTechs need to learn from China and embed transformational technologies in every aspect of their businesses
 
Unleashing MedTech's Competitive Edge through Transformational Technologies
Digitalization, Big Data, and AI as Catalysts for MedTech Competitiveness and Success
 
 
In the rapidly evolving landscape of medical technology, the integration of digitalization, big data, and artificial intelligence (AI) [referred to in this Commentary as transformational technologies] has emerged as a pivotal force shaping the future of MedTech companies.  Such technologies are not mere add-ons or peripheral tools but will soon become the lifeblood that fuels competition and enhances the value of MedTechs. From research and development (R&D) to marketing, finance to internationalization, and regulation to patient outcomes, digitalization, big data, and AI must permeate every aspect of medical technology businesses if they are to deliver significant benefits for patients and investors. To thrive in this rapidly evolving high-tech ecosystem, companies will be obliged to adapt to this paradigm shift.
 
Gone are the days when traditional approaches would suffice in the face of escalating complexities and demands within the healthcare industry. The convergence of transformational technologies heralds a new era, where innovation and success are linked to the ability to harness the potential of digitalization, big data, and AI. MedTech companies that wish to maintain and enhance their competitiveness must recognize the imperative of integrating these technologies across all facets of their operations. From improving their R&D processes by utilizing advanced data analytics and predictive modeling, to optimizing internal processes through automation and machine learning algorithms. Embracing such technologies opens doors to enhanced marketing strategies, streamlined financial operations, efficacious legal and regulatory endeavours, seamless internationalization efforts, and the development of innovative offerings that cater to the evolving needs of patients, payers, and healthcare providers.
 
This Commentary aims to stimulate discussion among MedTech senior leadership teams as the industry's competitive landscape continues to rapidly evolve, and the fusion of digitalization, big data, and AI becomes not only a strategic advantage but a prerequisite for survival in an era defined by data-driven decision-making, personalized affordable healthcare, and a commitment to improving patient outcomes.
 
In this Commentary

This Commentary explores digitalization, big data, and AI in the MedTech industry. It presents two scenarios: one is to fully embrace these technologies and integrate them into all aspects of your business and the other is to perceive them as peripheral add-ons. The former will lead to growth and competitive advantage, while the latter will result in stagnation and decline. We explain why many MedTechs do not fully embrace transformational technologies and suggest this is partly due to executives’ mindsets, resource constraints and resistance from employees, patients, and investors. Despite these pushbacks, the global healthcare ecosystem is undergoing an unstoppable transformation, driven by aging populations and significant increases in the prevalence of costly to treat lifetime chronic conditions. Western MedTechs are responding to structural shifts by adopting transformational technologies and increasing their footprints in emerging markets, particularly China. To date, company acceptance of AI-driven strategies has been patchy. We suggest that MedTechs can learn from China and emphasize the need for organizational and cultural change to facilitate the comprehensive integration of transformational technologies. Integrating these technologies into all aspects of a business is no longer a choice but a necessity for companies to stay competitive in the future.
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Transformational technologies in MedTech

Digitalization in the MedTech industry involves adopting and integrating digital technologies to improve healthcare delivery, patient care, and operational efficiency. It transforms manual and paper-based processes into digital formats, enabling electronic health records, connected medical devices, telemedicine, and other digital tools. This allows for seamless data exchange and storage, improving clinical processes, decision-making, and patient empowerment. Big data in the MedTech industry refers to the vast amount of healthcare-related information collected from various sources. It includes structured and unstructured data such as patient demographics, clinical notes, diagnostic images, and treatment outcomes. Big data analysis identifies patterns, correlations, and trends that traditional methods may miss. They aid medical research, drug discovery, personalized medicine, clinical decision support, evidence-based care, population health management, and public health initiatives. Data privacy, security, and ethical use are crucial considerations. Artificial Intelligence (AI) in the MedTech industry uses computer algorithms to simulate human intelligence. AI analyzes medical data to identify patterns, make predictions, and improve diagnoses, treatment plans, and patient outcomes. It assists in medical imaging interpretation, personalized medicine, and patient engagement. In R&D, AI accelerates the development of devices and the discovery of new therapies and has the capacity to analyze scientific literature and molecular data. The technology serves as a tool to augment healthcare professionals' expertise and support decision-making.
With the proliferation of large language AI models (LLM) and to borrow from a recent essay by Marc Andreeseen - an American software engineer, co-author of Mosaic, [one of the first widely used web browsers] and founder of multiple $bn companies - everyone involved with medical technology, including R&D, finance, marketing, manufacturing, regulation, law, international etc., “will have an AI assistant/collaborator/partner that will greatly expand their scope and achievement. Anything that people do with their natural intelligence today can be done much better with AI, and we will be able to take on new challenges that have been impossible to tackle without AI, including curing all diseases.”

Two scenarios

We suggest there are only two scenarios for MedTechs: a company that fully embraces transformational technologies and one that does not. The former, will benefit from strengthened operational efficiencies, improved patient outcomes, and enhanced innovations, which will lead to increased market share and investor confidence. By leveraging digital technologies, such as remote monitoring devices, telemedicine platforms, LLMs, and machine learning, a company will be able to offer more personalized, effective and affordable healthcare services and solutions. An enterprise that integrates these technologies into their strategies and business models will, over time, experience improved growth prospects, increased revenues, and potentially higher profitability. These factors will contribute to a positive perception in the market, leading to an increase in company value. MedTechs that fail to fully embrace digitalization, big data, and AI will face challenges in adapting to the rapidly evolving healthcare landscape. They will struggle to remain competitive and relevant in a market that increasingly values transformational technologies and data-driven approaches. As a result, such companies will experience slower growth, lower market share, and limited investor interest, which will lead to a stagnation or decline in their value.
 
The analogue era's influence on MedTechs

If the choice is so stark, why are many MedTechs not grabbing the opportunities that transformational technologies offer? To answer this question let us briefly remind ourselves that the industry took shape in an analogue era, which had a significant effect on how MedTech companies evolved and established themselves. During the high growth decades of the 1980s, 1990s, and early 2000s, the medical technology industry operated with limited access to the technologies that have since radically changed healthcare. The 1980s marked a period of advancements, which included the widespread adoption of medical imaging such as computed tomography (CT) scans and magnetic resonance imaging (MRI). These modalities provided detailed visualizations of the human body, supporting more accurate diagnoses. Medical devices like pacemakers, defibrillators, and implantable cardioverter-defibrillators (ICDs) were developed and improved the treatment of heart conditions. The 1990s witnessed further advancements, with a focus on minimally invasive procedures. Laparoscopic surgeries gained popularity, allowing surgeons to perform operations through small incisions, resulting in reduced patient trauma and faster recovery times. The development of laser technologies enabled more precise surgical interventions. The decade also saw the rise of biotechnology, with the successful completion of the Human Genome Project and increased emphasis on genetic research. The early 2000s saw the emergence of digital transformation in some quarters of the medical technology industry. Electronic medical records (EMRs) began to replace paper-based systems, increase data accessibility and upgrade patient management. Telemedicine, although still in its nascent stages, started connecting healthcare providers and patients remotely, overcoming geographical barriers. Robotics and robotic-assisted surgeries gained traction, enabling more precise and less invasive procedures. During these formative decades, the medical technology industry focused on enhancing diagnostic capabilities, improving treatment methods, and streamlining healthcare processes. The industry had yet to witness the transformational impact of digitalization, big data and AI that would emerge in subsequent years, enabling more advanced analytics, personalized medicine, and interconnected healthcare systems.
 
From analogue to digital

During these formative analogue years, MedTechs experienced significant growth and expansion, where innovative medical technologies changed healthcare practices and improved patient outcomes. Companies thrived by leveraging their expertise in engineering, biology, and clinical research and developed medical devices, diagnostic tools, and life-saving treatments. For MedTechs to experience similar growth and expansion in a digital era, they must fully harness the potential of transformational technologies, and to achieve this, there must be a receptive mindset at the top of the organization.
 
According to a recent study by Korn Ferry, a global consulting and search firm, the average age of CEOs in the technology sector is 57, and the average age for a C-suite member is 56. Thus, as our brief history suggests, many MedTech executives advanced their careers in a predominantly analogue age, prior to the proliferation of technologies that are transforming the industry today. Thus, it seems reasonable to suggest that this disparity in experience and exposure colours the mindsets of many MedTech executives, which can lead to them underestimating and under preparing for the significant technological changes that are set to reshape the healthcare industry over the next decade. Senior leadership teams play a pivotal role in developing the strategic direction of companies and driving their success. Without a proactive mindset shift, these executives may struggle to fully comprehend the extent of the potential disruptions and opportunities that digitalization, big data, and AI bring.
 
By embracing such a mindset shift, senior leadership teams could foster a culture of innovation and agility. But they must recognize the urgency of preparing for a future fueled by significantly different technologies from those they might be more comfortable with. Such urgency is demonstrated by a March 2023 Statista report, which found that in 2021, the global AI in healthcare market was worth ~US$11bn, but forecasted to reach ~US$188bn by 2030, increasing at a compound annual growth rate  (CAGR) of ~37%. As these and other facts (see below) suggest, the integration of digitalization, big data, and AI has already begun to redefine healthcare delivery, patient engagement, and operational efficiency and is positioned to accelerate in the next decade. To remain competitive and relevant in this rapidly evolving high-tech world, MedTechs must foster a culture of openness to change and innovation. Leaders should encourage collaboration, both internally and externally, and create cross-functional teams that bring together expertise from various domains, including AI and data analytics. This multidisciplinary approach facilitates the integration of transformational technologies into all aspects of the business, ensuring that the organization remains at the forefront of the evolving industry.
 
Implementation and utilization

Limited resources, such as budgets and IT infrastructure, can hinder the adoption and utilization of digitalization, big data, and AI, especially for smaller companies. Compliance with healthcare regulations like HIPAA and GDPR adds complexity and can slow down technology implementation. Resistance to change from employees, healthcare providers, and patients also poses challenges. Fragmented and unstandardized healthcare data limit the effectiveness of AI-driven strategies. The expertise gap can be bridged through collaboration with academic institutions and technology companies. Demonstrating the tangible benefits of digitalization, big data and AI is essential to address concerns about return on investments (ROI). Strategic planning, resource investment, collaboration, and cultural change are necessary for the successful implementation and utilization of transformational technologies in MedTech companies. 
 
Organizational and cultural changes

MedTechs must embrace agility and innovation to harness the potential benefits from transformational technologies. This requires fostering a culture that encourages risk-taking and challenges conventional practices. Creating cross-functional teams and promoting collaboration nurtures creativity and innovative solutions. Transitioning to data-driven decision-making involves establishing governance frameworks, ensuring data quality, and leveraging analytics and insights from big data. Talent development and upskilling are crucial, necessitating training programmes to improve digital literacy and add analytics skills. Collaboration and partnerships with external stakeholders facilitate access to cutting-edge technologies. Enhancing patient experiences through user-friendly interfaces and personalized solutions is essential. Investing in agile technology infrastructure, including cloud computing and robust cybersecurity measures is necessary. MedTechs must navigate complex regulatory environments while upholding ethical considerations, transparency, and patient consent to gain credibility and support successful technology adoption.
 
Investors

A further potential inhibitor to change is MedTech investors who may harbour conservative expectations that tend to discourage companies from taking risks, such as fully embracing and integrating digitalization, big data, and AI across their entire businesses. This mindset also can be traced back to the formative analogue decades on the 1980s, 1990s, and early 2000s when investors became accustomed to growing company valuations. During that time, most MedTechs catered to an underserved, rapidly expanding market largely focussed on acute and essential clinical services in affluent regions like the US and Europe, where well-resourced healthcare systems and medical insurance compensated activity rather than patient outcomes. However, the landscape has since undergone a radical change. Aging populations with rising rates of chronic diseases have significantly increased the demands on over-stretched healthcare systems, which have turned to digitalization, big data, and AI in attempts to reduce their mounting burdens. These shifting dynamics now demand a more forward-thinking approach, but investor expectations often remain fixed on a past traditional model, which impedes the adoption and full integration of transformational technologies into MedTech enterprises.

To overcome investor conservatism and reluctance to embrace transformational technologies requires a concerted effort by MedTechs to demonstrate the tangible benefits of these technologies on the industry. Companies can focus on providing evidence of improved patient outcomes, increased efficiency, cost savings, and competitive advantages gained through the integration of digitalization, big data, and AI. Engaging in open and transparent communications with investors, showcasing successful case studies, and highlighting the long-term potential and sustainability of a technology-driven approach can help shift investor expectations and encourage a more receptive attitude towards risk-taking and innovation.
Global structural drivers of change

For decades, Western MedTechs have derived comfort from the fact that North America and Europe hold 68% of the global MedTech market share. These wealthy regions have well-resourced healthcare systems, which, as we have suggested, for decades rewarded clinical activity rather than patient outcomes, and MedTech’s benefitted by high profit margins on their devices, which contributed to rapid growth, and enhanced enterprise values. Today, the healthcare landscape is significantly different. North America and Europe are experiencing aging populations, and large and rapidly rising incidence rates of chronic diseases in older adults. Such trends are expected to continue for the next three decades and have forced governments and private payers to abandon compensating clinical activity and adopt systems that reward patient outcomes while reducing costs. This shift has put pressure on healthcare systems to adopt transformational technologies to help them cut costs, increase access, and improve patient journeys. MedTech companies operating in this ecosystem have no alternative but to adapt. Their ticket for increasing their growth and competitiveness is to adopt and integrate digitalization, big data, and AI into every aspect of their business, which will help them to become more efficient and remain relevant.
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Most developed economies are experiencing aging populations, which affect everything from economic and financial performance to the shape of cities and the nature of healthcare systems. Let us illustrate this with reference to the US. According to the US National Council on Aging, ~56m Americans are ≥65 and this cohort is projected to reach ~95m by 2060. On average, a person ≥65 is expected to live another 17 years. Older adult Americans are disproportionately affected by costly to treat lifetime chronic conditions such as cancer, heart disease, diabetes, respiratory disorders, and arthritis. ~95% of this older adult cohort have at least one chronic disease, and ~80% have two or more. Multiple chronic disorders account for ~66% of all US healthcare costs and ~93% of Medicare spending.

