The AI Content Flywheel: How I Build an Audience as a Surgeon Without Sounding Like a Robot

Claude CoWork Live Artifacts and Scheduled Tasks Tutorial

This is a free preview of a paid episode. To hear more, visit techysurgeon.substack.com [https://techysurgeon.substack.com?utm_medium=podcast&utm_campaign=CTA_7] If you want to go deeper and combine scheduled tasks and artifact with context from meetings. Check out the articles below: How I use AI to create content that can be part of your “AI loops””

The AI Content Flywheel: How I Build an Audience as a Surgeon Without Sounding Like a Robot

This is a free preview of a paid episode. To hear more, visit techysurgeon.substack.com [https://techysurgeon.substack.com?utm_medium=podcast&utm_campaign=CTA_7] Thank you Doug Fullington, MD [https://substack.com/profile/445396101-doug-fullington-md], Alex Rivero [https://substack.com/profile/32407752-alex-rivero], HealthMind Insights [https://substack.com/profile/6308510-healthmind-insights], Eric Burgh, MD [https://substack.com/profile/49261132-eric-burgh-md], and many others for tuning into my live video! Join me for my next live video in the app. I can publish a paper in an upper-tier orthopedic journal and, if I’m being candid, a few hundred to maybe a few thousand people will read it. Some of those people will cite it in their own papers. A small number will change anything in their practice because of it. Then I share an insight about CMS payment model mechanics on Substack, and a health system CFO I’ve never met emails me because she’s been trying to articulate that exact problem to her board. A policy researcher in Geneva follows the thread. Three orthopedic surgeons I’ve never spoken to start a conversation about what I wrote that turns into an ongoing dialogue that has genuinely shaped how I think. That asymmetry in exposure is what motivated me to start writing in public. Not personal branding, not monetization, not the promise of newsletter revenue (though those have their own logic). The primary driver was reach and dialogue. The feeling that the ideas worth communicating in healthcare were reaching a fraction of the people who needed them, mostly because we’d built a science communication infrastructure that made academic publishing the gold standard and everything else secondary. AI changes the equation a bit. Not by doing the thinking. The thinking is still yours, and it’s still the most important part. But by handling enough of the operational needs that someone running a clinical practice, a research agenda, and a startup can also maintain a consistent presence in public. That’s what I want to share here: how I’ve structured that system, in enough detail that you can build your own version of it if it seems useful. What works for me may not work for you. I can’t promise to optimize your content calendar. But I wanted to describe infrastructure that has made the practice of writing in public sustainable for me, and that has produced a lot of unexpected good in the process. Why This Isn’t Primarily About Content Creation A minor but important framing note before the setup details: the value I’ve gotten from Techy Surgeon isn’t primarily the newsletter metrics. It’s the policymakers who’ve reached out. The research collaborators who found me because of a piece on care coordination. The clinicians who wrote to say that an article crystallized something they’d been trying to explain to administrators for years. The people building interesting companies in healthcare who wanted to connect because we were apparently thinking about similar problems from different angles. None of those connections would have happened if I hadn’t been willing to put my thoughts into a form that others could interact with. And it wasn’t a white paper or journal article that did that. It was something closer to a public conversation, where the format invites response and the distribution reaches people outside the academic bubble. I think medicine, and clinical research more broadly, is underinvested in this kind of communication. Not because clinicians don’t have things worth saying (clearly they do), but because the infrastructure for saying them publicly has been either unavailable, considered taboo, or too costly in time. AI is changing that. The skill of generating multimodal media whether written, visual, or video, is becoming something a motivated clinician can build and maintain without a full production team. We probably need more of us doing this. The Flywheel, Briefly Before the setup details, the concept: a content flywheel is a system where each piece of output feeds the next, and where the marginal cost of producing content decreases over time rather than remaining constant. The alternative is what most clinician-writers default to: the brute-force model, where every piece starts from scratch, involves a Sunday afternoon staring at a blank editor, and depends entirely on having energy left over from the clinical and research work. That’s a fragile system. It produces good work occasionally and nothing the rest of the time. The flywheel I’ve built runs on five loops: Ideate (keep a backlog so you never start from nothing), Research (get sources before you start writing, not after), Write (use AI constrained by your voice, not AI in its default state), Distribute (publish once, distribute many times across platforms), and Repurpose (one strong piece seeds two weeks of downstream content). The setup below is how I’ve operationalized each of those loops.

