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2 months, 3 weeks ago

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

HealthPad
Directory:
Medical Technology
Tags:
Artificial Intelligence
commentary
Machine Learning
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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Healthcare 2040


 
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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Redefining Leadership In The Evolving Landscape Of MedTech
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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4 months, 2 weeks ago

Healthcare 2040

HealthPad
Directory:
Medical Technology
Tags:
AI
commentary
genomics
Internet of Things
Machine Learning
Medical Technology
robotics
telemedicine
  • Since 2000 healthcare has been transformed by genomics, AI, the internet, robotics, and data-driven solutions
  • Traditional providers, anchored in outdated technologies, struggle to keep pace with the evolving healthcare landscape
  • Over the next two decades anticipate another seismic shift, bringing further disruptions to medical technology and healthcare delivery
  • In the face of this imminent transformation, risk-averse leaders may cling to outdated portfolios, showing little interest in adapting to a 2040 healthcare ecosystem
  • Providers must decide; embrace change now and thrive in a transformed healthcare landscape, or stick to the status quo and risk losing value and competitiveness
 
Healthcare 2040
 
Abstract

By 2040, the landscape of healthcare will have undergone a seismic shift, discarding antiquated models in favour of cutting-edge AI-genomic-data-driven approaches that will radically change both medical technology and healthcare delivery. This transformation signifies a departure from the conventional one-size-fits-all system, ushering in an era of targeted therapies grounded in molecular-level insights that challenge entrenched healthcare paradigms. The evolving healthcare narrative emphasises prevention, wellbeing, personalised care, and heightened accessibility. This departure from the norm is not a trend but a significant reconfiguration, where the fusion of biomedical science, technology, and expansive datasets merge to facilitate early detection and proactive interventions. This not only deepens our comprehension of diseases but also elevates the efficacy of therapies. At the core of this transformation is the empowerment of individuals within a framework that champions choice and fosters virtual communities. Genetic advancements, far from just addressing hereditary conditions, play an important role in enhancing diagnostic accuracy, optimising patient outcomes, and fundamentally shifting the focus from reactive diagnosis and treatment to a proactive commitment to prevention and holistic wellbeing. The indispensable roles played by genomics and AI-driven care in reshaping healthcare are not isolated occurrences; they will catalyse the emergence of new data-intensive R&D enterprises, which are poised to redefine the healthcare landscape against a backdrop of multifaceted influencing factors. Successfully navigating this transformative period necessitates a distinct set of capabilities and strategic alignment with an envisioned 2040 healthcare environment.

Providers find themselves at a crossroads, confronted with a choice: adapt and thrive or risk losing value and competitiveness in a rapidly evolving landscape. Recognising potential resistance to change and the scarcity of pertinent capabilities, leaders of traditional enterprises must acknowledge that immediate strategic action is not just beneficial but a prerequisite for success in the redefined healthcare ecosystem of 2040. The urgency of this call to action cannot be overstated, as the window of opportunity for adaptation narrows with each passing moment.
 
In this Commentary

This Commentary aims to help healthcare professionals to strategically reposition their organizations for success in the next two decades. Leaders must evaluate their strengths and weaknesses in the context of an envisioned future and implement strategies to align their organisations with the demands of a rapidly changing health ecosystem. Failure to do so will dent enterprises’ competitiveness and threaten their survival. Leaders should anticipate and address resistance to change among executives with a preference for the status quo. The Commentary has two sections: Part 1, Looking Back 20 Years, describes the scale and pace of change since 2000 and emphasises how genomics, the internet, AI, digitalization, data-driven solutions, robotics, telehealth, outpatient services, personalised care, ubiquitous communications, and strategic responses to demographic shifts have transformed medical technology and healthcare delivery. Part 2, Looking Forward 20 Years, seeks to stimulate discussions about the future of healthcare. While we highlight a range of factors positioned to impact medical technology and healthcare deliver in the future, we emphasise the significance of genomics, varied and vast datasets, and AI. We suggest the emergence of specialised agile, AI-driven research boutiques with capabilities to leverage untapped genomic, personal, and medical data. The proliferation of such entities will oblige traditional healthcare enterprises to reduce their R&D activities and concentrate on manufacturing. Over the next 20 years, anticipate an accelerated shift towards patient-centric, cell-based prevention and wellbeing care modalities, large hospitals replaced with smaller hubs of medical excellence, the rapid growth of outpatient centres, and the acceleration of home care and care-enabled virtual communities. The future dynamic healthcare ecosystem necessitates stakeholders to change immediately if they are to survive and prosper. Takeaways posit a choice for healthcare leaders: either stick to the status quo and risk losing value and competitiveness or embrace change and stay relevant.
 
