After I wrote Why Primary Care is a Product No One Wants, Dr. Paulius Mui - a leading voice in primary care innovation - was happy to let me know where I got it wrong.

In our conversation, we dive into why primary care is both an investment and a broken business model, the misalignment between what doctors and patients actually want, and whether throwing more money at the problem will fix anything.

We also explore the rise of Direct Primary Care (DPC), the evolving role of AI in clinical decision-making, and what the future of primary care looks like in a world of corporate medicine, policy constraints, and shifting workforce dynamics.

Dr. Mui shares why technology is like fire - it can cook a meal or burn down the house - and how we should be thinking about empowering clinicians and patients alike, rather than just optimizing for efficiency.

It’s a candid, thought-provoking conversation about the past, present, and future of primary care. Give it a listen and let me know what you think!

Conversation Companion Guide - Appendix

Dr. Mui mentions a few concepts and books in our talk. Below is a quick reference for you if needed. FYI this list was made with the help of an LLM.

📚 Concepts and Books Referenced

* Nicholas Carr – The Glass Cage

* Explores automation’s effects on industries like aviation, healthcare, and manufacturing, warning about the risks of over-reliance on technology.

* Highlights the paradox of automation: while technology makes tasks easier, it can also deskill professionals, making them dependent on the very systems meant to assist them.

* G. Gayle Stephens – The Intellectual Basis of Family Medicine

* Positions family medicine as a countercultural movement resisting the fragmentation of care caused by the rise of medical specialization.

* Discusses how primary care’s holistic approach fosters long-term patient relationships, which is often undervalued in fee-for-service models.

* Clayton Christensen – The Innovator's Prescription

* A blueprint for disruptive innovation in healthcare, proposing models like retail clinics, technology-driven diagnostics, and a stronger role for primary care.

* Introduces the concept that what was once specialized medical knowledge is now being democratized, first to primary care physicians and ultimately to patients through technology.

* Rebecca Etz – PCPCM Measure (Patient-Centered Primary Care Measure)

* A validated tool designed to measure the strength of patient-provider relationships and assess primary care’s comprehensiveness.

* Suggests that strong patient-provider continuity leads to better health outcomes, yet current healthcare reimbursement models fail to prioritize it.

* Barbara Starfield – Four C’s of Primary Care

* Defines the essential functions of primary care: Care Coordination, Continuity, Comprehensiveness, and First Contact access.

* Advocates that strengthening primary care leads to better health outcomes, lower costs, and reduced disparities, yet U.S. policy and payment structures undermine these core principles.

* Direct Primary Care (DPC)

* A membership-based healthcare model where patients pay a flat monthly fee for primary care services, bypassing insurance and reducing administrative burdens.

* Seen as a potential solution to improve physician satisfaction and patient care by allowing for longer visits, better continuity, and cost transparency. However, concerns exist about its scalability and impact on access for lower-income populations.

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After I wrote this piece ‘Will AI Kill Innovation?’, Michael Schrage - A technology, business, and innovation thought leader at MIT - was happy to let me know that my argument was off track :).

So take a listen to our conversation that covers AI’s role in human innovation, the debate between free will and determinism, and how philosophy shapes our understanding of intelligence, decision-making, and creativity. We also talk about what Healthcare is getting wrong with Generative AI, and why the future of AI lies in pattern recognition rather than just language.

If you have a chance, check out Michael’s article “Philosophy Eats AI” in the MIT Sloan Management Review.

Conversation Companion Guide - Appendix

If you are like me and have faded memories of college Philosophy classes, then the below notes may be a helpful guide since we chat about authors, philosophers, and other concepts. Apropos, it was made with the help of an LLM.

📚 Thinkers and Authors Referenced

1. Judea Pearl – Causality & AI

Judea Pearl is a computer scientist and philosopher best known for his work on causality and Bayesian networks. His book The Book of Why explores how we can move beyond mere correlations to understand cause-and-effect relationships, a key issue in AI and predictive analytics.

2. Peter Thiel – Innovation and Zero to One

Peter Thiel is a venture capitalist, entrepreneur, and co-founder of PayPal. His book Zero to One argues that true innovation happens when you create something entirely new, rather than just improving existing ideas.

3. Daniel Kahneman & Amos Tversky – Behavioral Economics

Psychologists Kahneman and Tversky pioneered behavioral economics, studying how human decision-making is often irrational due to cognitive biases. Kahneman’s book Thinking, Fast and Slow explains how our brains use two systems: a fast, intuitive system and a slow, logical system.

4. Dan Ariely – Predictably Irrational

A behavioral economist who studies why people make irrational decisions and how emotions, biases, and social influences affect economic choices.

5. Albert Einstein – Thought Experiments & Relativity

I mention Einstein’s Gedankenexperiment (German for "thought experiment") about riding a beam of light, which led to the development of the Special Theory of Relativity. This highlights how imaginative experiments (not just data-driven analysis) fuel groundbreaking discoveries.

6. Stephen Wolfram – A New Kind of Science

A computer scientist and physicist, Wolfram explores how simple computational rules (like cellular automata) can generate complex behavior, influencing AI and machine learning.

7. John von Neumann – AI & Game Theory

A mathematician and physicist, von Neumann contributed to game theory, quantum mechanics, and early computer science, laying the foundation for modern AI.

8. Kurt Gödel – Incompleteness Theorem

Gödel’s Incompleteness Theorem states that in any formal system, there are truths that cannot be proven within the system itself. This challenges AI’s ability to fully understand the world.

9. Ludwig Wittgenstein – Language & Meaning

Wittgenstein explored how meaning is derived from context and usage rather than fixed definitions.

10. Sam Harris – Free Will Debate

Sam Harris is a neuroscientist and philosopher who argues that free will is an illusion, a point Michael Schrage vehemently pushes back against!

11. Jean Baudrillard – Simulacra & Hyperreality

Baudrillard argued that in today’s world, representations (like media and AI-generated content) can become more "real" than reality itself.

12. Nassim Taleb – The Black Swan & Risk

A philosopher-statistician who introduced the concept of Black Swan events—rare, unpredictable events that shape history—and argues for "skin in the game", meaning decision-makers should bear the risks of their own choices.

13. Michel Foucault – Power & Institutions

Foucault studied how power and knowledge shape social institutions, including healthcare. Michael pushes back on my “Foucauldian” view, arguing that information asymmetry is a bigger issue than power asymmetry.

14. Peter Singer – AI & Ethics

A moral philosopher known for utilitarian ethics (the idea that decisions should maximize overall well-being). Singer has an AI trained on his writings -https://www.petersinger.ai/

📖 Key Concepts Mentioned

🔹 Epistemology – The Study of Knowledge

Epistemology is the branch of philosophy concerned with how we acquire, justify, and define knowledge. It explores questions like: What counts as knowledge? Can AI “know” something, or does it merely predict?

🔹 Ontology – The Study of Being & Existence

Ontology is the philosophical study of what exists and what it means to “be”. It’s a critical issue in AI, raising questions like: Is an AI that mimics human thought truly intelligent, or just simulating it? Do AI models have any kind of “understanding” or are they just pattern-recognizers?

🔹 Teleology – The Study of Purpose & Goals

Teleology examines the purpose or end goal of things. In AI and philosophy, it raises questions like: Does AI have a purpose, or is it just a tool? Should AI systems be built with a specific moral or ethical objective?

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit ankoors.substack.com

Learn more about Dr. Reza Alavi and Quintuple Aim Solutions.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit ankoors.substack.com