Sunday, February 15, 2026
ISSN 2765-8767
  • Survey
  • Podcast
  • Write for Us
  • My Account
  • Log In
Daily Remedy
  • Home
  • Articles
  • Podcasts
    The Future of LLMs in Healthcare

    The Future of LLMs in Healthcare

    January 26, 2026
    The Future of Healthcare Consumerism

    The Future of Healthcare Consumerism

    January 22, 2026
    Your Body, Your Health Care: A Conversation with Dr. Jeffrey Singer

    Your Body, Your Health Care: A Conversation with Dr. Jeffrey Singer

    July 1, 2025

    The cost structure of hospitals nearly doubles

    July 1, 2025
    Navigating the Medical Licensing Maze

    The Fight Against Healthcare Fraud: Dr. Rafai’s Story

    April 8, 2025
    Navigating the Medical Licensing Maze

    Navigating the Medical Licensing Maze

    April 4, 2025
  • Surveys

    Surveys

    AI in Healthcare Decision-Making

    AI in Healthcare Decision-Making

    February 1, 2026
    Patient Survey: Understanding Healthcare Consumerism

    Patient Survey: Understanding Healthcare Consumerism

    January 18, 2026

    Survey Results

    Can you tell when your provider does not trust you?

    Can you tell when your provider does not trust you?

    January 18, 2026
    Do you believe national polls on health issues are accurate

    National health polls: trust in healthcare system accuracy?

    May 8, 2024
    Which health policy issues matter the most to Republican voters in the primaries?

    Which health policy issues matter the most to Republican voters in the primaries?

    May 14, 2024
    How strongly do you believe that you can tell when your provider does not trust you?

    How strongly do you believe that you can tell when your provider does not trust you?

    May 7, 2024
  • Courses
  • About Us
  • Contact us
  • Support Us
  • Official Learner
No Result
View All Result
  • Home
  • Articles
  • Podcasts
    The Future of LLMs in Healthcare

    The Future of LLMs in Healthcare

    January 26, 2026
    The Future of Healthcare Consumerism

    The Future of Healthcare Consumerism

    January 22, 2026
    Your Body, Your Health Care: A Conversation with Dr. Jeffrey Singer

    Your Body, Your Health Care: A Conversation with Dr. Jeffrey Singer

    July 1, 2025

    The cost structure of hospitals nearly doubles

    July 1, 2025
    Navigating the Medical Licensing Maze

    The Fight Against Healthcare Fraud: Dr. Rafai’s Story

    April 8, 2025
    Navigating the Medical Licensing Maze

    Navigating the Medical Licensing Maze

    April 4, 2025
  • Surveys

    Surveys

    AI in Healthcare Decision-Making

    AI in Healthcare Decision-Making

    February 1, 2026
    Patient Survey: Understanding Healthcare Consumerism

    Patient Survey: Understanding Healthcare Consumerism

    January 18, 2026

    Survey Results

    Can you tell when your provider does not trust you?

    Can you tell when your provider does not trust you?

    January 18, 2026
    Do you believe national polls on health issues are accurate

    National health polls: trust in healthcare system accuracy?

    May 8, 2024
    Which health policy issues matter the most to Republican voters in the primaries?

    Which health policy issues matter the most to Republican voters in the primaries?

    May 14, 2024
    How strongly do you believe that you can tell when your provider does not trust you?

    How strongly do you believe that you can tell when your provider does not trust you?

    May 7, 2024
  • Courses
  • About Us
  • Contact us
  • Support Us
  • Official Learner
No Result
View All Result
Daily Remedy
No Result
View All Result
Home Uncertainty & Complexity

Familiarity Biases

Daily Remedy by Daily Remedy
August 8, 2021
in Uncertainty & Complexity
0

“No one realized an epidemic was going on”, an anonymous Chinese patient reported in early 2020 at the onset of the COVID-19 in China.

That was just one patient’s words. But it could have been any one, as most of us were in some way caught off guard by the pandemic.

The tendency to continually be surprised by new trends in healthcare is the well-known familiarity bias that we have seen before, whether we have recognized it or not.

When presented with a new set of symptoms or a new disease presentation, we initially seek to understand it in familiar terms. Our biases always prioritize the familiar, which means our interpretations come from a frame of reference that assumes we have seen this before.

And we rely on our initial interpretations to set the framework for how we interpret new information, and subsequently understand it further. Hence the power of initial impressions.

Only when we have added enough unique perspectives, do we adjust our frame of reference through interpretations that acknowledge the uncertainty. With every new perspective, be it a direct observation, an experience, information received, we add to the frame of reference. Which accumulate into one aggregated set of perspectives, that determine the dominant perception through which we interpret something new or unfamiliar.

When we first heard of COVID-19 variants, we were not sure how to understand them. For many still coming to terms with the pandemic as a real disease, it takes yet another shift in perception for those to understand and become consciously aware of the heightened risks the variants pose. Take for example, a person who initially struggles to understand the pandemic.

When she first hears about the variants, she processes the new information from the perception she currently has. She consciously interprets the variants to be more dangerous only after she has sufficient reasons to change her perception. As the new perspectives accumulate and influence her overall interpretations.

This shift occurs differently among different people. For those familiar with virus mutations and epidemics, it may not take much to shift perceptions. For those struggling to understand whether the pandemic is real, this shift may require more information and more time. It depends on how the person understands new information, and the existing perceptions the person holds. All of which predispose the person to first look in terms of what is already familiar, reinforcing the heuristics emanating from familiarity biases.