According to a May 2023, Statista report, the US spends more on healthcare than any other country. In 2021, annual health expenditures stood at US$4.2trn, ~18% of the nation’s Gross Domestic Product (GDP). The demographic trends we described in the US are mirrored in all the principal global MedTech markets. Many of which, particularly Japan, are also experiencing shrinking working age populations resulting from a decline in fertility rates, and curbs on immigration. This shrinkage further impacts a nation’s labour force, labour markets, and tax receipts; all critical for resourcing and paying for healthcare services.
 
MedTechs’ response to structural changes

Western MedTechs’ response to these structural challenges have been twofold: (i) the adoption of transformational technologies, which contribute to lowering healthcare costs, improving innovation, and developing affordable patient-centric services and solutions and (ii) targeting emerging markets as potential areas for growth and development. As we have discussed the first point, let us consider briefly the second. Decades ago, giant MedTechs like Johnson and Johnson (J&J), Abbott Laboratories and Medtronic established manufacturing and R&D centres in emerging economies like Brazil, China, and India, where markets were growing three-to-four times faster than in developed countries. Notwithstanding, many MedTechs, were content to continue serving wealthy developed regions - the US and Europe - and either did not enter, or were slow to enter, emerging markets. More recently, as a response to the trends we have described, many MedTechs are either just beginning or accelerating their international expansions. However, such initiatives might be too late to reap the potential commercial benefits they anticipate. Establishing or expanding a footprint in emerging economies is significantly more challenging today than it was two decades ago. 

For instance, two decades ago, China lacked medical technology knowhow and experience and welcomed foreign companies’ participation in its economy. Today, the country has evolved, enhanced its technological capacity and capabilities, and is well positioned to become the world’s leading technology nation by 2030. No longer so dependent on foreign technology companies, the Chinese Communist Party (CCP) raised barriers to their entry. In 2017, government leaders announced the nation's intention to become a global leader in AI by putting political muscle behind growing investment by Chinese domestic technology companies, whose products, services and solutions were used to improve the country's healthcare systems. Over decades, the CCP committed significant resources to developing domestic STEM skills, and research to achieve “major technological breakthroughs” by 2025, and to make the nation a world leader in technology by 2030, overtaking its closest rival, the US. According to a 2023 AI Report from the Stanford Institute for Human-Centered Artificial Intelligence, in 2021, China produced ~33% of both AI journal research papers and AI citations worldwide. In economic investment, the country accounted for ~20% of global private investment funding in 2021, attracting US$17bn for AI start-ups. The nation’s AI in the healthcare market is fueled by the large and rising demand for healthcare services and solutions from its ~1.4bn population, a large and rapidly growing middle class, and a robust start-up and innovation ecosystem, which is projected to grow from ~US$0.5bn in 2022 to ~US$12bn by 2030, registering a CAGR of >46%. 

>4 years ago, a HealthPad Commentary described how a Chinese internet healthcare start-up, WeDoctor, founded in 2010, bundles AI and big data driven medical services into smart devices to help unclog China’s fragmented and complex healthcare ecosystem and increase citizens’ access to affordable quality healthcare. The company has grown into a multi-functional platform offering medical services, online pharmacies, cloud-based enterprise software for hospitals and other services. Today, WeDoctor owns 27 internet hospitals, [a healthcare platform combining online and offline access for medical institutions to provide a variety of telehealth services directly to patients], has linked its appointment-making system to another 7,800 hospitals across China (including 95% of the top-tier public hospitals) and hosts >270,000 doctors and ~222m registered patients. It is also one of the few online healthcare providers qualified to accept payments from China's vast public health insurance system, which covers >95% of its population. WeDoctor, like other Chinese MedTechs, has expanded its franchise outside of China and has global ambitions to become the “Amazon of healthcare”. China’s investment in developing and increasing its domestic transformational technologies and upskilling its workforce has made the nation close to technological self-sufficiency and has significantly raised the entry bar for Western MedTechs wishing to establish or extend their presence in the country.

China's progress in AI and digital healthcare underscores the urgent need for Western MedTechs to adopt and implement these technologies. To remain relevant and survive in a rapidly changing global healthcare ecosystem, Western MedTechs might do well to learn from China's endeavours in leveraging AI, big data, and digitalization to drive innovation, enhance competitiveness, and ultimately contribute to the transformation of the global healthcare landscape. Notwithstanding, be minded of the ethical concerns Western nations have regarding China’s utilization of big data and AI in its healthcare system and its potential to compromise privacy and individual rights due to the CCP's extensive collection and analysis of personal health data.
 
Takeaways

Digitalization, big data, and AI are transformational technologies that have the power to influence the shape of MedTech companies over the coming decade, and their potential impact should not be underestimated. Fully embracing these technologies and integrating them into every aspect of a business is necessary for growth and competitive advantage. On the other hand, treating them as peripheral add-ons will likely lead to stagnation and decline. However, the path towards their full integration in companies is not without its challenges. MedTech executives, hindered by their analogue mindsets and resource constraints, often struggle to fully embrace the potential of digitalization, big data, and AI. Moreover, there may be pushbacks from various stakeholders including employees, patients, healthcare providers, and investors. These concerns and resistances can impede the progress of transformation within the industry. Nonetheless, governments and payers across the globe are being compelled by unstoppable structural trends to enforce the utilization of digitalization, big data, and AI within healthcare systems. The large and escalating healthcare burdens facing economies throughout the world leave them with little choice but to leverage these technologies to reduce costs, improve patient access and outcomes. In response to the rapidly evolving healthcare landscape, Western MedTechs are making efforts to adopt transformational technologies and expand their presence in emerging markets, particularly China. They recognize the need to stay ahead of the curve and adapt to the changing demands of the industry. However, the adoption and integration of digitalization, big data, and AI by companies thus far have been inconsistent and patchy. To remain relevant and enhance their value, Western MedTechs, while being mindful of ethical concerns about China’s use of AI-driven big data healthcare strategies, might take cues from their Chinese counterparts and embed these transformational technologies in every aspect of their businesses. The transformative impact of digitalization, big data, and AI on MedTech companies cannot be overstated. While challenges and resistance may arise, the inexorable drive towards leveraging these technologies is unstoppable. MedTech companies should shed their analogue mindsets and resource constraints and fully embrace the potential of these transformational technologies.
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1 year, 2 months ago

Re-imagining healthcare

HealthPad
Directory:
Medical Technology
Tags:
Artificial Intelligence
big data
commentary
Machine Learning
  • Recently, Peter Arduini, CEO of GE Healthcare, proclaimed that the software development business “is central to our growth strategy”
  • Although AI is in its infancy, AI technology has become embedded in all aspects of care journeys: from diagnosis to recuperation at home; from prevention to improved lifestyles
  • Notwithstanding, many established MedTech leaders still advocate the production of physical devices for episodic surgical interventions marketed by B2B business models in wealthy regions of the world
  • Jenson Huang, a key opinion leader from the AI industry recently stressed how rapidly AI technologies have advanced over the past decade and predicts that AI “will revolutionize all industries” over the next decade
  • If Huang is right and more MedTech leaders bet their future growth on innovative AI driven strategies, healthcare systems will be soon re-imagined

Re-imagining healthcare
 
On 16 February 2023, a Wall Street Journal article announced, “GE Healthcare Makes Push into Artificial Intelligence”. The company, spun-out of General Electric (GE) in January 2023, is now an independent enterprise traded on Nasdaq, and Peter Arduini, its Chief Executive, says that the software development business “is central to our growth strategy”. In the first instance, GE Healthcare is planning to apply artificial intelligence (AI) and machine learning (ML) techniques to masses of disparate data generated by hospitals during patients’ therapeutic journeys, to enhance hospital services, improve patient outcomes and reduce healthcare costs.
 
Arduini is right. However, to fully appreciate the future potential impact of AI technologies on the medical technology industry and healthcare systems, we need to engage with key opinion leaders (KOL) from the AI industry. One such leader is Jenson Huang, a Taiwanese-American electrical engineer, founder, president and CEO of Nvidia, a semiconductor company launched in 1993. Today, it is a world leading, Nasdaq traded AI technology enterprise with a market cap of ~US$509bn, annual revenues of ~US$27bn and >26,000 employees. To put this into a perspective: if AI was the mid-19th century gold rush in the US, then Nvidia would be the producer of pickaxes for the hundreds of thousands of prospectors drawn to Sutter's Mill in Coloma, California. But before engaging with Huang, let us get a better understanding of the state of healthcare systems, AI and ML.
 
In this Commentary

This Commentary discusses Arduini’s proposition that AI big-data driven software strategies, which aim to enhance patient outcomes and reduce healthcare costs, are key to the growth of medical technology companies. This raises a question whether traditional MedTechs, producing physical devices, and marketing them with B2B business models will create sufficient growth and value over the next decade to satisfy their investors. Although AI technologies are in their infancy, they have already entered many areas of healthcare and are well positioned to play a significant role in future, re-imagined healthcare systems. The Commentary describes AI and ML, provides a brief history of AI, outlines its recent uptake in healthcare and notes how AI technologies have been used by both agile start-ups and giant techs to develop ‘big ideas’ with the potential to disrupt the medical technology market. We briefly describe six start-ups that have leveraged AI to enter the MedTech market and by doing so, increased the competitive pressure on traditional enterprises. Although AI technologies have only recently been introduced to healthcare systems, they are embraced by the FDA and feature in many aspects of patients’ therapeutic journeys: from diagnosis and treatment to recovery and rehabilitation at home. The Commentary takeaways suggest that the actions of industry leaders like Peter Arduini will have a significant impact of the shape on healthcare systems over the next decade.
 
Healthcare in crisis

Healthcare systems throughout the world are in crisis and experiencing large and rapidly growing care gaps,which we have described in previous Commentaries. These are created by growing shortages of health professionals and a vast and rapidly growing demand for care from aging populations; a significant proportion of which present with chronic lifetime diseases, such as heart disorders, diabetes, and cancer, that require frequent physician visits and more resources to treat. Such care gaps result in millions of people having difficulties gaining prompt access to health services, which delay diagnosis, worsen patient outcomes, and increase treatment costs. 

Addressing such issues requires re-imagining healthcare systems. Commercial enterprises have a role to play. Like GE Healthcare, agile start-ups and giant techs have embraced new and evolving AI technologies to create innovative offerings that provide solutions to care gaps predicated upon patient-centric, AI big-data strategies. However, many traditional medical technology companies have not developed software offerings and continue to focus on the production of physical devices, and B2B business models to support episodic hospital-based surgical interventions.  
 
Brief history of AI

AI refers to the development of computer systems that can perform tasks, which typically require human intelligence, such as decision making and natural language processing. The technology is based on the premise that machines can learn from data, identify patterns, and make recommendations with minimal human intervention. ML algorithms [instructions carried out in a specific order to perform a particular task] build mathematical models based on sample data, referred to as "training data", to make predictions or decisions without being explicitly programmed to do so.
 
AI has been around since the 1950s. The term was coined by computer scientist John McCarthy in 1956 at the Dartmouth Workshop in Hanover, New Hampshire, USA. In the early days of AI, scientists focused on building computers that could think, reason, and solve problems like humans. In the 1960s and 1970s, AI research concentrated on developing more advanced algorithms and techniques for programming computers to solve tasks. This resulted in expert systems, which used knowledge-based decision making to solve complex problems. In the 1980s, AI shifted towards ML, which allowed computers to learn from experience by enabling them to recognize patterns and make decisions based on data. In the 1990s, AI developed methods for robots to interact with their environment and learn from experience. This led to autonomous robots that can navigate and perform tasks in the real world. Today, AI research is focused on creating more intelligent and autonomous systems and is used in a wide range of applications, and increasingly in healthcare.
 
AI and healthcare

AI’s use in healthcare can be traced back to the 1970s, when researchers developed expert systems that could diagnose and treat certain medical conditions. Early AI healthcare applications were limited by the availability of data and the dearth of computer power. In the 1990s, as computing power increased and the internet became more widely available, AI began to be used more extensively in healthcare. One of the early applications was in radiology, where it was used to interpret medical images. Other applications included decision support systems for medical diagnoses and treatments, and natural language processing systems for medical documentation. In the 2000s, the use of AI continued to expand, with the development of ML algorithms that could analyze large datasets to identify patterns and make predictions. These were used in a variety of healthcare applications, including personalized medicine, drug discovery and medical imaging.
 
Today, AI benefits a wide range of healthcare applications from faster diagnosis to the prediction of pandemics, from clinical decision support to digital therapeutics. The aspiration of AI driven solutions and services in healthcare is super-human performance, free from errors and inconsistencies, and scalable to provide expert-level care across entire health systems. AI technologies have the potential to provide services that improve the accuracy and speed of medical diagnoses and treatments, monitor conditions, assist with recovery, support medicine regimens, facilitate personalized healthcare and reduce costs for providers. These functions are relevant in the context of attempts to narrow care gaps, but they require vast amounts of computing power, which most companies do not have in-house.
 
This is where cloud computing, and Nvidia's new solution come in. Dubbed "DGX Cloud", Nvidia’s offering is an AI supercomputer accessible via a web browser. The company has partnered with various cloud providers, including Microsoft, Google, and Oracle to develop the service, which provides enterprises easy access to the world’s most advanced AI platform and allows them to run large, demanding ML and deep learning workloads on graphic processing units (GPUs) to generate and implement ‘big ideas’.
 
Big ideas

New entrants to the medical technology market - agile start-ups and giant techs - often have ‘big ideas’; innovations with the potential to inspire stakeholders and disrupt the industry. By contrast, traditional MedTechs who do not employ AI strategies tend to have a dearth of big ideas and mainly focus their R&D spend on incremental improvements to their legacy devices. By contrast, new entrants have accelerated the use of AI, ML, and data analytics to help diagnose diseases earlier and monitor patients remotely. Further, they have championed wearable devices like Fitbits and Apple Watches that help people track their health metrics and allows them to make smarter decisions about their wellbeing. This is helping to transform the modality of healthcare from ‘diagnosis and treatment’ to ‘prevention and lifestyle’. 
 
Start-ups with big ideas
 
There are hundreds of healthcare start-ups with big ideas predicated upon innovative AI technology. To provide a flavour of these we briefly describe six.
 
Biofourmis
Boston based Biofourmis was founded in 2015. Its Biovitals™ Analytic Engine brings patient-specific data and ML together to provide the right care, to the right patients, at the right time. Advanced analytics process continuous and episodic data, notify clinicians of changes in patients’ conditions, and enable early intervention. With digital medicine, modular treatment algorithms (based on a patient’s disorder) enable the delivery of optimal medication.
 