Clinical AI Faceoff: OpenAI's ChatGPT for Clinicians vs OpenEvidence vs DoxGPT

This is a free preview of a paid episode. To hear more, visit techysurgeon.substack.com [https://techysurgeon.substack.com?utm_medium=podcast&utm_campaign=CTA_7] Thank you to everyone who tuned into my live video! Join me for my next live video in the app. I went live at 6:45 the other morning to open three tabs, ChatGPT for Clinicians, Doximity GPT, and OpenEvidence, and ask them the same questions. A few dozen clinicians and subscribers joined at that hour on a Sunday, which I did not expect, and I’m grateful for. The headline finding isn’t who won. It’s that it seems soon you won’t be able to tell the three tools apart from the navigation bar. Each one now has an ambient scribe (or form of one). Each one tracks CME. Each one has a “skills” or “dot flows” tab that, today, mostly amounts to baked prompts dressed up as workflows. OpenEvidence has a feature literally called the dialer — Doximity has had a dialer for a decade. The product surface is converging fast. A quick disclaimer before we go further: opinions here are mine alone. I have no financial relationship with any of these companies. I selected these three because they appear to be getting the most traction in the marketplace — not because they’re the only ones worth your time. Up-to-Date Expert AI, Glass Health, Abridge’s embedded answering, and others all deserve their own look. What each tool is best at right now ChatGPT for Clinicians is the new entrant. Verification is rigorous — NPI, photo of a driver’s license, a ClearID face match — which I read as a deliberate credibility signal. Underneath, the experience is polished but the clinical answers were the weakest of the three on the queries I ran. There is a skills surface that hints at where this is going, but most of the entries today function as prompts rather than true agentic workflows. I did not see a Business Associate Agreement presented during signup, and I have not yet found a satisfying answer on PHI handling. Doximity GPT quietly has the best one-off clinical answers right now. Not by a wide margin, the others are good, but on a hip arthroplasty question and a DVT prophylaxis report, Doximity surfaced the PREVENT CLOT trial [https://www.nejm.org/doi/full/10.1056/NEJMoa2205395] and the CRISTAL trial [https://jamanetwork.com/journals/jama/fullarticle/2802988] at the top of the response, where a domain expert would put them. For a clinician, citation prioritization is trust. Doximity also brings a distribution moat the others can’t replicate quickly — the dialer, fax, telehealth, the news network, and Peer Check (where physician experts grade the answers) — and a redesigned interface that’s the cleanest of the three. OpenEvidence has the lowest friction and the fastest latency. They are clearly throwing serious compute at the answer surface. The differentiator most clinicians never find is Deep Consult. Turn it on, answer two or three follow-up questions, and you get a research-grade brief with embedded figures from JAMA and NEJM, made possible by the licensing partnerships OpenEvidence has signed with NEJM Group [https://www.nejm.org/] and other major publishers. When I asked Deep Consult to brief me on secondary fracture prevention for a quality improvement committee, the output was something I could have walked into a department meeting with that morning. Distribution beats product when the products converge All three are free. All three answer questions credibly (ChatGPT least so). All three are racing to bolt on the same surrounding capabilities. Doximity wins on installed clinician base. OpenEvidence wins on speed, trajectory, raw capability and on Deep Consult. ChatGPT for Clinicians wins, today, on almost nothing — but the verification gate suggests they intend to be taken seriously, and they hold a foundational model and patient facing asset the others don’t. The chat interface is no longer the moat. The moat is whoever first connects grounded clinical evidence to native multimodal output, real workflow extensibility, and physician-earned trust without forcing the clinician to play copy-paste between four tabs to get there. This is the worst these tools will ever be. That should change how we evaluate them: I’m less interested in the question “does it work today?” , and more gravitating to “how do we shape what it becomes?” Date several. Marry none. Use the tool that fits the question in front of you. Send the teams behind them living, breathing feedback. On the Horizon None of these tools is built for patients. The updated guidelines on incidental hepatic steatosis answer ChatGPT gave me this morning was reasonable, and I am a bone surgeon. I read it the way a layperson would, and I would not stake decisions on it without help. The literacy gap between clinical outputs and patient comprehension is not a UX problem. It is a safety problem. Tearing down the gate before we have built tools that respect that gap is how we get harm. The administrative arms race — prior authorization letters, denial appeals, faster note-writing — is a symptom, not a cure. We went deep and fast on the workflows where the money lives, which are the workflows our payer infrastructure forces clinicians to spend their evenings on. That work is valuable, and it is not patient-facing. The places where AI could actually move outcomes — secondary fracture prevention, fall prevention, post-op care navigation, osteoporosis treatment rates that sit around 20% after a fragility fracture [https://www.bonehealthandosteoporosis.org/] when the evidence base for treatment is overwhelming — are still under-resourced. Trust and co-design with clinicians is the unlock. OpenAI has not earned it yet for clinical use. Doximity and OpenEvidence have, in different ways, by being physician-forward from day one. That posture is not optional going forward — it is the moat. The path from clinical intelligence in your pocket to democratized, evidence-based care that actually moves quality and outcomes runs straight through clinicians willing to show up and iterate. That is the dream. We are not there. We can get there. Christian Péan, MD, MHS, is an orthopedic trauma surgeon in Durham, North Carolina. He is core faculty at the Duke-Margolis Institute for Health Policy and CEO and co-founder of RevelAi Health [https://revelaihealth.com/], an AI care management platform for value-based care. Opinions are his own. 🔒 For paid subscribers — the full demo and the operator’s notes The complete screen-share from the morning’s livestream. Side-by-side queries across all three tools, the Deep Consult walkthrough, the prior authorization generation, the acetabular fracture surgical-plan comparison, the live multimodal handoff into a branded HTML committee deck, the connector detour inside Claude, and the clinical trials map I discovered on stage.

Founders Coffee with Techy Surgeon. RevelAi CTO Hadi Javeed, Claude Opus 4.7, and CMS Access

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Making Images and Powerpoints with AI

Thanks to everyone who joined this morning to tune in. I have below linked to my other techy flows, and please stay tuned for the follow-up article with a walkthrough of shorter clips of how you can use AI to make images and PowerPoints. Techy Surgeon is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. https://techysurgeon.substack.com/t/techy-flows This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit techysurgeon.substack.com/subscribe [https://techysurgeon.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_2]