Part 1
 
Looking Back 20 Years

Reflecting on the past two decades shows the rapid evolution and interplay of factors shaping medical technology and healthcare delivery. Appreciating the speed and scale of change helps to envision the future. Factors such as genomics, the Internet, AI, robotics, digitalisation, data-driven health solutions, telehealth, outpatient services, home care, personalised wellbeing, ubiquitous personal telephony, and strategic responses to demographic shifts have all influenced medical technology and healthcare delivery and will continue to do so in the future. Here we describe a few of these factors.

The completion of the Human Genome Project in 2003 was a pivotal moment in the direction of medical advancement, laying the foundations for the emergence of genomics. Genomics, encapsulating the mapping, sequencing, and analysis of DNA, is a pivotal tool for unravelling molecular information, variations, and their implications in both traits and diseases. This achievement not only transformed biomedical research but also changed healthcare, shifting it from a generic one-size-fits-all approach to finely tuned care tailored to the unique genetic makeup of individuals.

Over the past two decades, the decoding of the human genetic blueprint has provided unprecedented insights into diseases at the molecular level, triggering a paradigm shift in medicine. This ushered in an era of personalised and precision approaches to diagnoses, treatments, and prevention. From the advent of targeted therapies to the implementation of genetic screening, genomic research has had a transformative influence and is positioned to continue its impact on healthcare.

Indeed, genomic testing has become a standard practice, and US Food and Drug Administration (FDA)-approved genomic care modalities have advanced medicine. For example, pharmacogenonics tailors drug treatments to individual patients by utilising genetic information, with FDA-approved tests for specific biomarkers that predict medication responses. Hereditary assessments evaluate an individual's cancer risk based on genetic makeup, such as identifying BRCA gene mutations linked to elevated risks of breast and ovarian cancers. Gene expression profiling analyses a patient's tumour genetics to guide targeted cancer therapies, with FDA-approved companion diagnostic tests for specific cancer treatments. Carrier testing identifies genetic mutations that could be passed on to children, which contribute to family planning and prenatal care. Pharmacodiagnostic tests help pinpoint patients that would benefit from specific drug treatments, predicting responses, especially in cancer therapies.

In 2012, the UK government inaugurated Genomics England, an initiative designed to spearhead the 100,000 Genomes Project, which aimed to sequence the genomes of 100,000 patients with infectious diseases and specific cancers. The project’s goals included the enhancement of our understanding of various genetic factors in diseases, the facilitation of targeted treatments and establishing a framework for the integration of genomics into everyday clinical practice. The successful completion of the project in 2018, provided a basis for genomic medicine and a deeper understanding of the genetic framework influencing health and disease.

In addition to genomic data, since 2000, there has been a significant increase in health-related data, driven by the proliferation of electronic health records (EHRs), developments in information management technologies, initiatives to improve healthcare efficiency, and enhanced communications among stakeholders. The growth in data has, in turn, created opportunities for the utilisation of AI and machine learning (ML) algorithms. Over the last two decades, AI has changed medical technology and healthcare delivery by enhancing diagnostics, personalising treatment plans, streamlining administrative tasks, and facilitating research through efficient data analysis, which has improved patient outcomes, and advanced the field. As of January 2023, the FDA has approved >520 AI and ML algorithms for medical use, which are primarily related to the analysis of medical images and videos. Indeed, the rise of algorithms has transformed healthcare, with many of them focusing on predictions using EHRs that do not require FDA approval.

In addition to EHRs there has been the evolution of wearable technologies like the Apple Watch and Fitbit, which have transformed personal health. Initially focusing on fitness tracking, these devices have expanded to monitor an array of health metrics. Over the years, they have amassed vast amounts of personalised data, ranging from activity levels to heart rate patterns. These data reservoirs are a goldmine for healthcare and wellbeing strategies, enabling individuals, healthcare professionals and providers to gain unprecedented insights into health trends, customised care routines, and the early detection of health issues. This combination of technology and health data has created opportunities for proactive healthcare management and personalised wellbeing interventions.

