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Home Financial Markets

Healthcare Innovation is Evolutionary

Daily Remedy by Daily Remedy
August 8, 2021
in Financial Markets
0

What we presume to be continuous is often discrete.

Something Paleontologist Stephen J. Gould came to find in his studies of evolutionary biology. He noted that most evolution is characterized by long periods of stability and only infrequently punctuated by swift periods of rapid evolutionary change and new species formation. This idea contrasted the then widely held notion that evolutionary change is characterized by a pattern of continuous, ongoing change, which is what many presumed based upon the fossil records available at that time.

Subsequent fossil records discovered afterwards have proven Gould right – that evolution, once thought to be continuous, is fundamentally discrete, with well defined periods of stasis and periods of rapid change.

This particular pattern of evolution not only defines how life changes on earth, but also how healthcare advances into the future – through rapid periods of change punctuated by periods of stasis – though you may not think that, given the seemingly incessant, rapid influx of venture capital into healthcare start-ups.

When you follow the funding trends in early stage healthcare startups, you would inevitably assume that the pandemic merely accelerated an ongoing, upward trend of investments in healthcare digital startups. After all, most of the data presumes as much. If anything, it seems that the pandemic was merely building upon a preexisting trend.

But many of the patterns that appeared over the course of the pandemic may prove short term aberrations rather than meaningful changes in healthcare. Yet differentiating between pandemic driven trends and short term aberrations can be quite confusing.

In a peer reviewed article published in late 2020, Dr. Sadiq Patel found that during the pandemic, 30.1% of all visits were provided by telemedicine, which substantiated a well-documented, exponential increase from pre-pandemic levels. Though the percentages varied across the country and by specialty, the growth was undeniable.

But within recent weeks, many large insurance plans have found mixed benefits with telemedicine. In a recent Healthcare Investing Trends Report by HIMSS (Healthcare Information and Management Systems Society), prominent healthcare private equity investors – those who invest in more mature healthcare companies – envision a different future for telemedicine, one that blends telemedicine with traditional in-person visits, and integrates seamless triaging and referrals between remote and in-person care.

This means the change many investors believe will come to pass is not necessarily in the technology, but in the interaction between the patient and the technology. And in a heavily regulated industry like healthcare, the regulations often define the interactions that take place with the patient. Which means the future of telemedicine is less dependent upon technology, and more upon the regulatory changes that would foster more digital interactions with patients.

A sentiment echoed by Dr. Katherine Dallow, Vice President of Clinical Programs and Strategy at Blue Cross Blue Shield of Massachusetts, who in a recent interview with USA Today said:

“A lot of people had wanted to bridge the gap between technology and provision of health care long before the pandemic. What happened with COVID, for better or worse, was the entire industry was freed up from all the regulatory issues that had been a barrier to people accessing virtual care.”

Implying that changes in regulatory policy define the discrete events between periods of change and periods of stasis in healthcare innovation – which, when applied to the pandemic, can differentiate between pandemic drive trends and short term aberrations.

As evolution is not just change, but change that successfully adapts. Healthcare innovations that either began or accelerated during the pandemic must now prove they can successfully adapt to the post-pandemic world of healthcare – not only changing with the pandemic, but successfully adapting post-pandemic.

Something that is not immediately discernible through the trends in healthcare investments. The pandemic has increased demand for digital health solutions, especially for access to care technology like telemedicine. According to Rock Health, a digital health incubator based out of San Francisco, funding for digital health startups nearly doubled from $7.5 billion in 2019 to $14.1 billion in 2020. Moreover, StartUp Health, a prominent healthcare crowd-funding platform, reported that health innovation funding increased 55% from 2019 to 2020.

But funding does not characterize a successful startup, which can be defined broadly as the ability to grow sustainability, impact patient care meaningfully, and above all, create an exit – a sale to a larger company or initiate a public offering on financial markets – which are the two main mechanisms through which startup investors make their money.

But in healthcare, most exits come from acquisitions, or sales transactions in which the startup is sold to a large corporation. In a 2020 report by Angel Capital Association, only 4% of all medical and healthcare technologies were likely to exit via a public offering and become a standalone company, whereas 18% were likely to be sold to a larger, more established company – with the remaining companies failing – indicating that most innovations in healthcare eventually merge into larger companies, becoming one of many offerings by companies well versed with the current healthcare market.

Large companies have resources and capabilities well beyond any startup, and the most likely situation in which a large company would acquire a startup is when a new market opportunity emerges – which usually only emerges when regulatory changes appear.

So while the increase in funding is undoubtedly captivating, and the pandemic driven influx of venture capital funding seems to portend many positive changes in healthcare, only those startups that can successfully navigate changes in the regulatory landscape can become successful – truly able to adapt to a post-pandemic world of healthcare.

Which makes healthcare innovation evolutionary – though not the continuous, ongoing evolutionary trend we normally presume evolution to be, but rather the rapid, discrete evolutionary trend that truly defines healthcare innovation.

In which innovation is measured not by the amount of venture capital injected into startups, but by the rate of regulatory changes that redefine basic patient interactions.

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Daily Remedy

Dr. Jay K Joshi serves as the editor-in-chief of Daily Remedy. He is a serial entrepreneur and sought after thought-leader for matters related to healthcare innovation and medical jurisprudence. He has published articles on a variety of healthcare topics in both peer-reviewed journals and trade publications. His legal writings include amicus curiae briefs prepared for prominent federal healthcare cases.

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In this episode, the host discusses the significance of large language models (LLMs) in healthcare, their applications, and the challenges they face. The conversation highlights the importance of simplicity in model design and the necessity of integrating patient feedback to enhance the effectiveness of LLMs in clinical settings.

Takeaways
LLMs are becoming integral in healthcare.
They can help determine costs and service options.
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LLMs can produce inconsistent answers based on input.
Simplicity in LLMs is often more effective than complexity.
Patient behavior should guide LLM development.
Integrating patient feedback is crucial for accuracy.
Pre-training models with patient input enhances relevance.
Healthcare providers must understand LLM limitations.
The best LLMs will focus on patient-centered care.

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00:00 Introduction to LLMs in Healthcare
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Health systems are increasingly deploying ambient artificial intelligence tools that listen to clinical encounters and automatically generate draft visit notes. These systems are intended to reduce documentation burden and allow clinicians to focus more directly on patient interaction. At the same time, they raise unresolved questions about patient consent, data handling, factual accuracy, and legal responsibility for machine‑generated records. Recent policy discussions and legal actions suggest that adoption is moving faster than formal oversight frameworks. The practical clinical question is...

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