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Home Trends

Data & Individuals

Daily Remedy by Daily Remedy
August 30, 2021
in Trends
0

“I distrust the medical facts”, American philosopher Ralph Waldo Emerson wrote when criticizing nineteenth-century healthcare’s tendency towards Reductionism, a belief that perceives a complex system to be the sum of its parts.

Yet Emerson’s words did little to curb the tide, and modern healthcare is now fully immersed in reductionist belief, to where we use data to predict patient behavior, and to price insurance premiums and clinical services accordingly.

Our world has become so digitized that we assume the future of healthcare means incorporating more technology into it. However, just because a little of something is good, it does not automatically follow that a lot of something is better.

Technology, as with most things in life, is best in moderation. And when taken to its extreme, transforms into something entirely different.

Technology epitomizes the modern reductionist ethos in healthcare. Technologists believe we can use data to solve healthcare problems, because they believe we are defined by our data. That any behavior, no matter how rational or irrational, how stereotypical or individualized, can be understood through data or multiple data sets.

We diagnose and monitor diabetes through HbA1c levels, which measure the average blood glucose. We make clinical decisions on hypertension management based on a patient’s blood pressure. And we measure success through quantified outcomes – even quantifying the inherently qualitative, like patient satisfaction.

While data standardize the quality of care, it reduces patient care to the rubric of standardization. Eventually data define the patient. In other words, we are defined by our component parts.

When taken to its logical extreme, data can be used to influence as much as diagnose patient behavior.

Data can be used to price out rolling tax rates based upon a patient’s body mass index (BMI). Those with a higher BMI, the obese, might then pay a higher tax on unhealthy foods like ice cream and red meats.

Data can analyze a person’s purchasing history and credit rating to determine the capacity to pay hospital bills. And preemptively let hospitals know how much of the cost of care a patient can realistically pay.

Data can diagnose, manage, and treat medical conditions. Just like it can predict, stratify, and analyze patient behaviors.

Eventually we will have to reconcile the data and the individual, and determine how much of the data represent who we are as individuals.

Are we the cumulative sum of our medical data?

Can we be defined by a medical decision?

In Florida, physicians walked out on a hospital system and refused to treat unvaccinated patients, an effort for which they were lauded across media outlets.

But is it ethical to judge a patient worthy or unworthy of treatment based upon one medical decision – to be vaccinated? Yes, the data suggest unvaccinated patients have more COVID-19 related complications and can place a greater burden on the healthcare system.

But no medical ethicist would advise refusing treatment on that basis. And here is where we find limits on the value of data.

Data can do many things for healthcare, but it cannot define fundamental clinical decisions. It should always be used as a tool to support physicians and nurses, as a complement to patient care. If we depend upon data to make fundamental clinical decisions, then we lose individual patient autonomy.

Eventually we will justify rolling taxes on consumer goods based upon clinical conditions. It is easy to say a person with a family history of lung cancer should pay more for cigarettes if he or she cannot quit smoking despite the prevalent medical risk. But such logic becomes less palatable when we are asked to pay a higher tax for pumpkin pie in autumn because we gained weight during the pandemic.

Healthcare was never intended to be fully reductionist, nor was the data ever intended to supersede the individual.

In our quest to improve healthcare, we have unleashed data across all facets of patient care. In most instances it has helped, which has prompted calls for more data.

But anything taken to an extreme no longer possesses its original value. And if we overwhelm healthcare through an influx of data, then we will lose the original value of data.

Emerson wrote, “I distrust the medical facts”, despite relying heavily on facts throughout his prodigious writing career. We too must find a balance between relying on data and maintaining a healthy skepticism of it.

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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.

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
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...

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