Targeted medicine not only benefitted from AI but also from personalised telephony, which experienced a significant boost in the early 2000s by the widespread internet access in households across the globe. The period was marked by the introduction of the iPad in 2001, closely followed by the launch of the iPhone. These innovations triggered widespread smartphone use and accessible internet connectivity, laying the foundations for the emergence of telehealth and telemedicine. In the early 2000s, global cell phone subscriptions numbered ~740m. Today, the figure is >8bn, surpassing the world's population. This increase was driven by the proliferation of broadband, the evolution of mobile technologies and the rise of social media, all contributing to the ubiquitous presence of the internet. By the 2010s, the internet had integrated into the daily lives of a substantial portion of the global population. Initially, in 2000, ~7% of the world’s population had access online. Contrastingly, today, >50% enjoy internet connectivity. In a similar vein, broadband access in American homes has surged from ~50% in 2000 to >90% in the present day. Personal telephony has evolved into an omnipresent force, and has become an integral part of billions of lives, actively enhancing health and wellbeing on a global scale. After 2010, patient-centric wellbeing evolved and later was helped by Covid-19 pandemic lockdowns, with telehealth and telemedicine offering remote consultations and treatments, empowering patients, and emphasising shared decision-making between healthcare providers and patients.

On a more prosaic level, consider how robotics has changed surgery over the past two decades by offering enhanced precision, reduced invasiveness, and improved recovery times. The use of robotic systems, like the da Vinci Surgical System, which gained FDA-approval in 2000, has allowed surgeons to perform complex procedures with greater accuracy. Between 2012 and 2022, the percentage of surgical procedures using robotic systems rose from 1.8% to 17%. Robotic surgery is becoming increasingly popular, with an annual growth rate of ~15%. In 2020, its global volume was 1.24m, with the US accounting for >70% of all robotic surgeries.

The shifting demographics over the past few decades, marked by decreasing birth rates, prolonged life expectancy, and immigration, has transformed prosperous industrial economies, resulting in a substantial rise in the proportion of the elderly population. For instance, in the US in 2000, there were ~35m citizens ≥65; today, this figure has risen to ~56m, ~17% of the population. Concurrently, there has been an increase of chronic lifetime illnesses such as heart disease, diabetes, cancer, and respiratory disorders. In 2000, ~125m Americans suffered from at least one chronic condition. Today, this figure has increased to ~133m - ~50% of the population. Simultaneously, there is a shrinking pool of health professionals. Research suggests that by 2030, there will be ~5m fewer physicians than society will require. This, together with ageing populations, the growing burden of chronic diseases and rising costs of healthcare globally are challenging governments, payers, regulators, and providers to innovate and transform medical technology and healthcare delivery.
 
Part 2
 
Looking Forward 20 Years

This section aims to encourage healthcare professionals to envision the future. Over the next two decades, medical technology and healthcare delivery are likely to be affected by numerous interconnected factors, which include: (i) continued progress in AI and ML, internet of things (IoT), robotics, nanotechnology, and biotechnology, (ii) advances in genomics, (iii) increasing availability of multi-modal data (genomics, economic, demographic, clinical and phenotypic) coupled with technology innovations, (iv) accelerated adoption of telemedicine and virtual monitoring technologies, (v) changes in healthcare regulations, (vi) an increase of patient-cantered care and greater patient involvement in decision-making, (vii) emerging infectious diseases, antimicrobial resistance, and other global health issues, (viii) Investments in healthcare infrastructure, both physical and digital, (ix) an evolving and shrinking healthcare workforce, including the further integration of AI technologies and changes in roles, (x) economic conditions and healthcare funding, (xi) the ethical use of technology, privacy concerns, and societal attitudes towards healthcare innovations, and (xii) environmental changes and their impact on health and wellbeing. Such factors and their interconnectivity are expected to drive significant healthcare transformation over the next two decades. Healthcare systems throughout the world are tasked with: (i) improving population health, (ii) enhancing patients’ therapeutic journeys and outcomes, (iii) strengthening caregivers’ experience and (iv) reducing the rising cost of care. There appears to be unanimous agreement among healthcare leaders that these goals will not be achieved by business as usual.
 
In November 2023, BTIG, a leading global financial services firm, organised its Digital Health Forum, bringing together >30 healthcare companies that offer a diverse range of products and services. During the event, executives discussed business models, reimbursement, and commercial strategies, and unanimously agreed that: "The market is primed for the mainstream integration of digital diagnostics and therapeutics."  Here we focus on the anticipated accelerated convergence of genomics and AI technologies, and foresee the emergence of agile, AI-driven R&D boutiques as key players in reshaping medical technology and healthcare delivery.
 
These dynamic research entities thrive on the power of data. Currently, ~79% of the hospital data generated annually goes untapped, and medical information is doubling every 73 days. This emphasises the vast latent potential within these repositories. Traditional enterprises and healthcare professionals, constrained by a dearth of data management capabilities, have struggled to unlock the full potential inherent in these vast stores of information. By contrast, the adept data processing capabilities of these new innovative enterprises position them strategically to harness untapped data sources, extracting valuable insights into disease states and refining treatment modalities. Moreover, they boast advanced technology stacks, seamless connections between semiconductors, software, and systems, and are well-prepared to leverage specialised generative AI applications as they emerge in the market. Armed with cutting-edge technology and extensive datasets, they stand ready to enhance diagnostic precision, streamline treatment approaches, and reduce overall healthcare costs. Private equity firms will be eager to invest in these disruptive AI start-ups, anticipating M&A activities focused on specific therapeutic areas that will make them appealing to public markets.

