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Medical Data Lies

Our perceptions are the liars

Daily Remedy by Daily Remedy
September 25, 2022
in Contrarian
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Medical Data Lies

The more we learn about something, the weirder it gets. Animals in the ocean depths look nothing like the familiar surface dwellers. Subatomic particles like quarks behave nothing like the familiar protons and electrons we learn in basic physics.

Similarly, in healthcare, the more we study data, the weirder it gets. Today, data has become the all pervasive king of medicine. Most if not all clinical decisions are made or justified through data. When a patient presents with shortness of breath and an elevated heart rate, we run a gamut of tests. Data from those tests dictate the course of clinical care because they validate our perceptions of the presenting symptoms.

But when we study patient outcomes in today’s world of data driven medicine, which arguably is the true value gleaned out of data, the more inconclusive it proves to be. Data does only so much to help patient outcomes, and the more we rely on data, the more glaring the limitations become.

Data cannot address the persistent inequities in healthcare, the disparate outcomes based on race and gender. Data cannot explain why, despite an abundance of technological advancements, a growing sector of the patient population distrusts healthcare. We dismiss these contradictions as merely anecdotal or find some reason to explain away these persistent problems and justify our approach to medicine. But ultimately, our reliance on data is responsible. We do not understand data like we think we do.

We do not respond to data in medicine. We respond to the perceptions derived from them. When perceptions and data align, we call it good medicine. When the two cannot align, we call it a moral hazard, which can be seen throughout healthcare. In fact, it’s fundamental to the field.

Insurance companies carry deductibles for this very reason. When patients know they have funds to spend on their health, they are more willing to spend it. But when they have to pay an initial amount out of pocket before receiving coverage, they are less likely to use the reserved funds. It’s a perception play. Data has nothing to do with it. Studies have shown repeatedly that no matter how large the insurance coverage, deductibles deter patient consumption of healthcare services.

It’s ironic that we cite this example, since the moral hazard of deductibles has been studied at length, but we have rarely applied this perspective on data to other parts of healthcare where its impact is far greater in everyday patient care.

When a physician walks into a patient room, she normally begins by reviewing lab findings or an imaging result. It is a recital of data. But the patient hardly looks at data the same way as the physician. That difference in perspective leads to different reactions to the data. It is the veneer of perceptions.

Data never directly affects clinical behavior. The two are moderated by perceptions, a veritable middleman negotiating between both parties. It augments how the two interact. For any data point, a clinician always has the decision to integrate it into the care plan or to ignore it in favor of other information. Similarly, the patient balances information that appears to be substantially important compared with other information and prioritizes the perceived importance of certain data through the weight of emphasis.

The decisions that drive behavior are made implicitly through subconscious thought patterns. They are scattered rays of light, each flicker a perception; each perception a window into how we perceive data.

We think of data like it is an objective, concrete fact, but our use of data is anything but that – it is decidedly subjective. We missed that for decades in our push for evidence-based medicine. But luckily, we are taking note.

Maybe the pandemic opened our eyes to this. Maybe we knew the limitations of data all along, and we chose to ignore it. If that’s the case, then it would be quite ironic: that we knew of data’s limited value in clinical decision-making, yet we ignored it in favor of an artificial perception of data.

In the end, we have no option but to confront an uncomfortable reality – medical data lies and our perceptions are telling the lies.

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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.
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Healthcare providers must understand LLM limitations.
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00:00 Introduction to LLMs in Healthcare
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Large Language Models in Healthcare

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In just over two years, papers on large language models (LLMs) in medicine have accumulated nearly fifteen thousand citations, creating an academic canon that is already shaping funding decisions, regulatory conversations, and clinical experimentation. This study dissects the 100 most-cited LLM-in-medicine papers to show who is driving the field, which applications dominate attention, and where the evidence remains dangerously thin. What emerges is a picture of rapid intellectual consolidation—paired with a widening gap between technical promise and clinical reality. The...

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