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TytoCare
TytoCare is a New York-based medical technology start-up, founded in 2012, which aims to transform primary care by enabling people to have 24-7 medical examinations with a physician from the comfort of their home. The company has developed a suite of easy-to-use medical devices with built-in guidance technology and ML algorithms to ensure accuracy, which replicate face-to-face clinician visits. The devices include a hand-held modular tool for examining the lungs, throat, heart, skin, ears, and body temperature, and a health platform link to the cloud for storing, analysing, and sharing health data derived from the examinations.
 
Doctolib
Doctolib is a French digital health company founded in 2013. Its main product is a software-as-a-service platform for health professionals, which allows patients to book in-person and video consultations with healthcare providers. In January 2021, Doctolib acquired Tanker, a French start-up that developed the world’s first end-to-end encryption platform in the cloud, which Doctolib had been using since 2019. The Tanker platform is designed to be used by developers with no cryptographic skills and enables online businesses to easily encrypt their user’s sensitive data at the source: directly on end-users' devices. In October 2021, Doctolib acquired Dottori, an Italian online medical appointment scheduling service. The company is currently valued at >US$6bn, and  is used by ~300,000 healthcare professionals and ~70m patients in Europe.
 
CMR Surgical
CMR Surgical develops equipment and systems that aid in minimal access surgeries. During its establishment in 2014 in Cambridge, UK, the company’s founders asked, “why are so many people not receiving minimal access surgery and how can we change this?”. CMR’s main product is “Versius”, an EUMDR compliant device developed for high precision operations. During surgical procedures it can continuously collect data, which are stored and analysed to support surgeon training, and enhance the performance of future surgeries.
 
Healthy.io
Healthy.io is an Israeli start-up established in 2013. Its founders saw an opportunity to increase access to healthcare by leveraging the continuous improvement in smartphone cameras, which they transformed into at-home medical devices. As smartphone camera capabilities grew, Healthy.io’s range of clinical grade services expanded. With the company’s app and kits, users can undertake unitary tract infection (UTI) testing, prenatal monitoring, open wound assessments, and more, all in their homes. Health.io has partnered with healthcare systems throughout the world to provide clinical results at critical moments.
 
Proov
Proov, a US femtech start-up based in Boulder, Colorado, whose flagship offering is a rapid response progesterone test strip invented by Amy Beckley, a pharmacologist, with expertise in hormone signaling. It is the only FDA-cleared (March 2020) urine progesterone (PdG) test to confirm successful ovulation at home. Lack of, or insufficient ovulatory events, is the primary cause of infertility worldwide. In the US, ~12% of couples are diagnosed with infertility each year.  Thus, being able to confirm ovulation is an essential component of infertility evaluations in women.  Gold standards for confirming ovulation include transvaginal ultrasounds and serum progesterone blood draws. Both techniques are invasive, expensive, and/or inaccessible to most women. Proov’s offering is a non-invasive, inexpensive, home-based testing system.
 
A new era for AI in healthcare
 
Such start-ups with AI driven offerings suggest a new era for healthcare, which also is signalled in the introduction to a 2021, FDA action plan for AI/ML-based software medical devices. The plan describes how traditional B2B MedTech strategies are being complemented with B2C digital solutions and services that support entire patient journeys. According to the FDA’s action plan, “Artificial intelligence (AI) and machine learning (ML) technologies have the potential to transform healthcare by deriving new and important insights from the vast amount of data generated during the delivery of healthcare every day. Medical device manufacturers are using these technologies to innovate their products to better assist healthcare providers and improve patient care. One of the greatest benefits of AI/ML in software resides in its ability to learn from real-world use and experience, and its capability to improve its performance. FDA’s vision is that, with appropriately tailored total product lifecycle-based regulatory oversight, AI/ML-based Software as a Medical Device (SaMD) will deliver safe and effective software functionality that improves the quality of care that patients receive”. The agency currently has several ongoing projects designed to develop and update regulatory frameworks specific to AI. As of early 2023, there have been >500 FDA approved AI/ML-algorithms as medical devices.
 
Al and healthcare systems

Although AI is in its infancy and has only relatively recently begun to be used in healthcare systems, it has already taken root in many healthcare applications, including data analysis, diagnoses, monitoring, personalized apps, robotics, wearables, and virtual health assistance. This suggests a new era and the re-imagination of healthcare. Ambulances have become smart platforms, equipped with AI-based systems connected to hospitals, which can be used to diagnose medical conditions and provide real-time treatment recommendations. A&E departments use AI driven automated triage and diagnosis systems to assess incoming patients and prioritize those with the most serious conditions quickly and accurately. AI is also used to automate the dispensing of medications. Hospitals employ AI-based systems to analyze medical images such as X-rays and CT scans, which help medical personnel to quickly identify any abnormalities and make more accurate diagnoses. Surgery employs AI-enabled systems to assist with planning procedures, automating the delivery of anesthesia, and performing complex and delicate surgical interventions. Virtual recovery coaches use AI technology to create personalized plans for individuals recovering. Smart systems collect real time patient data and provide advice and support to help patients stay on track from their homes. AI-powered medication management systems help patients to track and manage their medications and send alerts to healthcare providers if there are any issues.
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The cusp of a new era

According to Huang, a new era of AI has been triggered by a technology most people have become familiar with over the past few months: ChatGPT. Developed by OpenAI and built on top of its family of generative, large language processing models, which have been fine-tuned using both supervised and reinforcement learning techniques.
Huang views ChatGPT, as “one of the greatest things that have been done in computing”. Generative AI models [algorithms that generate new outputs based on the data they have been trained on] have >100bn parameters and are the most advanced neural networks in today's world.  In no computing era has one computing platform (ChatCPT) reached ~150m people in ~60 days. In commercial terms this means, “a torrent of new companies and new applications . . . Nvidia is working with ~10,000 AI start-ups throughout the world in almost every industry”, says Huang. In a February 2023 earnings call to analysts Huang said that ChatGPT has incentivized businesses of all sizes to purchase Nvidia’s chips to develop ML software. Following the call, Nvidia’s market cap rose by US$79bn.
 
The democratization of AI programming

Huang’s enthusiasm for ChatGPT is partly because he perceives it as “democratising programming” by making human language a perfectly good computer programming language. The platform has the capacity to understand human-explained requests, generate coherent answers, translate texts, write code, and more. It has excited enterprises throughout the world and can be used for copywriting, translation, search, customer support, and other applications. While ChatGPT has many advantages, PyTorch and TensorFlow, two free and open-source software libraries have arguably done more to democratise programming by making it relatively easy to develop sophisticated ML applications without extensive programming skills. Notwithstanding, Huang is right to stress the significant leaps forward made by AI in the recent past and right to suggest that “AI is at a watershed moment for the world”.
 
Edge computing

Over the next decade, Huang predicts there will be a proliferation of edge-computing made possible by the spread of the Internet of Things (IoT). Edge computing is a connectivity paradigm that focusses on placing processing near to the source of data. This suggests that fewer activities will be executed using cloud computing. Instead tasks will be relocated to a user’s PC, cell phone or IoT devices. Huang refers to these as ‘AI factories’, which are positioned to have a significant impact on healthcare. By 2025, the global market for Internet of Medical Things (IoMT) is estimated to reach >US$500bn. This signals a significant change because currently most healthcare computing takes place in on-premises networks or, in the cloud. However, processing healthcare data from afar can be limited by infrastructures that cannot manage them quickly, securely, or cost-effectively. To address these issues, healthcare companies are implementing edge computing, which facilitates data being analysed and acted upon at the site of collection. This reduces end-to-end congestion and the constraints of limited connectivity and data broadband connections across vast distances by lowering transmission time, while also reducing risks to privacy and data protection. 

According to Huang, “AI processing performance has been boosted by a factor of no less than one million in the last 10 years”. Over the course of the next decade Huang predicts there will be, “new chips, new interconnections, new systems, new operating systems, new distributed computing algorithms and new AI algorithms (which will) accelerate AI by another million times."
 
Takeaways

Our discussion suggests that Peter Arduini, CEO of GE Healthcare, is right: software development is central to the growth potential of medical technology companies. Over the past two decades AI, ML and big-data strategies have substantially extended the horizons of industry players by giving them the means to provide software solutions and services to support entire patient journeys. This has introduced B2C MedTech business models, which complement conventional B2B models, and have the potential to provide access to new revenue streams while improving patient outcomes and reducing healthcare costs. If software initiatives like Arduini’s and others spread, healthcare systems are likely to be re-imagined. The fundamental technology of MedTech leaders is intelligence. But as Huang suggests, “We’re in the process of automating intelligence”, which can only empower industry executives. “The thing that’s really cool”, says Huang, “is that AI is software that writes itself, and it writes software that no humans can. It’s incredibly complex. And we can automate intelligence to operate at the speed of light, and because of computers, we can automate intelligence and scale it out globally instantaneously”. If Huang is right, over the next decade, AI is well positioned to play a significant role in re-imagining healthcare.
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1 year, 3 months ago

The impact of big data, artificial intelligence, and machine learning on the medical technology industry

HealthPad
Directory:
Medical Technology
Tags:
Artificial Intelligence
big data
cloud service
commentary
cybersecurity
Machine Learning
MedTech
  • The core business of medical technology companies (MedTechs) has been manufacturing and marketing physical devices
  • Physical devices will continue to be a substantial part of their business, but on their own, are unlikely to deliver high growth rates, which are more likely to come from artificial intelligence (AI) data driven strategies that improve patient outcomes
 
The impact of big data, artificial intelligence, and machine learning on the medical technology industry
 
James Carville, an American strategist, who played a leading role in Bill Clinton winning the 1992 presidential race, insisted that the campaign focus on the economy and coined the phrase “It’s the economy, stupid”. If Carville was asked today for a winning long-term growth strategy for medical technology companies, might he say, “It’s big data, stupid”?
 
This Commentary suggests that while physical products have been the backbone of MedTech companies in the past, they are unlikely to contribute significantly to future growth rates, which are more likely to come from artificial intelligence (AI) driven big data innovations, which create new solutions that improve patient journeys and outcomes.
 
In this Commentary
 
This Commentary describes the meaning of ‘big data’ in a healthcare context, explains ‘the data universe’ and stresses not only its immense volume, but also its variety, and the phenomenal speed at which the data universe is growing. Today, most industries leverage big data and AI techniques to create innovative offerings that drive growth and enhance competitive advantage. However, with few exceptions, traditional MedTechs have been relatively slow to collect and analyse a wide range of health, medical and lifestyle data which have the potential to provide innovative software offerings that improve patients’ therapeutic journeys and complement physical products. This is partly because the industry must adhere to strict regulations and partly because many medical technology companies lack the necessary capabilities and mindsets to collect and leverage big data. Most have business models that tweak legacy physical products and accept growth rates of ~5% as the ‘new normal’. We provide a brief history of big data and AI business strategies mainly to underline that these are relatively new. It was only in the early 2000s that electronic health records (EHR) began to replace paper-based patient records, which were stored in numerous filing cabinets in healthcare silos. It was not until ~2015 that EHRs became standard practice and researchers started to apply algorithms to EHRs and other data to detect patterns and make predictions that could improve diagnoses and treatments, enhance patient outcomes, and reduce healthcare costs. The increased use of big data and AI techniques in healthcare raises important cybersecurity concerns and trust issues because health professionals and patients do not understand how algorithms arrive at their conclusions and actions. Cybersecurity concerns are addresses by a range of encryption techniques and security protocols. Trust in algorithms has been helped by the development of  ‘explainable AI’, which is software that describes the essence of algorithms in easily understood terms. However, more work is still needed in these two areas. We introduce cloud and cloud services together with an explanation why these have experienced such rapid growth across all industries in recent years. The cloud makes it easier to store and access big data via the internet from anywhere in the world. Cloud services provide security for big data as well as a range of management and analytical tools that help to transform data into revenue generating software offerings. For MedTech companies, the cloud and cloud services provide the basis for more efficacious R&D. The medical technology industry has become bifurcated between companies that leverage AI driven big data strategies to enhance growth rates and those that predominantly focus on legacy physical product offerings and settle for lower growth rates. Over the past decade the nature of the medical technology industry has changed; partly because of AI big data strategies supported by the cloud computing and a large and rapidly growing range of open-source, easy-to-use AI tools. This has given small companies a competitive advantage. The Commentary concludes by describing a few of these small MedTechs with disruptive digital products that target large, rapidly growing, underserved market segments.       
 
Big data and healthcare

Big data are comprised of a wide range of information collected from multiple sources that surpasses the traditionally used amount of storage, processing, and analytical power and is unmanageable using conventional software tools. In healthcare settings, big data include hospital records, medical records of patients, results of medical examinations, and data generated by traditional medical devices as well as various biomedical and healthcare tools such as genomics, wearable biometric sensors, and smartphone apps. Biomedical research also generates data relevant for the medical technology industry.
 
The data universe

The massive amount of data, which is generated from the entirety of the internet is referred to as the ‘data universe’. It is not only its volume that makes this special, but it is also the variety of the data and the phenomenal speed at which the universe is growing. The International Data Corporation (IDC) estimated that the data universe grew from ~130 exabytes in 2005 to >40,000 exabytes in 2020.  To put this in perspective: 1 gigabyte of data is 1bn bytes (18 zeros after the 1 or 230 bytes), and 1 exabyte is equal to 1bn gigabytes.
Data generated healthcare innovations

In the past, collecting and interpreting vast quantities of data was not feasible, partly because computer systems were relatively small and did not generate much data, and partly because technologies to manage big data were underdeveloped. Fast forward to the present, and businesses across most industries now generate enormous amounts of data. Organizations apply AI and machine learning (ML) techniques to these data to create innovative product offerings to access new revenue streams with significant growth potential. Such technologies, combined with health-related big data, can positively impact the medical technology industry by generating novel diagnostics and treatments for patients, streamlining the process of medical record keeping and developing more personalized and responsive care plans that improve patient journeys and outcomes.

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The new rapidly evolving AI data driven healthcare ecosystem

Despite the potential commercial advantages of AI data driven diagnostic and therapeutic solutions, many traditional MedTechs have been slow to collect health and lifestyle data from multiple sources to develop software offerings, which complement their legacy physical products. One notable exception is Philips Healthcare. In the early 2000s, the company was challenged by new entrants to the market who were successfully leveraging information from health wearables and other sources to create and market AI data driven offerings. At the 2016 annual conference of the American Healthcare Information and Management Systems Society (HIMSS) in Chicago, Jeroen Tas, a Philips executive, said, “We are in the midst of one of the most challenging times in healthcare history, facing growing and aging populations, the rise of chronic diseases, global resource constraints, and the transition to value-based care. These challenges demand connected health IT solutions that integrate, collect, combine, and deliver quality data for actionable insights to help improve patient outcomes, reduce costs, and improve access to quality care”.
 
