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Patterns of Association

Daily Remedy by Daily Remedy
August 8, 2021
in Contrarian
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When you look into the pitch-black night sky and you see only one star, your entire focus is on that star. There is nothing else to see. But when you look and see a whole constellation of stars, your focus shifts – not to any one star, but to the pattern of stars forming the constellation.

You see the patterns of association, and a metaphor to describe an expert.

An expert is not necessarily someone more intelligent about something. Rather, an expert is someone who has a greater number of associations around a given topic, and therefore, a more comprehensive understanding of that topic.

We see this when watching physicians make a diagnosis. They are able to diagnose a medical condition out of a host of symptoms by associating the present of a single or set of symptoms with a diagnosis. When a patient presents with pain in the right lower abdomen, along with nausea and vomiting, most physicians would assume the patient has appendicitis. From the earliest days of medical training, physicians are taught to make these associations.

But sometimes relying excessively on associations limits how a physician makes decisions.

Something Dr. William Osler warned against when emphasizing bedside learning for medical students studying how to evaluate patients. He believed that maximizing direct experiences with patients will lead to better quality of care. Not because we will glean new data or uncover something previously hidden through such efforts, but because direct experiences with each patient allows us to understand the patient better.

Developing a more accurate assessment of how the patient thinks, and how the patient internalizes the care received, which in turn allows physicians to make better decisions – going beyond traditional patterns of associations derived through medical knowledge. As better decisions come from more accurate perceptions that come only through time and direct experience with the patient.

When physicians rely primarily on patterns of association, they focus only on the information available, not on the additional information that is not available, which may impact overall decision-making.

Great chess players look at a chess board and see both the pieces and the empty spaces. Great clinical decision-makers look at any decision in terms of what is known and unknown, and of relative benefit and risk – fully aware of all the factors that go into every decision.

Yet most physicians reflexively associate certain symptoms and signs with a specific diagnosis or treatment. A process that eventually becomes a mechanically observed thought pattern – since physicians think this way, physician decisions are also made in this way.

Simply put, physicians are experts in medicine who emphasize the most likely pattern of association. But in emphasizing these patterns, physicians begin to depend upon these distinct patterns of thinking, which may appear unfamiliar or even odd to someone who lacks these patterns of association.

This is the main reason patients and physicians often fail to communicate effectively. Physicians think a certain way, take pride in their presumed expertise, and communicate according to their patterns of association. Patients, lacking any such association, are piecing together knowledge as it appears to them – one star at a time – creating patterns that are distinctly different from the physician’s preexisting patterns.

When perceived in this manner it should be obvious why the physician and the patient think differently. A physician has a predetermine constellation of thoughts, a set pattern of association. Conversely, a patient is building his or her knowledge – star by star – learning while iteratively creating a new pattern simultaneously. The odds that the eventual patterns will be same are far less than the odds that the patterns will be different.

This means that over the course of a physician-patient relationship, the two will develop different understandings about a given diagnosis, treatment, or overall care management.

But simply because something is likely does not mean it is inevitable.

When the right steps are taken, the tendency to create different patterns of association can be avoided –steps that begin with the physician taking the time to understand how patients think.

Physicians should think through – or at least attempt to understand – how patients internalize their care management. If a physician prescribes a noncompliant patient struggling with diabetes a second diabetic medication, then that physician should also understand how the patient perceives that decision.

Something seemingly obvious, yet something that rarely happens, largely because physicians conflate a greater understanding of medicine with an all-around greater level of intelligence.

But numerous behavioral economic studies have shown that the baseline level of intelligence between an expert and an amateur is surprising similar – and the critical distinction comes in the level of knowledge, the patterns of association, separating an expert from an amateur.

This is why physicians cannot understand why so many patients are reluctant to receive a vaccine. Physicians cannot understand that patients perceive the data differently, or simply dismiss the patient’s perspective, failing to recognize the different patterns of association.

When looking at vaccine clinical trials, most physicians understand that effectiveness and safety are commonly studied together. Physicians recognize that effectiveness and safety are studied together in most clinical trials. Patients, unfamiliar with clinical study designs, do not see the association between effectiveness and safety – they see it as fundamentally distinct – that just because a trial proves the vaccine is effective, it does not automatically mean the patient would feel it is safe.

So when two of the COVID-19 vaccines, manufactured by AstraZeneca and J&J, became associated with rare incidents of blood clots, patients immediately focused on the perceived lack of safety, despite the vaccines demonstrating tremendous success against COVID-19 related hospitalizations. Physicians balanced the effectiveness of the vaccine with its side effects, and deemed the blood clots to be a rare side effect that most of the population would not suffer from.

Physicians see the constellation of facts and data, while patients see the one glaring star. Both are looking into the night sky, but both see different patterns.

To reconcile these presumed differences, physicians should realize that sometimes a single star in the constellation outshines all others in the minds of the patients.

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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
05:16 The Importance of Simplicity in LLMs
The Future of LLMs in HealthcareDaily Remedy
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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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