To think about something new requires us to first understand what makes it new. That comes from understanding the uncertainty through which we are deriving our thoughts. We naturally assume something that is new relative to things we already know or are familiar with.

But only after we shift our frame of reference do we actively entertain the possibility that we are observing something new. We overcome familiarity biases by focusing on the uncertainty that gives rise to the heuristic.

The easiest way to do this is by refocusing our attention on thoughts opposite to our dominant perception. If we believe we should order a test, we should also consider why we should not order a test. Emphasize the opposite whenever interpretations are leading to a decision. And once both the predominant perception and its opposite have been considered, visualize the corresponding decision that arises from interpretations of either perception. Determine what makes one decision a better option compared to its opposite.

That consideration, and subsequent determination, creates awareness.

Awareness changes the way we approach every decision. Instead of deciding upon something and reflexively acting, we are balancing options relative to one another. Healthcare decisions then become the clinical equivalents of opportunity cost decision-making.

As the decision to order a lab is no longer an instinctive order, but a balanced consideration to order or not, integrating all the factors that go into that decision – cost, medical need, unnecessary punctures to the patient – each weighed accordingly.

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

Without structuring clinical decision-making, most interpretations are generated reflexively. Often by adhering to a set protocol. Or generated chaotically. By listening to the fluctuating perceptions of the moment.

Lacking structure, different factors are considered more important compared to others, and what we consider important is typically a reaction to something we encountered recently.

A provider who missed diagnosing a patient with anemia will thereafter order hemoglobin blood tests on more patients, and with greater frequency as a reaction to the one decision not to order a hemoglobin test.

Establishing a structured frame of reference addresses this tendency to react. Allowing providers to interpret new information without reacting to previous perceptions, and to understand the current uncertainty presenting in the patient. A shift in thinking that leads to better decision-making.

Something Dr. William Osler advocated when emphasizing bedside learning for medical students initially learning 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 allows providers to make better decisions in turn. Better decisions derive from more accurate perceptions that come only through time and direct experience with the patient.

Many may argue that it is not necessary to think this way most of the time. As most of medicine is based upon recognizing a familiar constellation of symptoms and signs, and diagnosing and treating patients accordingly.

Over time, providers associate certain symptoms and signs with a specific diagnosis or treatment. And this process eventually becomes a thought pattern that is mechanically observed, situation after situation. In which the provider first notices things that are familiar and then gradually begins to become aware of differences afterwards. Since our minds think this way, our decisions are made in this way.

Which explains why much of the care for cancer patients focuses on treatment protocols after the cancer has already been formed, and less on optimizing the early detection of the first symptoms. Early detection requires a shift in awareness to interpret the early symptoms differently.

In a way that optimizes the way we approach the uncertainty around an initial set of vague symptoms. Cancer researchers who study early detection find the proper interpretation of the symptoms is as important as the symptoms themselves.

Optimizing clinical decision-making does not come down to fixing a set of perceptions or interpretations. That simply produces more familiarity biases.

Rather, it depends on being aware of how the different perceptions fluctuate to create different interpretations. Become aware of the biases to preemptively address them.

Instead, we attempt to standardize clinical care by creating set thought patterns that fix the way we think. Reinforcing heuristics without being consciously aware of it. In dynamic, chaotic ways largely without us knowing.

But in the process of structuring these patterns, we fix the interactions that define healthcare.

Whether it is between the provider and patient, patients and their thoughts, or the dominant perception and its subsequent interpretation.

To what is familiar, and what is not.

ShareTweet
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.

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

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
Subscribe

2027 Medicare Advantage & Part D Advance Notice

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
0

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...

Read more

Join Our Newsletter!

Twitter Updates

Tweets by TheDailyRemedy

Popular

  • The Information Epidemic: How Digital Health Misinformation Is Rewiring Clinical Risk

    The Information Epidemic: How Digital Health Misinformation Is Rewiring Clinical Risk

    0 shares
    Share 0 Tweet 0
  • Prevention Is Having a Moment and a Measurement Problem

    0 shares
    Share 0 Tweet 0
  • Child Health Is Now a Platform Issue

    0 shares
    Share 0 Tweet 0
  • Behavioral Health Is Now a Network Phenomenon

    0 shares
    Share 0 Tweet 0
  • The Breach Is the Diagnosis: Cybersecurity Has Become a Clinical Risk Variable

    0 shares
    Share 0 Tweet 0
  • 628 Followers

Daily Remedy

Daily Remedy offers the best in healthcare information and healthcare editorial content. We take pride in consistently delivering only the highest quality of insight and analysis to ensure our audience is well-informed about current healthcare topics - beyond the traditional headlines.

Daily Remedy website services, content, and products are for informational purposes only. We do not provide medical advice, diagnosis, or treatment. All rights reserved.

Important Links

  • Support Us
  • About Us
  • Contact us
  • Privacy Policy
  • Terms and Conditions

Join Our Newsletter!

  • Survey
  • Podcast
  • About Us
  • Contact us

© 2026 Daily Remedy

No Result
View All Result
  • Home
  • Articles
  • Podcasts
  • Surveys
  • Courses
  • About Us
  • Contact us
  • Support Us
  • Official Learner

© 2026 Daily Remedy