Philips had the mindset and resources to respond positively to this rapidly changing ecosystem. In 2017 the company appointed Tas as its Chief Innovation & Strategy Officer, tasked with launching a suite of big data AI driven solutions, the IntelliVue® patient monitors, which support the growing demands of health professionals to provide quality care and improved outcomes for an expanding population of older, sicker patients with fewer resources. These monitoring solutions seamlessly connect big data, AI technology and patients to support health professionals to manage patients as they transition through their care journeys. In 2016, Philips and Masimo, a medical technology company specializing in non-invasive AI data driven patient monitoring devices, entered a multi-year business partnership involving both companies’ innovations in patient monitoring. Philips agreed to integrate Masimo's measurement technologies into its IntelliVue® monitors, to help clinicians assess patients’ cerebral oximetry and ventilation status. The outcome of the collaboration was the launch of a new suite of patient solutions, called Connected Care, which give healthcare providers the ability to monitor patients more effectively and reduce costs.
 
The bifurcation of the MedTech market

In addition to large MedTechs such as Philips and Masimo, there are hundreds of small companies developing AI driven big data offerings aimed at improving patient outcomes. The reasons for many traditional companies’ slowness to fully leverage big data and AI applications are partly because medical devices are required to comply with stringent regulatory guidelines and partly because of the lack of capabilities. The different responses have bifurcated the industry. On the one hand there are traditional MedTechs, which predominantly focus on existing customers and market legacy physical offerings in slow growing markets. On the other hand, there are many small companies and a few very large medical technology corporations, which have embraced AI driven big data patient-centric solutions.
 
A brief history

Big data has its genesis in the 1950s and 1960s when scientists and mathematicians began exploring the possibility of using computers to process large amounts of data to make intelligent decisions. This led to the development of technologies such as the first neural networks, which laid the foundation for modern Deep Learning. In the 1980s, researchers at IBM popularized the concept of big data to describe the process of collecting and analyzing large amounts of data, which empowered organizations to gain insights from information that previously was too complex to process. The 1990s saw the development of AI and ML, which enabled computers to learn from data and make decisions without the need for explicit programming. By the early 2000s, AI-based algorithms empowered machines to learn from data and make predictions. Many organizations, across a range of industries, saw the commercial opportunities of this and acquired capabilities to collect, store and analyse large amounts of information to identify patterns and trends that were previously impossible to detect.  Without large amounts of data, AI and ML techniques are less effective, which is significant for healthcare and the medical technology industry.
 
Big data in healthcare

AI driven big data strategies are becoming increasingly important in healthcare. This is because AI techniques applied to masses of health-related information can improve patient care, enable more effective decision-making, reduce costs, identify new treatments, explore new markets, and create more efficient healthcare systems. Further, such applications can provide more accurate and timely diagnoses, as well as insights into how various treatments affect different people. As increasing amounts of health information become available, and data handling techniques improve, so traditional MedTech companies will have opportunities to boost their growth by complementing their physical devices and volume-based care with digital assets and personalised care.
 
Paper-based mindset

Until recently health professionals were responsible for most of the different types of data associated with a patient’s treatment journey, which included medical histories, known allergies, medical and clinical narratives, images, laboratory examinations, and other private and personal information. Until the early 2000s these data were recorded on paper and stored in filing cabinets across numerous healthcare departments. It was not until 2003 that the US Institute of Medicine used the term ‘electronic health records’ (EHR). By 2008, only ~10% of US hospitals were using EHRs, which increased to ~80% by 2015. As EHRs became standard practice across multiple providers and data interoperability issues were resolved, the provision of healthcare improved, and medical errors and healthcare costs were reduced. Currently, the American National Institutes of Health (NIH) is inviting 1m people from diverse backgrounds across the US to help build a comprehensive big data set, which can be used to learn more about how biology, environment and lifestyles affect health in the expectation of discovering new ways to treat and prevent disease.
 
Trust and medical algorithms
 
As AI driven big data applications have increased, so trust in algorithms has been raised as an issue. This has been a major concern in healthcare. To address this challenge, explainable AI, has been developed. This is an AI technology that explains decisions and actions made by algorithms in a way that is easily understood by health professionals and patients. Explainable AI has helped to create trust in algorithms by providing a level of transparency, understanding and accountability. Further, incorporating feedback from medical professionals, patients, and other stakeholders into the development of medical algorithms has also helped to build trust. However, this entails collecting a wider variety of data than many healthcare companies are used to.
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Have diversified medical technology companies blown their competitive advantage?
Big data healthcare strategies and security
 
With the increasing number of big data and AI healthcare solutions, cybersecurity has become a concern. Reducing this involves using technologies such as data encryption, secure cloud computing (see below), and authorization protocols to protect data stored in large databases. Additionally, organizations may use AI-driven applications to monitor their systems to find anomalies, detect malicious activity and unauthorized access to sensitive, personal information. To ensure the security of healthcare data, organizations also employ measures such as risk assessments, incident response plans, and regular security training of their staff.
Cloud storage and services

Since the early 1990s, big data have benefitted from cloud storage, which makes it easier to store and access data over the internet and helps businesses to become more efficient and productive. It also offers organizations scalability, more control over their data and reduced costs. Organizations can: (i) easily increase their storage capacity as their data needs grow, (ii) access their data from anywhere in the world, and (iii) stop investing in expensive local storage devices. Further, cloud storage is becoming more secure, with encryption and other security measures making it safer to store data.
 
Companies moving their data from local storage devices to the cloud is more than just a simple transfer process and can be a complex, multi-year journey. Any organization that has accumulated several legacy databases and infrastructures will have to develop and manage a hybrid architecture to transfer the data. However, once in place and shared among stakeholders, cloud-based platforms can assist in unlocking clinical and operational insights at scale while speeding up innovation cycles for continuous value delivery. In combination with a secure and interoperable network of connections to hospital systems, cloud-based solutions represent an opportunity for healthcare leaders to unlock the value of data generated along the entire patient journey, from the hospital to the home. By turning data into insights at scale, it is possible to empower healthcare professionals by helping them to deliver personalized care, improved patient outcomes and lower costs.
 
The cloud also offers an increasing number of computing services. These are provided by companies such as Amazon Web Services, Google Cloud Platform, Microsoft Azure, IBM Cloud, Oracle Cloud, and Rackspace Cloud. The services include: (i) Infrastructure-as-a-Service (IaaS), which provides users with access to networks, storage, and computing resources, (ii) Platform-as-a-Service (PaaS) helps users to develop, run, and control applications without the need to manage infrastructure, (iii) Software-as-a-Service (SaaS), provides access to a variety of applications, (iv) Data-as-a-Service (DBaaS), gives users access to several types of databases, and (v) Serverless Computing enables users to run code without needing to provision or manage servers. Such services are expected to continue growing and help to transform healthcare. The provision of cloud computing services in healthcare makes medical record-sharing easier and safer, automates backend operations and facilitates the creation and maintenance of telehealth apps. The increasing use of data and cloud services by MedTech companies helps to break down data silos and develop evidence-based personalized solutions for a connected patient journey. In 2020, the healthcare cloud computing market was valued at ~US$24bn, and it is expected to reach ~US$52bn by 2026, registering a CAGR of >14% during the forecast period. Major drivers of cloud services include the increasing significance of AI driven big data applications.
 
Changes the nature of R&D

Further, the cloud can change and speed up R&D. The starting point for MedTech R&D should be evolving patient needs and affordability. Healthcare-compliant cloud platforms offer a flexible foundation for the rapid development and testing of AI driven big data solutions created by cross functional teams working across an entire life cycle of an application: from development and testing to deployment. This changes medical technology companies’ traditional approach to R&D by transforming it into short cycles undertaken by multiple stakeholders. This modus operandi is replacing traditional lengthy and expensive R&D often carried out in an organisational silo and constrained by annual budgeting cycles. This often means that a significant length of time passes before an innovation gets into the hands of health professionals and patients for testing. Digital health solutions, on the other hand, can be tested by physicians and patients early in their development and improved features quickly added.   
 
Free and easy to use AI and ML software libraries

In the early 2000s, when AI and ML were in their infancy, companies needed data engineers with advanced mathematical capabilities to build complex AI systems. Today, this is unnecessary because of the development of simplified AI and ML libraries such as PyTorch and Tensorflow. These are free, easy to use, open-source, scalable AI, and ML packages, which reduce the need for data engineers to have advanced mathematical skills to build effective software health solutions. PyTorch, released in 2016,  was developed by Facebook and then Meta AI, and is now part of the Linux Foundation. The technology is known for its ease of use and flexibility, making it favoured by developers who want to rapidly prototype and experiment with new ideas. Its tools support graphics processing, which is popular with deep learning medical imaging strategies that involve training large, complex models on big data. TensorFlow was developed by the Google Brain team and originally released in 2015 for internal use.  It is a highly scalable library for numerical computations and allows its users to build, train and deploy large-scale ML models. Both platforms have become significant open-source tools for AI and ML due to their ability to support the development and training of complex models on large datasets. They have been widely adopted by researchers and developers throughout the world and are regularly used in a variety of applications relevant to the medical technology industry. Significantly, they give smaller MedTechs a competitive advantage. 
 
Disruptive effects of AI driven big data strategies

The development and availability of big data and predictive AI help small medical technology companies enter markets, grow, and strengthen their competitive positions, which has the potential to change market dynamics. Over the past decade, several large medical technology companies have experienced their markets dented by small companies, which have successfully used open-source AI applications to leverage big data. For example, Philips Healthcare’s market was affected by the emergence of innovative offerings developed by new entrants using cloud computing services and big data from medical wearables. Above we described how Philips robustly responded to this and became a market leader in AI data-driven patient monitoring technology. Siemens Healthineers’ market share suffered from small MedTechs with innovative AI driven offerings. Further, the rise of digital imaging technology caused GE Healthcare’s market share to shrink. These vast companies have since developed AI driven big data strategies and bounced back. However, traditional MedTechs that fail to leverage big data and AI capabilities risk being left behind in an increasingly competitive digitalized industry.
 
Small MedTechs using big data and AI

Examples of small MedTechs that leverage big data, AI, and ML techniques to capture share of large underserved fast-growing market segments include Brainomix, which was spun out of Oxford University, UK, in 2010 and serves the stroke market. Iradys, a French start-up specialising in interventional neuroradiology. Elucid, a Boston, US-based MedTech founded in 2013, which has developed innovative technology that supports the clinical adoption of coronary computed tomography angiography, and Orpyx Medical Technologies, a Canadian company that provides sensory insoles for people living with diabetes. These are just a few examples of small agile companies that collectively have helped to bifurcate and disrupt segments of the medical technology industry by developing offerings predicated upon big data, AI and ML that deliver faster, more accurate diagnoses to ensure that patients get the treatment they need, when they need it.

Brainomex’s lead product offering is a CE-marked e-Stroke platform, which has been developed using data from images sourced across 27 countries including the UK, Germany, Spain, Italy, and the US and provides fast, effective and accurate analysis of brain scans that expedite treatment decisions for stroke patients. The platform has been adopted across multiple healthcare systems throughout the world, and for the past two years, England’s National Health Service (NHS) has been using the technology on suspected stroke patients. Early-stage analysis of the technology predicated on >110,000 patients suggests that eStroke can reduce the time between presenting with a stroke and treatment by ~1 hour and is associated with a tripling in the number of stroke patients recovering with no or only slight disability - defined as achieving functional independence - from 16% to 49%. With this disease, time is of the essence because after a stroke, each minute that passes without treatment leads to the death of ~2m neurons (nerve cells in the brain), which cause permanent damage. It can be challenging for health professionals to determine whether stroke patients need an operation or drugs, because the interpretation of brain scans is complicated and specialist doctors are required. Sajid Alam, stroke consultant at a large regional hospital in the UK, (Ipswich Hospital), reflected: “As a district general hospital, we don’t have ready access to dedicated neuroradiologists to interpret every stroke scan. Having Brainomix’s AI software gives us more confidence when interpreting each scan.”

Intradys is a French start-up, which develops algorithms that combine ML and mixed reality to empower interventional neuroradiologists and help them enhance the care of stroke patients. Orpyx Medical Technologies provides sensory insoles for people living with diabetes who have developed peripheral neuropathy to help prevent foot ulcers. The insoles collect data on pressure, temperature, and steps and give feedback to the wearer and healthcare professionals. Elucid is a Boston-based MedTech founded in 2013. The company’s offerings are predicated on big data, AI, and ML to provide fast and precise treatments that improve the outcomes of patients with cardiovascular disease and reduce healthcare costs. Heart attack and stroke are primarily caused by unstable, non-obstructive plaque (the buildup of fats, cholesterol, and other substances in and on the artery walls) that often goes undiagnosed and untreated. Current non-invasive testing cannot visualize the biology deep inside artery walls where heart disease develops. Elucid’s lead offering is an FDA-Cleared and CE-marked non-invasive software to quantify atherosclerotic plaque.
 
Takeaways
 
The potential benefits for medical technology companies that leverage AI driven big data strategies include: (i) improved diagnoses and treatments, (ii) enhanced patient journeys and outcomes, (iii) cost savings, (iv) a better understanding of stakeholders’ needs, (v) superior decision-making, (vi) more effective products and services, and (vii) increased competitive advantage. Big data strategies may also be used to uncover insights from large datasets to develop predictive models that can automate repetitive tasks, optimize care processes, free up resources for healthcare professionals to focus on providing care, and staying ahead of the competition by providing greater insights into customer trends and needs. Medical technology companies that do not leverage AI driven big data strategies to develop innovative products for growth and competitive advantage potentially risk: (i) falling behind the competition in terms of product innovation, (ii) missing out on key market opportunities, as data-driven insights can help identify new trends and customer needs, (iii) struggling to keep up with the changing pace of technological change, as staying ahead of the competition requires a deep understanding of the latest developments in data-driven product development and (iv) losing the trust of customers, as they may be wary of MedTechs that do not use advanced technologies to develop their product offerings. Future significant growth for medical technology companies is more likely than not to come from AI driven big data strategies. Start collecting data.
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1 year, 10 months ago

Should MedTechs follow surgeons or patients?

