Why "Disrupting Healthcare" Isn't the Answer

Written By Dr. Carl Preiksaitis

In 1920s America, venture capitalists and industrialists declared they would revolutionize farming. "Agriculture is ripe for disruption," they proclaimed, as they deployed tractors and industrial techniques across the Great Plains. Traditional farming methods were dismissed as inefficient, old-fashioned, wasteful. Within a decade, this hubris turned 100 million acres of fertile farmland into the devastating Dust Bowl—a catastrophe born from ignoring generations of agricultural wisdom in favor of "efficient" modernization.

Today, we're watching a similar story unfold in healthcare.

"Healthcare is a colossal, untapped market ripe for technological disruption," declare venture capitalists, as they pour billions into startups promising to revolutionize medicine. Silicon Valley's finest minds are convinced they can fix healthcare the same way they transformed retail, transportation, and entertainment. Their battle cry? Move fast, break things, and let algorithms solve what humans couldn't.

But healthcare isn't like other industries. Just as those 1920s industrialists learned the hard way that soil isn't merely a substrate for plants, today's tech disruptors are discovering that healthcare isn't simply a series of transactions waiting to be optimized.

The Disruption Fantasy

The promise is seductive: Imagine healthcare that works as smoothly as ordering an Uber or shopping on Amazon. No more waiting rooms, instant access to doctors, AI-powered diagnoses, and dramatically lower costs. Given healthcare's very real problems—millions lacking access to basic care, byzantine billing practices, and fragmented delivery systems—this tech-enabled utopia sounds like exactly what we need.

But reality tells a different story:

  • Amazon shut down Haven, its ambitious healthcare venture with JPMorgan and Berkshire Hathaway

  • Google Health has repeatedly restructured after failing to gain traction

  • IBM Watson Health, despite $4 billion in investment, was sold off "for parts" after failing to deliver reliable cancer care recommendations

  • Countless healthcare AI startups have quietly disappeared after their algorithms proved unreliable in real-world settings

Why do these smart, well-funded companies keep failing? And more importantly, why do investors and entrepreneurs keep trying the same approach, expecting different results?

Why Healthcare Disruption Fails

The pattern of high-profile healthcare failures reveals three fundamental problems with the disruption mindset:

1. Oversimplifying Complex Medical Decisions

While Uber can reduce ride-sharing to an algorithm matching drivers with passengers, healthcare resists such simplification. Each patient presents a unique constellation of symptoms, circumstances, and needs that can't be reduced to dropdown menus or decision trees.

Take AI-powered triage systems, for example. While they promise efficient patient routing, they can fail catastrophically with complex or atypical presentations. A patient showing subtle signs of an impending heart attack might be classified as having minor chest pain and directed to urgent care instead of emergency services—a potentially fatal oversimplification.

2. Dangerous Fragmentation of Care

Many healthcare startups cherry-pick profitable, straightforward services while ignoring the complex web of care coordination essential to patient safety. Companies like Hims and Roman offer quick prescriptions for specific conditions, bypassing traditional primary care. While seemingly efficient, this creates dangerous gaps: a patient receiving testosterone replacement therapy through a direct-to-consumer service might miss crucial screening for underlying conditions that their regular doctor would have caught.

3. Ignoring Physical Realities

Digital-only healthcare startups often treat healthcare's physical requirements as mere technical obstacles to be engineered away. But virtual visits cannot replace physical examinations, laboratory work, or procedures. Even Amazon Care failed primarily because it couldn't solve healthcare's "last mile" problem: getting qualified medical professionals to patients' homes for services that couldn't be provided virtually.

A Better Way Forward: The Cultivation Approach

Just as modern agriculture learned to work with natural systems instead of against them, healthcare innovation must align with existing clinical expertise rather than trying to replace it. This "cultivation" approach has already produced remarkable results.

Consider how Mass General Brigham handled telemedicine during COVID-19. Rather than attempting a wholesale digital transformation, they methodically enhanced existing clinical workflows. They trained experienced doctors in virtual care best practices, carefully studied which types of visits worked best remotely, and gradually expanded services based on evidence. While tech startups promised AI-powered diagnoses, MGB's cultivation approach delivered real results: they went from 0.2% virtual visits to 62% in three months, while maintaining quality metrics.

The evolution of robotic surgery at Intuitive Surgical illustrates another successful cultivation story. Instead of trying to replace surgeons, they spent a decade working alongside them to develop the da Vinci system. They started small in 1999 with basic laparoscopic procedures, carefully documenting outcomes and incorporating surgeon feedback. Each new capability—from 3D visualization to haptic feedback—was developed through close collaboration between surgeons, engineers, and researchers. Today, robotic surgery is standard practice not because it disrupted traditional surgery, but because it enhanced surgeons' existing expertise.

Mayo Clinic's approach to AI implementation offers another template for cultivation. Rather than rushing to deploy AI across all departments, they created a structured evaluation framework. Each AI tool must first prove itself in a controlled setting, then in limited clinical trials, and finally in carefully monitored real-world applications. For example, their ECG AI algorithm, which detects hidden heart problems, went through two years of development before being integrated into clinical workflows. The result? The tool now helps cardiologists screen patients more efficiently while leaving complex interpretations to human experts—augmenting rather than replacing clinical judgment.

