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Home Trends

Healthcare in 2022 Will Be Subjective

Daily Remedy by Daily Remedy
December 26, 2021
in Trends
0
Healthcare in 2022 Will Be Subjective

Clinical research is the heart of clinical medicine. It defines what we know. More importantly, it defines how we think.

Thinking that led to many of the policy debates seen over the pandemic – miscommunication gaffes, overt disparities in trust, and inequities in patient outcomes. Thinking derived from traditional methods of conducting clinical studies and policy decision-making ingrained into the ethos of clinical medicine.

We conduct clinical studies, we produce outcomes based on those studies, and we make decisions and enact policies according to the outcomes. And we laud the outcomes as objective or evidence based, implying they possess a certain validity based on the study methods used to obtain them.

In other words, how we think reflects what we know.

The problem is that there is much about medicine we do not know. We are just beginning to learn about healthcare inequities and physician burnout. We are only beginning to understand the impact of cognitive biases on healthcare disparities.

These problems, proven during the pandemic to be pervasive across healthcare, are inherently subjective – and that is the problem. There is no clinical study design that can study these problems and derive outcomes through which we can resolve them.

We have to change how we think in order to study these problems. We need new study methods.

Currently, most clinical studies are adapted from the scientific method, an approach developed centuries ago to test whether scientific observations or theories can be proven as fact. The method uses rigorous study designs to eliminate any potential bias or variability that could affect the outcome.

When we apply the scientific method to healthcare, we quantify healthcare through objective standards, eliminating any biases. This is what we mean by evidence based.

But healthcare is as qualitative as it is quantitative, as subjective as it is objective. We can even argue that healthcare inequities and burnout are predominantly qualitative and subjective. And in our quest to quantify everything, we missed the mark on many of the problems that currently overwhelm healthcare.

Now we must explore novel study methods and pilot new study designs that incorporate the subjective, experiential nature of medicine. Fortunately, we do not have to start from scratch.

We have an abundance of study designs and qualitative techniques used in other disciplines that we can incorporate into healthcare – in much the same way we incorporated the scientific method into clinical research.

These methods are distinctly subjective. They often embrace the logical fallacies and cognitive distortions eliminated through the scientific method – but are essential to the healthcare experience.

Techniques like the Delphi Method that organizes qualitative beliefs to reach consensus decisions or policies in areas where clinical data have not been established yet.

Models like Prospect Theory that recognizes decision-making is inherently biased and attempt to quantify the degree of bias in a projected outcome, which more accurately predicts patient decision-making.

For medicine to advance beyond the limitations seen during the pandemic, we must change how we think. This begins by developing new clinical study designs and techniques to glean novel outcomes. The conclusions we reach may not be objective in the traditional sense – as we understand the word – but relevant in understanding patient behavior and healthcare overall.

Right now we are merely adapting the scientific method in different ways to study subjective problems like healthcare disparities. This does not go far enough. We need study designs that entirely abandon the scientific method and wholly embrace novel study methods.

The transition will be difficult at first. But it is a necessary shift to fully understand these more subjective problems. What we will learn will change the way we think about medicine.

And as we think about medicine differently, we will accept different truths.

We will finally accept that medicine is subjective.

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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

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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
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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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