HealthPad
Directory:
Medical Technology
Tags:
Artificial Intelligence
commentary
medical devices
patient groups
  • The traditional strategy of the medical devices industry has been to maximise the experience of the surgeon
  • This has resulted in paying little attention to the demands of patients
  • Surgeon populations are shrinking while the general population is growing, aging, becoming ill and demanding care
  • This creates care gaps, which are challenging to reconcile, prolong unnecessary suffering and cause unnecessary deaths
  • Reconciling the shrinking supply of health professionals with the increasing healthcare demands has given more weight to patient demands
  • MedTechs will be obliged to recalibrate their approach to patients principally because regulators are involving them in the approval process of medical devices
  • Patient centric digital therapeutic solutions help to reduce care gaps
  • However, developing such digital therapeutics and involving patients will not come easy to traditional MedTechs because of their lack of capabilities and organizational culture
  • Notwithstanding, to be relevant in the future, MedTechs will need to continue to improve their ties with surgeons while increasing their focus on the large and rapidly growing patient demands
 
Should MedTechs follow surgeons or patients?
 
 
Traditional MedTech business models are overwhelmingly focussed on manufacturing physical devices for surgeons to use in episodic, hospital-based, interventions. Over decades, a symbiotic relationship between surgeons and medical device manufactures has been established and led to significant commercial success for both parties. This has meant that MedTechs have not paid the attention they should have to the growing demands of patients, which include primary prevention and screening through diagnosis and staging to treatment, rehabilitation, and the subsequent management of a condition. Should medical device companies double-down on their business models to follow surgeons, or should they change approach and follow patients?
 
In this Commentary

This Commentary has 2 sections: (i) Follow surgeons, and (ii) Follow patients. Section1 suggests that medical device companies will need to continue their mutually beneficial relationships with physicians but tighten their governance ties. Further, leaders might consider some aspects of surgeon populations, which could impact their business model. These include: (i) the increasing shortages and aging of surgical populations, (ii) burnout among surgeons that prompts early retirement, and (iii) the prevalence of unnecessary surgeries. Section 2 considers the business model of MedTechs following patients and suggests that this is likely to become more relevant in the future as regulators are encouraging patient participation in the approval process for medical devices. Further, patient demands are supported by advancing technologies and smart platforms such as PatientsLikeMe. Patient centric solutions tend to be digital therapeutics, based on software rather than hardware. Solutions that address patient care pathways require scarce digital, data management and artificial intelligence (AI) capabilities, which MedTechs tend not to have. To stand a chance of attracting these, MedTechs will need to develop non-hierarchical, agile working cultures with the capacity to innovate at speed. The significance of business models that improve patients’ care pathways is illustrated by two recent, transformative MedTech deals. Takeaways suggest MedTechs should continue following surgeons, albeit under enhanced governance principles and involve patients in the development of devices and increase their capabilities to provide patient centric digital solutions.
 
 
SECTION 1
Follow surgeons
 
The medical devices industry is “big business”. In 2021, the US devoted ~US$199bn (~5.2%) of annual national health expenditures to medical devices. Over the past four decades mutually beneficial relationships between surgeons and medical device companies have been built, and this forms the basis of a dominant industry business model to “follow surgeons”.
 
Surgeons play a crucial role in the conceptualization, development, and enhancement of medical devices; they influence hospital purchasing decisions, and are compensated for providing these services. Further, they are remunerated for representing MedTechs at conferences, giving speeches on behalf of corporations, and playing a critical role in training physicians to use devices because their efficacy is often associated with a specific use technique that needs to be taught. Further, surgeons may receive research grants from MedTechs and be promoted because of their association with a successful innovation. More recently, with the rise of medical device start-ups, the financial incentives to surgeons have included equity stakes in lieu of cash for various contributions. This means that significant financial ties between medical device companies and surgeons are relatively common, which can be the basis for potential conflicts of interest.
 
MedTechs code of conduct

AdvaMed, a US medical device trade association, based in Washington, DC, is aware of such conflicts and suggests that physicians should be compensated at fair market rates for work they perform. The Association is against equity compensation and says that there should be no link between the commercial success of a medical device and a physician. AdvaMed encourages voluntary, ethical interactions and advises member organizations and physicians to disclose all potential conflicts of interest, which include consulting arrangements, training, support of third-party educational conferences, participation in sales and promotional meetings, gifts, grants, and charitable donations.
 
Despite AdvaMed’s best efforts its suggested code of conduct does not appear to work. A bibliometric analysis of 100 clinicians receiving compensation from 10 large MedTechs and published in the November 2018 edition of JAMA Surgery found that conflicts of interest were not declared in 63% of 225 research projects that resulted in publications. Given the increasing significance of environmental, social, and governance (ESG) criteria among socially conscious investors to screen potential investments, it seems reasonable to suggest that MedTechs might consider regularly disclosing all their financial ties with surgeons and health professionals.
More issues to consider

In addition to the increasing significance of ESG issues, there are some further questions associated with MedTech business models that follow surgeons, which corporate leaders might wish to reflect upon. These include: (i) the surgeon population is aging and shrinking, (ii) surgeons have a higher propensity to burnout than other medical specialities, and (iii) surgeons are responsible for a substantial number of unnecessary operations. Let us describe these in a little more detail.
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Shrinking surgeon populations

Throughout the world, populations of surgeons and health professionals are shrinking. Findings of a 2016 US Department of Health and Human Services report suggest that by 2025, there will be shortages in 9 out of 10 surgical specialties in America, with the greatest reduction in ophthalmology, orthopaedics, urology, and general surgery. Research prepared for the Association of American Medical Colleges (AAMC) by the healthcare consulting firm IHS Markit and published in June 2020, suggests that, by 2032, the US could lack ~23,000 surgeons. Although the US has a higher number of total hospital employees than most countries, nearly half of that workforce is comprised of non-clinical staff who are not directly involved in delivering care. For instance, compared to Italy and Spain, America has fewer practicing physicians per capita: 2.6 per 1,000 inhabitants, compared to 4 in Italy and 3.9 in Spain. According to the World Health Organization (WHO), the global shortage of health workers is projected to reach 13m by 2033.
 
Care gaps

One reason for this projected shrinkage is that a large percentage of surgeons are nearing traditional retirement age. For instance, more than 2 in 5 currently active American doctors will be ≥65 years within the next decade. Further, people are living longer, and a substantial percentage are not staying healthy and need care. According to the US Census Bureau the number of Americans ≥65 is expected to reach ~84m by 2050, which is ~2X the 2012 level of 43m. Among this older population there is a large and growing prevalence of chronic lifetime diseases such as cancer, diabetes, heart conditions, respiratory diseases, and mental illness. In the US there are ~150m people with such conditions and ~40% of these are living with ≥2 chronic diseases. According to the US Centers for Disease Control and Prevention, ~90% of the US$4.1trn annual medical spend (~20% of the country's GDP) is attributable to chronic disorders. Such trends magnify the vast and growing pressure on a shrinking pool of health professionals, and this creates challenging care gaps.
 
Digital therapeutics

Care gaps will not be reduced by medical schools training more physicians and nurses. This takes too long to have an impact on the size of the problem. The UK has attempted to reduce care gaps by importing physicians: ~190,000 of the 1.35m NHS staff in England report a non-British nationality, and ~27% of NHS staff in London report a nationality other than British. This policy raises some ethical issues as most are imported from developing economies with underdeveloped healthcare systems and a scarcity of health professionals. The option to import physicians is not open to the US because its immigration policies make it difficult for international health professionals to work in America. Recently, many advanced industrial economies have sought to reduce their care gaps by developing digital therapeutic solutions for patients, which extend the reach of physicians by overcoming time, place and personal constraints that limit care delivery.
 
Surgeon burnout

Findings of a research study published in the June 2018 edition of the Journal of the American College of Surgeons suggest that the prevalence of burnout among surgeons has increased over time. The research references the 2015 Medscape Physician Lifestyle Report, which argues that burnout among surgeons is on the rise and documents burnout rates among various specialisms ranging ~37% to ~53%, with general surgeons nearing the top of the list at 50%. Research on the impact of the COVID-19 crisis on healthcare professionals published in the December 2021 edition of the Mayo Clinic Proceedings, found that ~1 in 3 US physicians expressed a clear intention to reduce their work hours, and ~1 in 4 intended to leave their practice altogether. Such trends are concerning considering the aging of the US population and the subsequent increased pressure this puts on healthcare systems.
 
Many factors contribute to surgeon burnout. Common causes among American surgeons include long work hours, delayed gratification, challenges with work-home balance, and issues associated with patient care in a changing healthcare ecosystem. According to the WHO’s International Classification of Diseases, (ICD-11) burnout results from “chronic workplace stress that has not been successfully managed”. It is characterised by being emotionally exhausted, feelings of cynicism and loss of empathy and a sense of low personal accomplishment with respect to one’s work. A meta-analysis of the prevalence of burnout published in the March 2019 edition of the International Journal of Environmental Research and Public Health  suggests that surgeons experience elevated rates of depression and psychiatric distress and posits that burnout among junior surgeons is at an epidemic level, which affects patient safety, quality of care and patient satisfaction.
 
Unnecessary surgeries

Another issue for medical device leaders to consider is the incidence rates of unnecessary surgeries. These are any intervention, which is not needed, not indicated, or not in the patient’s best interest when weighed against other available options.  Unnecessary surgeries are not a recent phenomenon: they are a significant reality that continue to expose patients to unjustified surgical risks. In 1976, the American Medical Association (AMA) called for a congressional hearing to address the issue, claiming that each year there are “2.4m unnecessary operations performed on Americans at a cost of US$3.9bn and that 11,900 patients had died from unneeded operations”.  Across the US, the phenomenon is patchy. A cross-sectional study of five US metropolitan areas and published in the January 2022 edition of the Journal of the American Medical Association found significant differences in physician treatment recommendations across a range of specialisms.

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Most common unnecessary surgeries

The incidence rates of unnecessary surgeries appear more prevalent in spinal, gynaecological and some orthopaedic procedures. Clinical trials have shown that a significant percentage of spinal fusions for back pain do not lead to improved long-term patient outcomes when compared to non-operative treatment modalities, including physical therapy and core strengthening exercises. Despite these findings, spinal fusion rates continue to increase significantly in the US.
Further, women are at high risk of unnecessary hysterectomies and caesarean sections. Although these rates are moderating, a study for the American College of Obstetricians and Gynecologists, suggested that hysterectomies were improperly recommended in ~70% of cases, even though there were non-surgical alternatives. Hysterectomies can lead to bladder and bowel dysfunction, prolapse, and incontinence,  as well as a 4-fold increased risk of pelvic organ fistula surgery. A study in Health Affairs found that caesarean rates varied significantly (from 2.4% to 36.5%) in hospitals across the US, even among those with low-risk pregnancies.
 
Another study published in Health Affairs suggests that after patients received information on alternatives to joint replacement surgeries, ~26% had fewer hip replacements and ~38% had fewer knee replacements. Each year in the US, >1m total hip and total knee replacement procedures are performed.
 
 
SECTION 2
Follow patients
 
It is not uncommon for MedTech leaders to say that they put “patients first” when developing devices. However, although things are changing, which we describe below, this is more rhetorical that factual. MedTech R&D teams tend to be relatively remote, inwardly focussed, and, particularly in the US, patient voices are generally ignored and not perceived as an integral part of the process.
 
However, the healthcare ecosystem is changing and “following surgeons” cannot constitute an entire strategy for MedTechs. In the future, MedTech business models that follow patients will be driven by patients’ knowledge and their increasing demands to participate in their healthcare decisions, the movement towards personalized care, and regulators’ mandates to incorporate patient perspectives into the development of medical devices and approval processes (see below). Earlier, we suggested that, when surgeons engage with medical device corporations there are competing interests, which often are not disclosed. By contrast, patients are primarily driven by their own safety and wellbeing, which, contrary to surgeons, are grounds for promoting mutual accountability and understanding with healthcare providers.
 
To remain relevant, MedTechs will need to incorporate patient perspectives and patient data into their business models, not least because patients are co-producers of their health and represent a consistent factor, probably the only consistent factor, throughout the care pathway. Further, patients, empowered by digital therapeutics and health information from wearables, hold invaluable personal data, which are often critical to improving care pathways, and outcomes.
 
PatientsLikeMe
 
Patient voices were loud and influential long before MedTechs recognised the significance of engaging patients in development processes. Consider PatientsLikeMe, a digital platform founded in 2004, with a mission to improve the lives of patients by sharing knowledge, experiences, and outcomes. The company quickly grew to become the world’s largest integrated community, health management, and real-world data platform. Via the site, users can document and share their experiences, track their conditions, and communicate with others living with similar disease states. Data generated by patients who use the site are systemically collected and quantified by the company, while providing users with an environment for peer support and learning. Today, PatientsLikeMe has >0.8bn users representing >2,900 conditions. The company makes money by selling the information patients share in de-identified, aggregated, and individual formats. In 2019, the platform was acquired by the UnitedHealth Group, an American multinational healthcare and insurance company, after former President Trump’s administration forced it to seek a buyer because its majority owner was China-based iCarbonX.
 
Increasing patient input in approval processes for medical devices

What will make MedTechs wake up to the significance of patient perspectives in the development of medical devices are initiatives and demands made by regulators. For the past decade, European regulators through the European Medicine’s Agency (EMA). have solicited patient inputs into their approval process for medical devices. In 2014, the FDA and the EMA created a joint working group to share knowledge and information on patient engagements. In 2007, the Clinical Trials Transformation Initiative (CTTI), a public-private partnership was co-founded by the US Food and Drug Administration (FDA) and Duke University and modelled on the EMA Patients’ and Consumers’ Working Party. CTTI’s mission is to develop and drive patient involvement in the development and approval of devices, which is expected to increase the quality and efficiency of clinical trials. Since its foundation, the CTTI has become a leader in evolving and advancing clinical trials, making them more efficient, and patient focused.
 
In December 2017, a nationwide request in the US was made for patients and patient advocate groups to join the CTTI and become more involved in healthcare product development and in the FDA product reviews. This call came ~1 year after the 21st Century Cures Act became law in December 2016. The Act’s intention is to expedite the process by which new medical devices and drugs are approved by easing the requirements put on companies seeking FDA approval for new products and indications. Under Section 3001 of the Act, the FDA is required to report any patient experience data that were used to support an approval process and to publicly provide aggregate reports on agency use of those data at five-year intervals. This suggests that MedTechs wanting new FDA approvals will need to provide patient-driven data.
 