These innovative entities are set to expedite the introduction of disruptive solutions, improve patients' therapeutic journeys, and optimise outcomes while driving operational efficiencies. Anticipate them to overshadow their traditional counterparts, many of which have outdated legacy offerings and historically have treated R&D as small adjustments to existing portfolios. Given that many conventional healthcare enterprises have: (i) failed to keep pace with technological developments, (ii) a dearth of in-house data-handling capabilities, and (iii) no experience in data-heavy disruptive R&D, it seems reasonable to suggest that they will most likely retreat into their core manufacturing activities, relinquish their R&D roles and lose value.

In the forefront of seismic change, the integration of digitalisation, AI, and cutting-edge decision support tools propels the emerging agile, data-driven R&D enterprises into a pivotal role within the landscape of well-informed, personalised healthcare. Meticulously safeguarding sensitive information, these enterprises not only adhere to the highest standards of privacy but also elevate security measures through state-of-the-art encryption techniques and decentralised storage solutions. As staunch guardians of privacy, they go beyond conventional approaches, crafting data repositories that not only shield confidential information but also facilitate the seamless flow of critical insights crucial for advancing medical technology and elevating care delivery. The seamless synergy between vast genomic, economic, demographic, clinical, and phenotypic data repositories and advanced AI techniques is poised to radically change healthcare R&D, redirecting it away from refining traditional products towards disruptive endeavours. Moreover, these agile research entities are anticipated to encourage widespread industry cooperation, harnessing the power of diverse data sources to innovate health solutions and services that transcend boundaries, thereby playing an important role in shaping a borderless health and wellbeing ecosystem.

In the regulatory arena, a transformation is anticipated by 2040. Regulators are likely to evolve from enforcers to stewards of progress, collaborating with industry stakeholders to promote a consumer-centric healthcare. Advocating transparency, patients' rights, and ethical innovation, regulators will become influential drivers of progress, contributing to a shared and equitable healthcare future. This collaborative effort is expected to contribute to a data-driven healthcare ecosystem that prioritises individual wellbeing, innovation, and accessibility in equal measure.

By 2040, expect healthcare payers to have undergone a transformative change, fuelled by a seismic shift in medical technology and healthcare delivery. New payment models will prioritise individualised therapies and patient outcomes, leveraging real-time health data for customised coverage. AI will streamline administration, reduce costs, and enhance overall healthcare efficiency. Increased patient engagement and collaboration among payers, providers, and patients will drive a holistic, patient-centred approach, ultimately improving the quality and accessibility of healthcare services.

This section has emphasised the transformative forces of genomics and AI shaping a personalised healthcare ecosystem. While traditional medical technology and healthcare delivery may be predicated upon physical devices and a one-size-fits-all approach, the future lies in the fusion of data and smart software to accelerate targeted care, which marks a significant departure from the conventional.
 
Takeaways

The shift towards genomic-driven healthcare marks a transformation in the medical landscape expected by 2040. Moving away from outdated models, the trend towards personalised care, rooted in molecular insights, necessitates a revaluation from health professionals. This shift, facilitated by the fusion of biomedical science, advanced technologies, and vast amounts of varied data, foresees a future where prevention, individualised wellbeing, and improved accessibility become the new norm. The convergence of genomics and AI not only improves diagnostics and treatments but also points to prevention and overall wellness. This Commentary has highlighted the transformative impact of genomics and AI-driven healthcare at the cellular level, making way for data-intensive R&D enterprises that will shape the future of medical technology and healthcare delivery. The path to 2040 demands a departure from conventional norms of the past, requiring strategic realignment and specific capabilities. Traditional providers find themselves at a juncture: those that adapt to an envisioned care environment of 2040 are more likely to succeed, while those that resist risk becoming obsolete. By acknowledging potential obstacles to change and the scarcity of relevant capabilities, leaders are encouraged to recognise the urgency of strategic action as a prerequisite for success in the redefined healthcare landscape of 2040. The future is imminent, and the time for transformative readiness is now.
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5 months ago

MedTechs Battle with AI for Sustainable Growth and Enhanced Value

HealthPad
Directory:
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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Unleashing MedTech's Competitive Edge through Transformational Technologies
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 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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1 year, 1 month 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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