What Successful Cultivation Looks Like Today

  1. Enhance, Don't Replace: At Stanford Medical Center, radiologists partnered with a computer vision team to develop AI that flags potential fractures in X-rays. Instead of trying to automate diagnosis, the tool acts as a "second set of eyes," reducing missed fractures by 29% while letting radiologists focus on complex cases. The key? Radiologists were involved in every stage of development, ensuring the tool fit their workflow rather than disrupting it.

  2. Integrate, Don't Fragment: Cleveland Clinic's approach to digital health shows how new technologies can enhance rather than fragment care. Instead of launching standalone services, they integrated virtual visits into their existing primary care teams. When patients use their app, they see the same doctors who know their history. Remote monitoring data flows directly into patients' regular charts, and virtual visits can seamlessly transition to in-person care when needed. The result: 94% patient satisfaction while maintaining continuity of care.

  3. Respect Complexity: Kaiser Permanente's medication safety system demonstrates how embracing complexity leads to better outcomes. Rather than trying to streamline medication ordering with a simplified "one-click" system, they built a comprehensive platform that includes multiple safety checks, pharmacist review, and integration with patient records. While this process takes longer than direct prescribing, it reduced medication errors by 50% in its first year. The system succeeds because it works with healthcare's complexity rather than against it.

The Path Forward: Balancing Urgency with Wisdom

Critics of the cultivation approach often argue that healthcare's problems are too urgent for gradual change. They point to the 30 million Americans without health insurance, rising costs, and persistent health disparities. Surely, they argue, such systemic failures justify dramatic disruption?

But this creates a false choice between reckless disruption and complacent incrementalism. The cultivation approach actually enables faster, more sustainable progress by building on what works while thoughtfully incorporating new technologies. Consider how:

  • Health systems using cultivation principles deployed telemedicine more effectively during COVID-19 than pure "digital health" startups

  • Integrated delivery networks like Geisinger achieve better outcomes and lower costs by systematically improving existing systems rather than trying to reinvent them

  • Organizations that partner with tech companies rather than trying to replace clinical expertise consistently deliver better results

For entrepreneurs and technologists eager to improve healthcare, this means shifting from "disruption" to "enhancement" as the primary goal. Success requires:

  1. Deep partnership with clinical experts from day one

  2. Recognition that some healthcare "inefficiencies" serve crucial safety functions

  3. Focus on augmenting rather than replacing human judgment

  4. Commitment to evidence-based, systematic improvement

The future of healthcare innovation lies not in choosing between disruption and stagnation, but in combining technological creativity with clinical wisdom. By cultivating rather than disrupting, we can build a healthcare system that delivers what patients truly need: better care, lower costs, and improved access for all.

References

Balasubramanian S. Meaningful Healthcare Disruption has Become A Huge Challenge. Forbes. 2022; Accessed Jan 25, 2025. https://www.forbes.com/sites/saibala/2022/08/31/meaningful-healthcare-disruption-has-become-a-huge-challenge/.

Glaser J, Vaezy S, Guptill J. Why the Tech Industry Won’t Disrupt Health Care. 2024; Accessed Jan 25, 2025. https://hbr.org/2024/02/why-the-tech-industry-wont-disrupt-health-care.

Shaw B, Chisholm O. Creeping through the backdoor: Disruption in medicine and health. Front Pharmacol 2020;11:818.

Rahman A, Airini. Emerging Technologies Disruptive Effects: Review. Review PERINTIS eJournal 2017;7(2).

Numerof R. U.S. healthcare system braces for more tough times as disruptors continue their march to reform. Forbes. 2023; Accessed Jan 25, 2025. https://www.forbes.com/sites/ritanumerof/2023/12/26/a-failing-us-healthcare-system-braces-for-more-tough-times-as-disruptors-continue-their-march-to-reform/.

Lohr S. What Ever Happened to IBM’s Watson? The New York Times. 2021; Accessed Jan 25, 2025. https://www.nytimes.com/2021/07/16/technology/what-happened-ibm-watson.html.

Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med 2022;28(1):31–8.

Woolliscroft J. Innovation in response to the COVID-19 pandemic crisis. Academic Medicine 2020; https://journals.lww.com/academicmedicine/fulltext/2020/08000/Innovation_in_Response_to_the_COVID_19_Pandemic.23.aspx?context=LatestArticles. 10.1097/acm.0000000000003402

Akay M, Subramaniam S, Brennan C, et al. Healthcare innovations to address the challenges of the COVID-19 pandemic. IEEE J Biomed Health Inform 2022;26(7):3294–302.

Dixon-Woods M, Amalberti R, Goodman S, Bergman B, Glasziou P. Problems and promises of innovation: why healthcare needs to rethink its love/hate relationship with the new. BMJ Qual Saf 2011;20 Suppl 1(Suppl 1):i47-51.

George EI, Brand TC, LaPorta A, Marescaux J, Satava RM. Origins of robotic surgery: From skepticism to standard of care. JSLS 2018;22(4):e2018.00039.

Young AS. AI in healthcare startups and special challenges. Intell Based Med 2022;6(100050):100050.

Langlotz CP. Will Artificial Intelligence Replace Radiologists? Radiol Artif Intell 2019;1(3):e190058.

Institute of Medicine (US) Roundtable on Evidence-Based Medicine. The Learning Healthcare System: Workshop Summary. Washington (DC): National Academies Press (US); 2011.

Paulus RA, Davis K, Steele GD. Continuous innovation in health care: implications of the Geisinger experience. Health Aff (Millwood) 2008;27(5):1235–45.

Dr. Carl Preiksaitis