These initiatives are driven by an ever-improving consumer-controlled social and health data ecosystem, advancements in personal genetic understanding, and increased healthcare cost-sharing. Patient-driven changes are systematically beginning to inject more than token patient participation and viewpoints into all stages of device and drug development.
 
A cultural shift

Improving patient engagement in the development process of medical devices will be challenging for MedTechs that have focussed their business models mainly on manufacturing physical devices and building relationships with surgeons, rather than developing digital assets for patients. The latter requires scarce data management and AI capabilities, which do not thrive in conservative hierarchical organizations. Rather, they require a culture, which promotes innovation at speed and agile ways of working. A recent survey of European executives by The Economist Intelligence Unit, found that poor collaboration between a company’s IT function and its business units slows progress in a firms’ digital objectives. MedTechs that are slow to develop digital capabilities that address patient needs and integrate these into their business models risk not being a party to decisions shaping the emerging healthcare ecosystem.
 
The increasing significance of scarce AI talent

Digital therapeutics predicated upon AI techniques, which are growing in significance with healthcare systems, require large amounts of data collected from electronic health records (EHR), medical images, and information from patients’ wearables. Key areas where AI techniques can improve the delivery of care include: (i) diagnoses, (ii) managing patient journeys, and (iii) improving patient engagement. Streamlining these three areas can ease administrative burdens on healthcare systems, optimize physicians’ time, improve patient outcomes, and lower costs. However, a significant challenge for MedTechs is the scarcity of essential capabilities to develop digital strategies. A 2020 research report by Deloitte Insights suggested that there are significant shortages of “AI developers and engineers, AI researchers, and data scientists”. Corporate leaders might consider bolstering their chances of attracting digital and AI talent by: (i) leveraging their company’s unique value and purpose, (ii) prioritizing and offering best-in-class training over recruiting, (ii) prioritizing diversity, and (iv) engaging with universities.
 
Transformative MedTech deals
 
The significant shift in MedTech strategies towards patients is demonstrated by two recent transformative deals: Teledoc’s 2020 acquisition of Livongo and Siemens Healthineers AG’s 2021 acquisition of Varian Medical Systems Inc. Both combinations emphasise the significance of digitalization and demonstrate the strategic shift towards patients. 
 
The US telehealth giant Teledoc’s acquisition of Livongo for US$18.5bn was the largest digital healthcare deal in history, which valued the combined company at US$38bn. Livongo, founded in 2014, provides digital therapeutic solutions to improve patient health outcomes for a range of chronic conditions including diabetes, and hypertension. The other transformative MedTech digitalization deal was the German health imaging giant Siemens Healthineers AG’s acquisition of cancer device and software specialist Varian in April 2021 for US$16.4bn. Siemens Healthineers is the leading supplier of medical imaging solutions used to support the planning and delivery of radiotherapy. Varian was the leading supplier of radiotherapy solutions. Both deals were substantially larger than Amazon’s US$0.75bn 2019 acquisition of PillPack, and Google’s US$2.1bn 2021 acquisition of Fitbit, and they signal a new and permanent path for MedTech companies towards a digital-first future.
 
Takeaways

To remain relevant MedTechs will need to continue their symbiotic relationships with surgeons albeit in a modified form, while becoming significantly more patient centric and digitally savvy. However, a bigger challenge Western MedTechs will have to face in the next five years is whether they can develop digital therapeutic solutions for patients fast enough to compete with the looming threat from China’s large and rapidly growing capacity to develop and market medical robotics for surgeons and innovative digital therapeutics for patients. This will be the subject of a forthcoming Commentary.
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2 years ago

Leaning-in on digital and AI

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  • Digital therapeutics and artificial intelligence (AI) techniques are increasing their influence on the medical devices industry and fuelling a shift of healthcare away from hospitals into peoples’ homes
  • This poses a challenge to traditional medical device companies (MedTechs) that solely focus on manufacturing physical devices for hospital-based episodic interventions
  • Some MedTechs are changing their business models and strategies, diverting their focus to patients, and adding digital therapeutic applications to their legacy offerings
  • Zimmer-Biomet and Stryker are MedTechs that have embraced digital therapeutics and AI
  • Stryker’s CEO advises other MedTechs to ‘lean-in on AI and don’t be sceptical’
 
Leaning-in on digital and AI
 
Rapidly growing digital therapeutic technologies are disrupting hospital-based healthcare and posing a challenge to those medical device companies that are slow to complement their legacy physical product offerings with patient centric digital solutions. Such technologies have the potential to enhance patient outcomes, reduce healthcare costs, and give providers access to new revenue streams. Today, digital solutions increasingly contribute to the prevention, management, and treatment of a wide range of diseases and health conditions. Their rapid growth is driven by advances in the behavioural sciences, artificial intelligence (AI) techniques and the increase in the consumer health wearables market, which is converging with the regulated medical devices market. This convergence facilitates care to move away from hospitals and into peoples’ homes.
 
In this Commentary
 
This Commentary describes how two decades ago a world-renowned surgeon and CEO of a large hospital group warned that digital therapeutics would disrupt healthcare and push a lot of hospital-based care to peoples’ homes. For years the medical devices industry did not pay too much attention to such warnings and continued to focus on manufacturing physical products for surgeons in hospitals. The Commentary describes two leading MedTechs - Zimmer-Biomet and Stryker – which have recently begun to reinvent themselves and embrace digital therapeutics and AI techniques expected to improve patient outcomes and reduce surgical inconsistencies. We briefly develop this thought process by suggesting how machine learning AI techniques might be employed to reduce the high failure rates of spinal surgeries. The Commentary describes the large and growing global market for digital therapeutics and prescription digital therapeutics, a large proportion of which are enabled by wearables and telehealth. The market for digital therapeutics is large enough and growing fast enough to pose a threat to traditional medical device companies that solely manufacture physical offerings and fail to develop digital solutions to improve patient journeys. Although some MedTechs neither have the resources nor the mindsets to develop digital solutions, it seems reasonable to suggest that, in the medium term, they will be obliged to acquire or develop such assets to remain competitive. However, achieving this will be challenging.
  
Early warnings of change

Over a decade ago, Devi Shetty, warned health professionals to prepare for care to become heavily influenced by digital therapeutics, which he argued would move a significant portion of care away from hospitals and into peoples’ homes. This warning had resonance because Shetty is a surgeon as well as being the founder and executive director of Narayana Health, one of India’s largest hospital groups. In an interview with HealthPad in 2012 he suggested that hospitals were becoming less relevant in a new, and rapidly growing digitally driven healthcare ecosystem. “Healthcare of the future will be dramatically different to that of the past. The future is not an extension of the past. In the future, chronic illnesses will be treated at home”, said Shetty and continued, “The next big thing in healthcare is not going to be a magic pill, a faster scanner, or a new operation. It’s going to be digital therapeutics, which will dramatically change the way health professionals interact with patients. Every step of a patient’s care journey will be informed by software. This will make healthcare safer for the patient and shift most of hospital activities to the home. If a physician doesn’t have to operate on a patient, the patient can be anywhere, distance doesn’t matter”. Shetty repeated this argument at a 2022 Microsoft ‘Future Ready’ conference suggesting that, “95% of people who are unwell, don’t need an operation. All they need is medical intervention, which can be enabled by digital technology and telehealth and treated in the home”.
 
Leading MedTech companies reinventing themselves
 
Two decades after Shetty’s warning, the CEOs of Zimmer-Biomet and Stryker, respectively Bryan Hanson, and Kevin Lobo, have made substantial commitments to digital therapeutic solutions that improve patient outcomes, reduce surgical inconsistencies and extend treatment and monitoring to the entirety of patients’ journeys, much of which takes place in patients' homes. Medical device companies that fail to develop software solutions or link-up with providers of such technologies could risk losing market share to emerging competitors.
 
Zimmer-Biomet and digital therapeutics

Zimmer is a player in total knee arthroplasties, which involve replacing the knee joint with a prosthetic device that carries out similar functions as a person’s own knee. The surgery has become routine. In 2020, US physicians carried out ~1m total knee arthroplasties, and by 2030, ~2m such procedures are expected to be carried out annually in the US. In 2020, the global total knee replacement market was valued at ~US$7.8bn, expected to grow at a CAGR of >6%, and reach ~US$12.5bn by 2027.

In 2021, Zimmer and Canary Medical, a software company, which had developed an implantable digital therapeutic application, received approval from the US Food and Drug Administration (FDA) to market Persona IQ: the world’s first ‘intelligent’ total knee replacement. Zimmer’s traditional knee prosthesis is embedded with Canary’s technology to provide a range of automatic, reliable, and accurate data and analyses that facilitates remote monitoring and tracking of patients' post-operative progress long after they have left hospital.  Following this success, Hanson is directing a substantial percentage of Zimmer’s R&D spend on the development of digital therapeutic solutions, and Persona IQ is expected to be the first in a pipeline of intelligent joint prostheses.
 
Stryker and digital therapeutics

In a March 2022 interview, Stryker’s CEO, Kevin Lobo, stressed his ongoing commitment to increase his company’s digital therapeutic and AI capabilities. In 2021 Stryker acquired Gauss Surgical, which had developed Triton™, an AI-enabled app for real-time monitoring of blood loss during surgery. “After a mother gives birth”, says Lobo “it’s important to calculate how much blood she’s lost. Today, this quantification is very crude and rudimentary. Triton™ allows you to use your smartphone to accurately measure the amount of blood that is in sponges as well as cannisters. It can distinguish between different liquids and measure only the haemoglobin. This is critical to determining whether a mother needs a transfusion or not. You would be shocked, even here in the US, how often a mother doesn’t get a transfusion she needs or gets one she doesn’t need”.

In January 2022, Stryker acquired Vocera Communications for ~US$3bn. Vocera is a US Nasdaq traded company founded in 2000 that makes wireless communications systems for healthcare and has developed a digital platform, which helps connect caregivers and "disparate data-generating medical devices". The platform is used by >2,300 facilities throughout the world, including ~1,900 hospitals. Interoperability between the platform and >150 clinical and operational systems reduce health risks and enhance the consistency of surgical procedures, speeds up staff response times; and improves patient outcomes, safety, and affordability. According to Lobo, "Vocera will help Stryker significantly accelerate our digital therapeutic aspirations to improve the lives of caregivers and patients".

Lobo has made AI a shared service. Stryker employs ~200 software engineers that are using AI. “This we never had before at Stryker. AI is going to be a central core competence for our company. I can see that all our business units are going to be using AI within the next two to three years”, says Lobo, who expects AI inspired digital therapeutic applications to “lead to more consistent outcomes for our procedures”. According to Lobo this is “a big deal because today there are a lot of variations in surgical outcomes”.

AI and its potential impact on spinal surgery

Spinal surgery is a good example of significant inconsistencies in outcomes. Each year, ~7.6m spinal surgeries are performed globally, and ~1.2m in the US, where spinal fusions account for ~60% of all procedures. Although ~50% of primary spinal surgeries are successful,  ~30%, ~15%, and ~5% of patients only experience a successful outcome after the second, third, and fourth surgeries, respectively. Machine learning AI techniques applied to patients’ electronic medical records (EMR) and clinical data could potentially reduce this high failure rate by predicting what product and surgical procedure could produce an optimal solution for individual patients.
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Robotic surgical spine systems, China, and machine learning
Let us briefly explain. Machine learning, a subfield of AI, is the capability of a machine to imitate intelligent human behaviour. It is the process of using mathematical models of data to help a computer to learn and adapt without following explicit human instructions. Machine learning employs algorithms (a set of instructions for solving a problem) to identify patterns in large data sets, potentially comprised of multiple sources, and then uses these patterns to create a predictive model. With increased training on more data, the results of a machine learning algorithm may become more accurate, much like how humans improve with practice. Once this point is reached, regulatory approval for the algorithm can be applied for under the FDA’s category of “software as a medical device”. Once approved, the algorithm may be used to help reduce the high failure rates of spinal surgery.
 
The digitalization of healthcare
 
MedTech leaders should be mindful of the impact that digital therapeutics is having on their industry, which goes far beyond embedding legacy physical offerings with sensors. Digital therapeutics is a rapidly growing healthcare modality, predicated upon scientific advances in the behavioural sciences and AI techniques, that help individuals to form habits, which improve their health, reduce healthcare costs and boosts productivity. Such software tools increasingly are used for the management and prevention of a range of debilitating and costly chronic conditions, including mental health challenges, substance abuse disorders, opioid-induced conditions, cancer, cardiovascular diseases, metabolic disorders, respiratory conditions, and inflammatory diseases. Chronic disease is a public health emergency. In the US, six in ten citizens are living with at least one chronic disorder. Not only are such conditions the leading cause of hospitalizations, disability, and death, but their total annual cost to the US exchequer, which includes lost economic productivity, is ~US$3.7trn.
 
The market for digital therapeutics is driven by a combination of different factors, including: technological advances, particularly consumer wearables (such as the Apple Watch and Fitbit apps, see below), the high penetration levels of mobile telephony, the growth of telehealth, the increasing demand from consumers to take more control of their health, aging populations, the large and escalating incidence of preventable chronic diseases, the need to control healthcare costs, and rising investments in digital therapeutics. According to Statista, a business data platform, in 2021 the number of people globally using digital therapeutic applications reached ~44m. Almost double the number of 2020. By 2025 the number of users is expected to reach >362m, and this only includes devices that have sought validation in clinical trials. The global digital therapeutics market is growing at a CAGR of ~31% and is projected to reach ~US$13bn by 2026, up from ~US$3.4bn in 2021.
 
An advantage of digital health modalities is their ability to deliver continuous personalized care and bridge large care gaps created by shortages of specialized health professionals. In the US, for instance, there are ~6,500 specialist physicians in full-time clinical practice to treat diabetes (endocrinologists), but there are ~27m Americans living with the condition. Similar health gaps occur in other common disease states. In developing economies, care gaps are even wider. For example, India has a chronic shortage of doctors and nurses and has ~77m people living with diabetes and ~55m people living with cardiovascular disease. The latter kills ~5m Indian citizens each year. India, like many other Asian countries, has chosen to deal with care gaps by establishing itself as a major presence in the digital health economy. By several key metrices, from internet connections to app downloads, both the volume and the growth of India’s digital economy now exceeds those of most other countries. Expect this shift to increasingly influence corporations looking to enter and extend their franchises in large and rapidly growing medical devices markets in developing economies. 
 
