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Home Uncertainty & Complexity

Can Medicine Return to its Roots?

Daily Remedy by Daily Remedy
September 6, 2022
in Uncertainty & Complexity
0
Can Medicine Return to its Roots?

We complain about medicine so often that it has become a cliché. Yet we do little about it. We might write an article or post something on social media from time to time. But that is it.

It feels like empty pageantry more than anything else. Perhaps that is the point. Complain a bit about a system that is inherently flawed and then go about operating within it. It makes for a convenient balance of justified self-righteousness.

Unfortunately, there is a difference between being self-righteous and being right – between simply talking versus doing something meaningful. We see it when we acknowledge our hypocrisies.

Few in medicine want to change the system. Sure, it may not lead to optimal patient outcomes, but it surely enriches those in positions of power. And they dictate the course of care. Since financial gains come from technological advancements, we have a technological model of medicine.

The purpose of technology is to create data. The purpose of data is to create certainty. Certainty is good for shareholders and financial returns. It is all quite circular.

But it is not good for medicine, which requires uncertainty. Most would assume technology and data are the problem. So let us rid ourselves of that and return to the idealized, pastoral roots of humanistic medicine. That would be wrong and grossly unrealistic. Technology and data are not the problem. It is our use of them.

The two hold value, and have the potential for even greater value if used properly. That sounds overly idealistic and incredibly difficult to define. What is proper in a healthcare system may not be proper for patients. But the problem is more fundamental: we do not even know the context for understanding this term.

That, ironically enough, is why we will find a solution. Medicine is by nature uncertain. We distort it into something concrete because we like to think in terms of certainty. We then take technology and the data derived out of it and force an answer. If we palpate an abdominal mass, then we order an imaging study of the abdomen, visualize the mass, and measure its size. We then determine the course of clinical care off of the numbers.

It is all so simple, so certain – yet somehow proves limited and harmful to patients. In our quest for certainty, we forgot why uncertainty matters in medicine. It explains why we have mounting vaccine hesitancy despite unprecedented technological advancements in vaccine development. Certainty is rational and uncertainty is irrational, consisting of subjective perceptions innate to each of us.

We like data that is rational and when it behaves irrationally, we like to explain it away. We call it things like noise or randomness. Instead, we should call it patient perceptions and explain it to patients. When we diminish their gut feelings, we lose the humanity in medicine.

And we are left with technology and data: outcomes and cash flow statements. When we see it so plainly, we confront an uncomfortable reality and we react polemically, yelling: medicine has lost its way, we need to reset the entire system, and on it goes.

No, we do not need a massive overhaul. We need to realign how we approach technology and data. Divert the two toward medical uncertainty and frame them around what we do not know. This provides a clear delineation between what is known relative to what is unknown.

Known information guides clinical decision-making. Whatever unknown that is acknowledged fosters clinical trust. One leads the way and the other ensures no one is left behind.

To attain this balance is simple enough. Adjust how we look at data and use technology. Right now we obsess over concrete certainty – find the right diagnosis, the best clinical treatment. Instead, use the two to find a balance between a good enough diagnosis, an effective enough treatment, and active patient engagement.

It will lead to better patient outcomes.

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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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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.

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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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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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