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

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    July 1, 2025

    The cost structure of hospitals nearly doubles

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

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    The Impact of COVID-19 on Patient Trust

    The Impact of COVID-19 on Patient Trust

    March 3, 2026
    Debunking Myths About GLP-1 Medications

    Debunking Myths About GLP-1 Medications

    February 16, 2026
    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
  • Surveys

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    Perceptions of Viral Wellness Practices on Social Media: A Likert-Scale Survey for Informed Readers

    Perceptions of Viral Wellness Practices on Social Media: A Likert-Scale Survey for Informed Readers

    March 1, 2026
    How Confident Are You in RFK Jr.’s Health Leadership?

    How Confident Are You in RFK Jr.’s Health Leadership?

    February 16, 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?

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Home Contrarian

Patient Perceptions Goes Beyond AI Modeling

Stop pretending you can predict a patient's emotions

Jay K Joshi by Jay K Joshi
February 17, 2024
in Contrarian
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Patient Perceptions Goes Beyond AI Association Modeling

Natanael Melchor

Artificial intelligence (AI) has revolutionized various industries, and healthcare is no exception. With advancements in technology, AI is expected to reshape the field of medicine, making it more efficient and productive. However, there are concerns that this focus on productivity may compromise patient perceptions and the quality of care.

True, AI has the potential to streamline various healthcare processes, from diagnosis to treatment. By analyzing vast amounts of data, AI algorithms can quickly identify patterns and make accurate predictions, leading to faster and more effective diagnoses. This efficiency allows healthcare professionals to make timely decisions, potentially saving lives.

With the advent of artificial intelligence in healthcare, the potential for increased efficiency and effectiveness in medical practices is undeniable. However, it is important to acknowledge the concerns that arise regarding the impact of AI on the human aspect of patient care. Many individuals may feel uneasy about the idea of AI tools becoming their main point of contact for healthcare services. The fear stems from the apprehension that crucial interpersonal qualities, like empathy and compassion, which are inherent in healthcare professionals, may be overshadowed by the cold and logical nature of AI algorithms. There is a genuine worry that patients may not receive the same level of understanding and emotional support that they would from a human healthcare provider.

While AI can greatly improve diagnosis, treatment, and overall healthcare outcomes, it is crucial to strike a balance between leveraging the benefits of AI and preserving the vital human connection that is at the core of patient care. The integration of AI in healthcare should be guided by the aim of complementing rather than replacing human healthcare professionals, ensuring that patients continue to receive the comprehensive care they need.

In essence, the healthcare sector was never designed to operate at absolute efficiency – a modicum of leeway or slack is always necessary. This concept of slack is primarily observed in systems thinking, an advanced yet largely underappreciated field of engineering.

Governed by societal norms that extol productivity and effectiveness as the ultimate pursuit, we commonly misinterpret such supposed efficiency as improvements in our daily functions; yet there exists a far more intricate principle known as slack. This theory underscores the criticality of refraining from overstressing maximum efficiency across all aspects of any system, especially when human involvement is substantial – for instance during patient interactions.

One of the most well-known instances of this phenomenon can be seen in the burnout experienced by employees in retail pharmacies when they attempt to introduce productivity metrics. When these metrics are implemented, it places an excessive burden on pharmacy staff, forcing them to rush through the process of filling prescriptions. Unfortunately, this hurried pace often results in an uptick in errors and mistakes. It is ironic that when we strive to improve efficiency and productivity through rigorous measures, it unintentionally leads to chaos and disorder. This highlights the importance of allowing some leeway and flexibility within complicated systems like retail pharmacies in order to maintain a productive and well-organized environment.

Consequently, despite attempts to enhance healthcare through increased proficiency which may instinctively seem beneficial and progressive we face bewilderment with little discernible improvement – even with high-tech enhancements permeating every corner of the industry.

We must strike a balance between efficiency and patient-centered care. Healthcare systems should incorporate AI as a tool to augment healthcare professionals’ abilities, rather than replace them. Implementing AI technology should be accompanied by proper training and education for healthcare professionals to ensure they can effectively utilize the technology and maintain quality patient care.

While AI has the potential to revolutionize medicine and improve efficiency, it is crucial to strike a balance between productivity-focused healthcare and patient perceptions. Healthcare should remain centered on providing compassionate care and considering patients’ subjective experiences.

That’s not efficiency. That’s empathy. The latter should be the focus.

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Jay K Joshi

Jay K Joshi

Dr. Joshi is the founding editor of Daily Remedy.

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Videos

In this episode of the Daily Remedy Podcast, Tiffany Ryder discusses her insights on healthcare messaging, the impact of COVID-19 on patient trust, and the importance of transparency in health policy. She emphasizes the need for clear communication in the face of divisiveness and explores the complexities surrounding the estrogen debate. Additionally, Tiffany highlights positive developments in health policy and the necessity of effectively conveying these changes to the public.

Tiffany Ryder is a political commentator and public health policy thought leader who publishes the Substack newsletter Signal and Noise: https://signalandnoise.online/


Chapters

00:00 Introduction to Healthcare Conversations
02:58 Signal and Noise: Understanding Healthcare Communication
05:56 The Storytelling Problem in Healthcare
08:58 Navigating Political Divisiveness in Health Policy
11:55 The Role of Media in Health Policy
15:03 Bias in Health Reporting
17:56 Estrogen and Health Policy: A Case Study
24:00 Positive Developments in Health Policy
27:03 Looking Ahead: Future of Health Policy
31:49 Communicating Health Policy Effectively
The Impact of COVID-19 on Patient Trust
YouTube Video ujzgl7HDlsw
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2027 Medicare Advantage & Part D Advance Notice

Clinical Reads

GLP-1 Drugs Have Moved Past Weight Loss. Medicine Has Not Fully Caught Up.

Glucagon-Like Peptide–Based Therapies and Longevity: Clinical Implications from Emerging Evidence

by Daily Remedy
March 1, 2026
0

Glucagon-like peptide–based therapies are increasingly used for weight management and glycemic control, but their potential impact on long-term survival remains uncertain. The clinical question addressed in this report is whether treatment with glucagon-like peptide receptor agonists is associated with reductions in all-cause mortality and age-related morbidity beyond their established metabolic effects. This question matters because these agents are now prescribed across broad patient populations, including individuals without diabetes, and long-term exposure may influence cardiovascular, oncologic, and neurodegenerative outcomes. Understanding whether...

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