Cybersecurity challenges

Headwinds for digital therapeutic applications, particularly in Western democracies, include challenges of informed consent to use, safety and transparency, algorithmic fairness and biases, and data privacy. Digital therapeutic applications tend to be more vulnerable to cyberattacks than traditional medical devices, which are manufactured according to strict protocols by a handful of regulated manufacturing partners. By contrast, digital applications often rely on third-party software, which may be less rigorous than the usual medical device standards. Cybersecurity threats to digital therapeutics include data theft, identity disclosure, illegally accessing data, corruption of data, loss of data, and violation of data protection. These risks are accentuated by the fact that the modality is predicated upon the continuous monitoring of patients’ vital signs and increased connectivity between physicians, providers, payers, and patients and breaches can occur at various points along the path of data movement. Risk mitigation includes encryption protocols and the ability to control data access and data integrity. An indication of how quickly the US policy environment around cybersecurity is changing is in March 2022, the US Senate unanimously passed legislation, which would usher in sweeping changes to the federal legal landscape relating to cybersecurity and mandate companies to report damaging hacks and ransomware payments to the government.
 
Prescription digital therapeutics

Another indication of the growing significance of digital therapeutics is a recent US policy push to establish an equivalence between some wearable healthcare solutions and prescription drugs and medical devices. On 10 March 2022, two US senators, Catherine Cortez Masto, D-Nevada, and Todd Young, R-Indiana, introduced legislation to expand Medicare and Medicaid coverage to include prescription digital therapeutics. Medicare is a federally run US medical insurance programme covering ~64m citizens >65 and younger disabled people. Medicaid is a government assistance programme, funded by both federal and state governments, but run by individual states and covers the medical expenses of ~75m Americans on low incomes and with limited resources. This is significant because of the vast number of individuals covered by these health insurances and the fact that the US regulatory hurdle is one of the toughest in the world. Prescription digital therapeutics fall under the FDA category of “software as a medical device” and are subject to the same stringent requirements as drugs and medical devices, and must demonstrate evidence of clinical effectiveness, safety, and quality. After that they require a prescription for use, following a consultation with a doctor.
 
The bill would standardize US reimbursement codings for prescription digital therapeutics, which is expected to incentivize American doctors to increase prescribing them. This would not only facilitate greater access to a wide range of digital therapies for >44% of Americans receiving state healthcare support but potentially create a precedent for US private health insurance companies to increase their coverage of prescription digital therapeutics. This would significantly help to propel the modality into mainstream healthcare.



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The future of health wearables

In June 2020, as the COVID-19 crisis escalated, the FDA expanded its guidance for non-invasive patient-monitoring technologies, including the Apple Watch’s ECG function. In 2021, ~34m Apple Watches were sold worldwide; up from ~22.5m in 2018. In addition to smartwatches, there is a wide range of intelligent wearables that monitor your vital signs in real time, promote self-management of chronic conditions, help people to engage with their own health and incentivize them to change their behaviour to improve their health and lifestyles. Thus, digital therapeutic applications have the potential, among other things, to slow the development of chronic disorders and reduce hospital visits and readmissions. The size and growth rate of the wearable health technology market influences the decisions of insurers, employers, health providers and producers. For example, insurers use data from wearables to adjust their premiums,  corporates derive benefits from their employees using wearables, which include healthier company cultures, a reduction in employee turnover, an increase in workplace safety and enhanced efficiency.  
In the US, consumers' use of wearables increased from 9% to 33% in four years as of 2021. The use of wearables is likely to increase as they become more conventional, connectivity expands, and more accurate sensors are developed. Such developments are likely to provide further incentives for insurers and employers to use wearables to develop healthier lifestyles to boost profitability and cut costs. According to Gartner, a technological research and consulting firm, in 2021 worldwide user spending on wearable devices was ~ US$82bn, ~18% increase from the previous year. This seems reflective of consumers, encouraged by the COVID-19 pandemic, becoming more conscious about their health, wellbeing, and changes to their lifestyles. According to a 2021 Deloitte’s survey, ~58% of US households own a smartwatch or fitness tracker, and ~39% of Americans personally own a smartwatch or fitness tracker. ~14% of consumers have bought their fitness devices since the start of the COVID pandemic in 2020, and activities such as counting steps, workout performance, heart health, and sleep quality monitoring are amongst the most popular activities.
 
Telehealth

Another factor driving the shift of care away from hospitals to peoples’ homes is the development of telehealth. The COVID-19 pandemic caused telehealth usage to surge as consumers and providers sought ways to safely access and deliver healthcare. According to the US Centers for Disease Control and Prevention (CDC), by late March 2020, telehealth had increased >154% compared to the same period in 2019.  Since the peak of the COVID-19 pandemic, telehealth has become a permanent part in the delivery of healthcare. The telehealth market is expected to rise to >US$397bn by 2027 from US$42bn in 2019. According to Devi Shetty the history of healthcare will be written in two sections, BC, and AC: before COVID and after COVID. “COVID-19 disrupted and transformed healthcare and forced inward looking healthcare professionals to rapidly change and adopt digital therapeutic technologies”, says Shetty.
 
The legacy of the COVID-19 related surge in digital therapeutics is an opportunity to make permanent hybrid care modalities created during the pandemic. The foundations for the opportunity are described in a 2021 McKinsey research report, which suggests that the pandemic, (i) accelerated the growth and acceptance of telehealth, which “stabilized at ~38X higher than before the crisis”, (ii) improved the attitudes of consumers and providers towards telehealth, (iii) made permanent some regulatory changes put in place during the pandemic (for example, Medicare and Medicaid’s expansion of reimbursable telehealth codes introduced in 2021 for US physician fee schedules, which have been made permanent), (iv) fuelled venture capital’s digital health investments, and (v) drove the adoption of digital therapeutics across a wide range of disease states. 
Shift in mindset

In the changing healthcare ecosystem, a primary strategic objective for MedTech leaders is to define relevant planning cycles and efficaciously manage from one cycle to the next. The current planning cycle in the medical devices industry is influenced by data, AI techniques, and patient centric digital therapeutic solutions. To effectively manage this cycle, MedTechs might consider copying Zimmer and Stryker and acquire complementary digital therapeutic assets and capabilities. Adapting M&A knowhow and experience to make such acquisitions is an option but not without risk.
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MedTech must digitize to remain relevant
This is because enterprises with digital assets and capabilities have different cultures, development practices, reimbursement policies and data management policies and practices compared to traditional medical device companies. It seems reasonable to suggest that poorly managed acquisitions could result in MedTechs ending up with a graveyard of unfulfilled digital technologies. To reduce this risk industry leaders might consider following Stryker’s example and recruit experienced digital and AI specialists, and make them a core competence.
 
Takeaways

In the near-term, disruptive digital technologies present both challenges and opportunities for medical device companies. Zimmer and Stryker have started to reinvent themselves through parallel efforts to digitize their legacy businesses, acquire complementary digital assets, and make AI a core competence. However, many MedTechs have not changed their business models and still focus R&D on making small improvements to existing product offerings. Corporate leaders considering changing their business models and strategies should be mindful that digital and AI assets and capabilities with the potential to create disruptive growth need to be protected from unnecessary bureaucratic burdens common in many traditional companies. To survive and prosper, managers might consider rethinking their operating models for innovation-led growth. The most effective models appear to combine a strategic process with multiple mechanisms for driving innovation development and scale-up. Stryker’s shared service of AI expertise is one example of a contrived core “capability” expected to transform legacy devices into growth engines that could help secure the company’s long-term survival. MedTech CEOs might do well to follow Lobo’s advice and, “lean-in on AI and do not be sceptical.”.
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2 years, 7 months ago

A prescription for an AI inspired MedTech industry

HealthPad
Directory:
Medical Technology
Tags:
AI
Artificial Intelligence
commentary
data mining
Medtech

 

  • A wind of change is blowing through MedTech markets
  • MedTech markets have matured and are experiencing slower growth and increased competition, which have fuelled endeavours to increase growth rates
  • Artificial intelligence (AI) techniques applied to data from existing devices have the potential to achieve this and improve care
  • Obstacles to developing AI solutions include rigid manufacturing mindsets and a dearth of appropriate talent
  • To remain relevant MedTech leaders will need to “think beyond physical products”, develop new business models, new types of investments and new approaches to R&D
  • Will a wind of change that is blowing through MedTech markets be perceived as a temporary breeze?
 
A prescription for an AI inspired MedTech industry
 
Thinking beyond physical products and the growing significance of AI in MedTech markets


A wind of change is blowing through MedTech markets, which has prompted some key opinion leaders to think beyond physical products and begin to use artificial intelligence (AI) techniques to develop value added services that bolt-on to their existing physical offerings to improve clinical care and economic efficiencies while providing access to new revenue streams.

Bryan Hanson, Zimmer-Biomet’s CEO, recently suggested that >70% of his company’s R&D spend is now being invested in data informatics and robotics. Not far behind is Stryker, another global orthopaedic corporation, which has implemented AI strategies to improve care and differentiate its offerings. Both are thinking beyond their physical products to create a suite of services derived from AI enhanced data collected from their existing devices. Such actions provide a template that can be copied by other enterprises. How long will it take for AI solutions to represent a significant percentage of MedTechs’ revenues?
 
In this Commentary

This Commentary: (i) describes the growing significance of AI, (ii) explains the difference between data mining, AI, and machine learning, (iii) illustrates AI technologies that have become an accepted part of our everyday lives, (iv) highlights technical drivers of AI solutions, (v) describes obstacles to the development of AI systems, (vi) indicates how such obstacles may be reduced, (vii) describes Zimmer’s and Stryker’s AI driven data initiatives, (viii) suggests that the Zimmer-Stryker AI template has broad potential, (ix) suggests that AI systems can breathe life into 'dead data', (x) provides an example of a company at the intersection of medical information and AI techniques, (xi) describes the origins of the phrase, ‘wind of change’, and defines the ‘winds’ driving change in current MedTech markets, (xii) reports that ~80% of B2B sales in the economy generally are digitally driven, (xiii) provides some reasons for MedTechs’ slow adoption of AI systems, (xiv) floats the idea that the future for producers is to partner with tech savvy start-ups and (xv) describes how US AI supremacy is being challenged.
 
AI: vast and fast growing
 
It is challenging for baby boomers and older millennials, who populate MedTechs’ C suites, to fully grasp the potential of AI. This is largely because their corporate careers were underway before the digital age started, and for three decades they have personally prospered from manufacturing physical devices without the help of AI.
 
A person who understands the potential of AI is Sundar Pichai, the CEO of Alphabet, one of the world’s largest tech companies. In a recent BBC interview Pichai suggested, "AI is the most profound technology that humanity will ever develop and work on . . .  If you think about fire or electricity or the Internet, it's like that, but even more profound". This suggests that Hanson is right to redirect Zimmer’s R&D spend towards AI-driven solutions. A February 2021 report from the International Data Corporation (IDC), a market intelligence firm, suggests that the current global AI market is growing at a compound annual growth rate (CAGR) of ~17% and is projected to reach ~US$554bn by 2024.
 
Data mining, AI, machine learning and neural networks

Among MedTechs’ C suites there is some confusion about data strategies and AI solutions. Many enterprises use data mining techniques on existing large datasets to search for patterns and trends that cannot be found using simple analysis. They employ the outcomes to increase revenues, cut costs, improve customer relationships, reduce risks and more. Although data mining is commonly used when working on AI projects, in of itself, it is not AI. So, let us briefly clarify.

AI is the science and engineering of developing intelligent computer programs to enable machines to provide requested information, supply analysis, or trigger events based on findings. AI creates machines that think, learn, and solve problems better and faster than humans. This is different to traditional computing, where coders provide computers with exact inputs, outputs, and logic. By contrast, AI systems can be “schooled” to carry out specific tasks without being programmed to do so. This is referred to as machine learning, which usually requires large amounts of data to train algorithms [mathematical rules to solve recurrent problems].

A critical element of machine learning’s success is neural networks, which is an AI technique modelled on the human brain that is capable of learning and improving over time. Neural networks are comprised of interconnected algorithms that share data and are trained by triaging those data: a process referred to as ‘back propagation’. In healthcare, machine learning outputs range from the ability to recognise images faster and more accurately than health professionals to making in vivo diagnoses.
 
AI systems have become an accepted part of our everyday lives without us realising it
 
Most people are aware of significant AI breakthroughs such as self-driving cars and IBM’s Watson computer winning the US quiz show Jeopardy by beating two of the best players the show had produced. Lesser known, is in 2012, AlexNet, a neural network learning system, won a large-scale visual recognition contest, which previously was thought too complex for any machine. In 2016, Google’s AlphaGo, a machine learning algorithm, defeated Lee Sedol, who was widely considered the world’s greatest ever player of the ancient Chinese game Go. Most observers believed it would be >10 years before an AI programme would defeat a seasoned Go champion. Although Go’s rules are simple, the game is deceptively complex, significantly more so than chess. It has a staggering 10170 possible moves, which is more than the number of atoms known in the universe. Significantly, machine learning algorithms embedded in AlphaGo, mastered the game without any prior knowledge and without any human input. More recently Google launched AlphaGo Zero, an AI system, which can play random games against itself and learn from it. During the decade of these breakthroughs, AI systems became an accepted part of our everyday lives without us realising it. Examples include, Google searches, GPS navigation, facial recognition, recommendations for products and services, bank loans we receive, insurance premiums we are charged, and chatbots, which organizations use to provide us with information.
 
Technical drivers of AI systems

In addition to commercial drivers, AI techniques are driven by easy availability of data, an explosion in computing power and the increased use of clusters of graphic processing units (GPUs) to train machine-learning systems. These clusters, which are widely available as cloud services over the Internet, facilitate the training of more powerful machine-learning models. An example is Google's Tensor Processing Unit (TPU), which has the capability to carry out more than one hundred thousand trillion floating-point operations per second (100 petaflops). This has the potential to accelerate the rate at which machine-learning models can be trained. Further, the cloud has made data storage and recovery easier, which has motivated government agencies and healthcare institutions to build vast unstructured data sets that they make accessible to researchers throughout the world to stimulate innovation.
 
