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Home Innovations & Investing

Art of the Startup

Daily Remedy by Daily Remedy
May 8, 2024
in Innovations & Investing
0

We call it a learning curve for a reason – it is not linear. Learning is a process with peaks and valleys, moving along a fluctuating curve.

Sometimes the curve trends downward and we fail. But few begin to learn something expecting this outcome. In fact, most of us are confident we can learn something so long as we apply the right amount of focus and effort. For our readers, this confidence extends to the world of healthcare startups.

We recently conducted a survey measuring the perception of healthcare venture capital. The survey reveals interesting discrepancies regarding their general understanding of venture capital relative to their perceived ability to raise venture capital.

Among survey participants, 43% acknowledge it is very difficult to raise capital. But 83% are confident they can raise funds, and 29% are confident they have the ability to raise funds, despite lacking any fundraising experience.

This survey reflects a general trend among the public that healthcare venture capital is accessible and readily available to those who lead promising healthcare startups. This belief is encouraged by media outlets broadcasting case studies of outlier successes of startups in healthcare, and conveying the belief that healthcare is ripe for innovation in healthcare.

But like the fluctuating learning curve, there are inevitable downward trends and outright failures. The most recent examples being the dissolution of Haven, a healthcare startup led by Dr. Atul Gawande, and Google’s unceremonious exit from healthcare insurance.

In 2017, Forbes Inc. found that 9 out of 10 startups fail and the leading reason for the failure is the inability to raise funds. But if a significant portion of the public believes they can raise funds, they must also then think they can successfully lead an innovative healthcare startup as well.

But healthcare is a notoriously difficult field to innovate and improve upon. Though that does not stop entrepreneurs from trying, nor dissuade them from thinking they have the chops to raise venture capital.

Clearly there is a gap in the perception of healthcare innovation and the realities in starting a successful healthcare venture.

How we think about something determines our approach to it. So if we think about healthcare innovation incorrectly, then we will naturally struggle to deliver proper solutions. And we will likely fail.

A recent study out of Public Health and Surveillance found a high failure rate for healthcare startups, mostly due to a disparity between the economic incentives that drive clinical behavior and the value offered by healthcare startups, including many digital technology platforms.

What healthcare entrepreneurs think their customers – be it patients or physicians – want and what they really want are two different things. Perhaps the leading reason healthcare startups fail is not the lack of funding, but the lack of proper understanding.

Healthcare entrepreneurs fail to understand their customers. We see this when a large corporation pivots into healthcare, only to exit after leaving a graveyard full of failures. And we see this in the abundance of healthcare startups and available venture funding, but little in the way of actual innovation in healthcare.

We see this is as a problem of perception.

Most entrepreneurs seek to improve upon something. They identify a problem and try to create a solution for it. Yet we find this approach ineffective in healthcare.

Instead, entrepreneurs should work to improve upon solutions that have not been fully developed. Rather than identify a problem to solve, find an opportunity to improve upon an already existing solution. The shift is subtle but will prove more impactful.

Healthcare is difficult to improve upon because it is a complex mix of large and small problems, half-solved problems, and newly created problems. It is impossible to create a definitive solution within such a byzantine structure.

Entrepreneurs should focus on incrementally improving nascent solutions already present in healthcare. Instead of developing a novel digital platform to communicate with patients, optimize the existing forms of communication. Instead of trying to invent a novel medical device, improve the utilization of existing devices.

The likelihood for success will be greater since the learning curve for improving an existing solution is less turbulent than creating a new solution.

At least that is the perception.

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Daily Remedy

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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Videos

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.
Hallucination in LLMs can lead to misinformation.
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.

Chapters

00:00 Introduction to LLMs in Healthcare
05:16 The Importance of Simplicity in LLMs
The Future of LLMs in HealthcareDaily Remedy
YouTube Video U1u-IYdpeEk
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AI Regulation and Deployment Is Now a Core Healthcare Issue

Clinical Reads

Ambient Artificial Intelligence Clinical Documentation: Workflow Support with Emerging Governance Risk

Ambient Artificial Intelligence Clinical Documentation: Workflow Support with Emerging Governance Risk

by Daily Remedy
February 1, 2026
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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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