Obstacles to the development of AI systems
 
So far, we have emphasised the benefits of AI, but there are concerns that machine intelligence will accelerate at an incomprehensible rate, surpass human intelligence, and transform our reality. This is referred to as “singularity”, which has generated concerns from key opinion leaders. Nearly a decade ago, Stephen Hawking, a pre-eminent British scientist, warned in a BBC interview, that singularity “could spell the end of the human race”. More recently, Hawking’s view has been echoed by Elon Musk, founder, and CEO of Tesla and SpaceX, who suggests that AI is, “more dangerous than nuclear warheads and poses a fundamental risk to the existence of human civilization". Musk has called for stronger regulatory oversight of AI, and more responsible research into mitigating its downsides. In 2015, he set up OpenAI, a non-profit research organization, with a mission to promote and develop AI systems that benefit society. 

 

In the June 2018 edition of the Atlantic Review, Henry Kissinger, who served as national security adviser and secretary of state for two US Presidents, described the potential harms from AI by addressing the question: “What would be the impact on history of self-learning machines that acquired knowledge by processes particular to themselves, and applied that knowledge to ends for which there may be no category of human understanding?”. Singularity might be more imminent than once thought. In a book published in 2015, futurist Ray Kurzweil predicted that singularity would occur in ~2045, but a paper published in the June 2020 edition of the International Journal of Astrobiology suggests that it is more likely to occur within the next decade.

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Robotic surgical spine systems, China, and machine learning
Overcoming obstacles to AI
 
In clinical settings there are growing concerns that complex algorithms can blur the reasoning behind specific machine interpretations and consequent actions of robotic surgical systems. As AI and machine learning develop so surgical robots are expected to become more autonomous and have the capability to make instantaneous diagnoses and pursue immediate therapies, which surgeons using the systems do not fully understand. The failure of humans to understand the workings of an AI system is referred to as an “interpretability challenge”, or more commonly, the “black-box” problem, which could impact future clinical regulations.
 
Combatting the possible dangers of AI systems not being understood by humans is a relatively new and growing research area, referred to as “Explainable AI” (XAI). XAI attempts to use AI techniques to develop solutions that can describe the intent, reasoning, and decision-making processes of complex AI systems in a manner that humans can understand. This could provide Stryker and Zimmer, and other manufacturers, a solution to potential future regulatory obstacles associated with advances in their robotic surgical systems
.
Zimmer’s and Stryker’s initiatives

In August 2021, the FDA granted De Novo marketing authorization [applicable for a new and novel device whose type has not previously been classified] for a “smart knee”, which Zimmer had developed in partnership with Canary Medical, a data analytics company. The device, called Persona IQ®, is the world's first and only smart knee cleared by the FDA for total knee replacement surgery. It combines Zimmer’s proven and trusted knee implant, Persona® The Personalized Knee®, with Canary’s proprietary sensor technology, which provides real-time feedback on how surgical implants and devices are working by generating self-reports on patient activity, recovery, and treatment failures, without the need for physician intervention and dependence upon patient compliance. The partnership is also expected to leverage Canary’s machine learning capabilities to identify further patterns in data from implants that could help clinicians catch problems, such as infections or loosening of the implants before they worsen. Persona IQ® will work together with Zimmer’s remote care management platform, mymobility® with Apple Watch®, as well as with other components of the  ZBEdge™ connected intelligence suite of currently available, and soon to be launched, digital and robotic technologies engineered to deliver transformative data-powered clinical insights, shared seamlessly across the patient journey, to improve patient outcomes. 

In January 2021, Stryker acquired OrthoSensor, a privately held technology company that makes intraoperative sensors for use in total joint replacements. Stryker expects these sensors to empower surgeons with AI-driven solutions and enhance its surgical robotic systems by eventually providing them with the capability to predict surgical outcomes. Additionally, OrthoSensor’s remote patient monitoring wearables, combined with a cloud-based data platform, are expected to significantly improve Stryker’s data analytics capabilities. According to a Stryker press release issued at the time of the acquisition, “OrthoSensor quantifies orthopaedics through intelligent devices and data services that allow surgeons and hospitals to deliver evidence-based treatments for all healthcare stakeholders. The company’s advancements in sensor technology, coupled with expanded data analytics and increasing computational power, will strengthen the foundation of Stryker’s digital ecosystem”.
 
The Zimmer-Stryker AI template has potential across MedTech

Despite Zimmer’s and Stryker’s AI-driven data initiatives to improve their respective competitive advantages and gain access to new revenue streams, few MedTechs collect, and store the data produced by their existing devices, and even fewer use such data to provide novel AI solutions. The Zimmer-Stryker template for achieving this is not limited to orthopaedics. For example, consider neuro critical care and traumatic brain injuries (TBI), which are a “silent epidemic”. Each year, globally ~69m individuals sustain TBIs. In the US, every 15 seconds, someone suffers a TBI. In England, ~1.4m people present at A&E departments each year following a head injury.

Despite extensive research, successful drug therapies for TBI have proven to be elusive. The gold standard management of the condition is to monitor intracranial pressure (ICP) and attempt to avoid elevated levels, which can cause further insults to an already damaged brain. Currently, there are no FDA approved means to identify advance warnings of changes in ICP. However, it might be possible to create an early warning of ICP crises by applying machine learning algorithms to standard physiological data produced by existing medical devices commonly used to monitor patients with TBI. This would not only provide time for interventions to prevent further trauma to critically ill patients but would also give producers access to new revenue streams.



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Breathing life into dead data

There are potentially limitless opportunities to improve care by breathing life into 'dead data'. This can be achieved simply by applying AI solutions to underutilized data from existing medical devices. The global MedTech industry is comprised of ~6,000 companies (mostly small to medium size). The overwhelming majority of these manufacture devices that produce, or could produce, patient data. These companies serve ~14 surgical specialisms each of which treat numerous conditions. For each condition there are millions of patients at any one time. For each patient, multiple devices used in therapies display real time data. Most producers are awash with dead data because they do not collect, store, and analyse these data to improve the quality of care. AI systems can change this.
A MedTech start-up at the intersection of medical information and AI techniques

A start-up, which understands the clinical and economic potential from the intersection of medical data and AI solutions is Komodo Health, which was founded in 2014. According to Web Sun, the company’s co-founder, and president, “We had a vision that integrating robust data with software solutions was the way forward for healthcare at a time when no one was doing this”. Komodo has created an AI platform, which it refers to as a "healthcare map", comprised of large-scale anonymous health outcome data from hundreds of sources.

In January 2020, Komodo announced a deal to import Blue Health Intelligence’s patient data onto its platform. Blue Health provides US healthcare claims data and actionable analytics to payers, employers, brokers, and healthcare services. The combined database charts >325m individual patient care journeys through tests and therapies at hospitals and clinics. In March 2021, Komodo raised US$220m to extend its platform to offer real-time assessments of patients’ healthcare journeys to detect disparities in the quality of care and outcomes, and to provide a basis for interventions aimed at improving outcomes and lowering costs.

The ability to introduce clinical insights into enterprise workflows potentially helps producers and providers close gaps in care journeys and address unmet patient needs. Not only are Komodo’s services designed to deliver timely interventions and alerts to improve care, but the company also records and reports the performance of specific medical products on patient cohorts. These data provide a basis to develop and market further innovative healthcare services, and novel therapeutics, which are expected to boost Komodo’s revenues.
 
A wind of change

We borrowed the ‘wind of change’ phrase used in our introduction from a famous speech made by British Prime Minister Harold Macmillan to the Parliament of South Africa on 3 February 1960 in Cape Town. Macmillan was referring to a system of institutionalised racial segregation, called Apartheid, which enforced racial discrimination against non-Whites, mainly predicated on skin colour and facial features. Despite the UK Prime Minister’s belief that in 1960, the days of White supremacy in South Africa were numbered, it took >30 years before Apartheid was ended and Nelson Mandela was inaugurated as the first Black President of South Africa on 10 May 1994. Mandela was an anti-apartheid activist and lawyer, who had spent 27 years as a political prisoner under the Apartheid regime.

A wind of change is now blowing through MedTech markets. In less than a decade, healthcare will be faced with significantly more patients, more data, more technology, more costs, more competition, and less money for producers and providers. Over the past five years, US providers’ profit margins have fallen, in Europe the gap between public health expenditure and government budgets has increased, and throughout the world healthcare systems are under budget pressure and actively managing their costs. With such strong headwinds, a sustainable future for MedTechs might be to reduce their emphasis on manufactured products distributed through labour intensive sales channels and increase their AI service offerings using data from their existing devices. Over the past five years AI solutions have become more prolific, easier to deploy, and increasingly sophisticated at doing what health professionals do, but more efficiently, more quickly and at a lower cost.  
 
~80% of B2B sales are digital

In addition to AI solutions being used to improve clinical outcomes, they can be employed to enhance business efficiencies. A previous Commentary described how AI systems can help to transform traditional labour intensive MedTech supply chains and personalise sales. A recent study undertaken by Gartner, a global research and advisory firm, suggests that, “Over the next five years, an exponential rise in digital interactions between buyers and suppliers will break traditional sales models, and by 2025, ~80% of B2B sales will occur in digital channels”. Giant tech companies are taking advantage of this to enter healthcare markets, MedTechs have been slow to implement such changes despite the boost in online engagements provided by the COVID-19 pandemic.
Reasons for slow adoption of AI systems

So, why are MedTechs slow to implement AI solutions to enhance clinical outcomes and improve economic efficiencies? Over ~3 decades they have achieved double-digit revenue growth from manufacturing physical devices and marketing them through labour intensive channels in a few wealthy regions of the world with relatively benign reimbursement policies. During this period of rapid growth and commercial success, MedTechs have not been required to confront data issues, bridge the science, technology, engineering, and mathematics (STEM) skills gap, and commit to new structures, new processes, new behaviours, and new aptitudes.
This suggests that despite a wind of change, now blowing through MedTech markets and challenging traditional business models and strategies, it could be perceived as a 'temporary breeze' and nothing will change. However, a step change in the direction of more AI solutions might occur when digital natives [people who have grown up in a digital age] replace digital immigrants [people whose careers were well underway before the onset of the digital age] in MedTechs’ C suites. According to a Gartner executive, “As baby boomers retire and millennials mature into key decision-making positions, a digital-first buying posture will become the norm. . . . . . Sales reps will need to embrace new tools and channels, as well as a new manner of engaging customers, matching their sales activity to their customers’ buying practices and information collecting needs”. A 2019 research report from the Boston Consulting Group (BCG), suggests that companies, which use AI systems to personalise sales can expect productivity gains of ~10%, and incremental revenue growth of ~10%.
 
Partnering with tech savvy start ups

Currently, many MedTechs neither have the mindsets nor the in-house STEM capabilities to create AI enhanced services. So, what might be a way forward? STEM skills, although scarce, tend to reside in people <30. Although there are ~68m of these people in the US, people with STEM skills tend to prefer to work either for giant tech companies or tech start-ups devoted to leveraging the potential of AI. Giant tech companies and start-ups are outside the comfort zones of most MedTechs. However, in the future, they may be obliged to partner with tech savvy start-ups engaged in developing AI driven solutions. Such collaboration will be challenging because it requires MedTechs to change their business models, create new ways of making strategic investments, and develop novel approaches to R&D that encompass a broader spectrum of partners.

Most of MedTechs’ R&D investment is consumed by incremental innovations to their current suite of devices. This tends to reinforce existing revenues rather than develop disruptive technologies aimed at capturing new revenue streams. Such strategies are efficacious in stable, fast growing economic environments, but lose their edge in slower markets. It seems reasonable to assume that, as market conditions tighten, MedTechs will need to consider shifting their R&D strategies towards the development of more disruptive technologies. We see this already in Stryker’s R&D investment in robotic surgical systems and Zimmer’s proposed R&D spend on AI, data informatics and robotics.

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Can Western companies engage with and benefit from China?
US supremacy challenged  

US tech giants are investing heavily in AI R&D and driving the adoption of advanced technologies in healthcare. Although these companies have made, and will continue to make, a significant contribution to the field, it would be a mistake to think that they have AI healthcare markets sewn up.
 
Three Chinese tech giants, collectively referred to as ‘BAT’, are also investing heavily in AI systems. All three offer services well beyond their core products and have far-reaching global ambitions. BAT is comprised of Baidu, China’s largest search provider, Alibaba the nation’s biggest eCommerce platform and Tencent, which runs WeChat that has access to >1bn users on its platform. For the past five years BAT has been expanding into other Asian countries, recruiting US talent, investing in US AI start-ups, and forming global partnerships to advance their AI ambitions.
In addition to these private endeavours, China has made AI a national project. Since 2017, Beijing has been pursuing a three-step New Generation AI Development Plan, which aims to turn AI into a core national industry. To this end, China is vigorously carrying out research on brain science, brain computing, quantum information and quantum computing, intelligent manufacturing, robotics, and big data. Already, China has become a world leader in AI publications and patents. The nation’s global share of AI research papers increased from 1,086 (4.26%) in 1997 to 37,343 (27.68%) in 2017, surpassing any other country, including the US. Most AI patents are registered by companies in the US and Japan. However, when it comes to AI patents registered by research institutes, China is the undisputed leader. According to a 2021 report on China's AI development,  ~390,000 AI patent applications were filed in China over the past decade, accounting for ~75% of the world total. Beijing’s competitive advantage in big data and AI strategies is driven by a combination of its weak privacy laws, a national plan, huge government investments, concerted data-gathering, and big data analytics by the BAT tech giants and others. Currently, China’s AI market is valued at ~US$22bn, and by 2030, the nation is expected to become a leader in AI-empowered healthcare businesses and the world’s leading AI power.

Beijing’s policies have given rise to hundreds of AI driven start-ups aimed at gaining access to new revenue streams in China’s rapidly growing healthcare market. Western MedTechs might consider accepting Beijing’s  Made in China 2025 policy, partner with these  tech savvy start-ups and jointly benefit from the nation’s current 5-year economic plan aimed at a “healthier China”.
 
Takeaways
 
We have presented an AI-driven prescription for MedTechs to enhance the quality of care while providing access to new revenue streams. We suggest that this can be achieved by bolting on AI solutions to existing devices, and over time through partnerships with tech savvy start-ups. But ~30 years of double-digit growth derived from manufacturing physical products and distributing them through labour intensive sales channels might have cemented mindsets among C suite incumbents that find it challenging to think beyond physical product offerings. This could suggest that the wind of change, now blowing through MedTech markets, will be perceived as a temporary breeze that does not require thinking beyond physical products, and AI solutions will be a long time coming.
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