Former Geisinger CEO says fire everyone 🔥, Pharma buys the AI search bar 💊, SimpliFed bets on maternal health 🤱

The former CEO of a 27,500-person health system just said the quiet part out loud: most of those jobs should be replaced by AI.

Glenn Steele ran Geisinger for 14 years — 27,500 employees, 1,800 physicians, one of the most respected integrated health systems in the country. Yesterday he wrote that health systems need to replace “huge numbers” of people with AI within five years. Not a think-tank whitepaper. Not a vendor pitch deck. A former CEO, naming the timeline, in STAT. When the person who managed the workforce says the workforce is the problem, clinician-builders need to decide: are you building the tools that make the transition humane, or waiting to see what happens to your department?

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🔬 The Big Thing

Former Geisinger CEO: Health Systems Must Replace “Huge Numbers” of People with AI

Glenn Steele Jr., who led Geisinger Health for 14 years, published an op-ed in STAT arguing that U.S. health systems must deploy autonomous AI to replace large portions of their administrative workforce — and they have about five years to do it. His core observation is structural: at Geisinger, the revenue cycle department that processed bills and reconciled data employed more people than the physician workforce. He says that ratio exists at every health system in America and has gotten dramatically worse over the past two decades.

Steele frames this as survival, not optimization. Hospital margins are thin. Administrative bloat is the structural inefficiency that AI can actually address. He’s not talking about replacing clinicians — he’s talking about the 25,000 non-physician jobs at a system like Geisinger and asking what that org chart looks like in 2031.

😤 Haters

“A retired CEO calling for job cuts from the safety of an op-ed is easy.” It is. Steele isn’t the one who has to manage the layoffs, retrain the workforce, or deal with the union negotiations. But his credibility comes from having run the system. He knows what the P&L looks like. He knows which departments are biggest. The messenger might be comfortable, but the math is real.

“AI can’t replace revenue cycle — it’s too messy, too many edge cases.” Revenue cycle is exactly the kind of work where AI excels: high-volume, rules-based, error-tolerant enough to iterate. Claims processing, coding, denial management — these are pattern-matching problems with structured inputs. The edge cases are real, but the 80% of routine work is automatable now.

“This just means worse patient experience — fewer humans to call, more chatbots.” Maybe. But the current experience isn’t great either. If you’ve ever spent 45 minutes on hold with a billing department, you know the human-staffed version has its own failure modes. The question is whether AI-driven billing is worse than understaffed billing.

💡 80/20: If you’re a clinician-builder, the administrative layer is about to get rebuilt. The tools that bridge the gap — appeal generators, documentation assistants tuned to specific payer requirements, coding audit agents — are the highest-value builds right now. Reframe: the back office isn’t disappearing. It’s becoming software. And software needs builders who understand the clinical context.

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📡 Builder’s Radar

Pharma Is Buying the AI Search Bar Your Colleagues Use

Out-of-Pocket’s second installment on pharma advertising through clinical AI tools surfaced a damning historical parallel: Practice Fusion pushed 230 million pharma-sponsored clinical alerts to physicians and paid a $145 million DOJ settlement for it. Now OpenEvidence — used by 757,000+ clinicians — generates revenue through pharma ads served while AI answers load. Gallup data shows a 14-point decline in physician trust since 2021, correlating with increased algorithm adoption in clinical workflows.

😤 Haters

“OpenEvidence is free for doctors — someone has to pay for it.” True. And Google Search is free too. The question isn’t whether ad-supported models exist. It’s whether physicians know the difference between evidence-based results and pharma-influenced results when they’re making prescribing decisions at 2 AM.

“Practice Fusion was different — those were alerts, not search results.” The mechanism differs, the incentive structure is identical: pharma pays to influence clinical decision-making at the point of care. The DOJ didn’t care about the UX pattern. They cared about the influence.

💡 80/20: If you build clinical decision support tools, your business model is a clinical decision. Ad-supported CDS introduces structural conflicts whether you intend it or not. Try: before integrating any “free” clinical AI tool into your workflow, find the revenue model. If it’s pharma-funded, that’s a feature you should disclose to your users.

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SimpliFed Raises $10.8M to Build Virtual Maternal Health Platform

SimpliFed raised a $10.8M oversubscribed Series A to expand from virtual lactation support into a full virtual OB provider for low-risk patients. Led by Morningside and Hesperia Capital, with the AHA Social Impact Fund participating. The company is on track to serve 5% of all U.S. births in 2026 and is in-network with most major commercial plans and Medicaid health plans across several states. Clinical outcomes data: 96.6% of patients breastfed at one week versus a national average of 85.7%.

😤 Haters

“$10M is small for a health tech company trying to serve 5% of U.S. births.” It is. But maternal health is capital-efficient when you’re virtual-first and aligned with payer incentives. The real validation isn’t the round size — it’s that commercial and Medicaid plans are already paying.

“Lactation to full OB is a massive scope expansion.” It is a leap. But the patient relationship already exists, the payer contracts are in place, and the clinical team understands the population. The risk is execution, not market fit.

💡 80/20: Maternal health remains one of the most underbuilt categories in health tech despite clear clinical need and payer willingness to pay. If you’re a clinician-builder looking for a vertical, maternal and postpartum care has fewer incumbents and more measurable outcomes than most. Try: look at your own health system’s maternal readmission rates and ask what a virtual touchpoint at day 3 postpartum would cost to build.

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NPR: AI Is Arriving in Mental Health — and Therapists Are Striking Over It

NPR reported that Kaiser Permanente is evaluating Limbic, a U.K.-based AI company that builds chatbots trained on cognitive behavioral therapy. This comes after 2,400 Kaiser mental health providers in Northern California went on a 24-hour strike, with AI-driven workflow changes as a key issue. Clinicians report that triage screening — previously a 10-15 minute encounter with a licensed clinician — is now conducted by unlicensed operators following scripts.

😤 Haters

“Kaiser therapists are striking about staffing, not AI specifically.” The strike is about both. The AI evaluation is happening in the context of chronic understaffing. When you don’t have enough therapists and you introduce an AI tool that can do intake, the workforce reads that as replacement, not augmentation. Context matters.

“Limbic is a U.K. company — this isn’t a U.S. story.” Kaiser evaluating it makes it a U.S. story. The largest integrated health system in the country is assessing whether AI chatbots can handle mental health intake. What Kaiser does, others follow.

💡 80/20: The mental health workforce shortage is real — and AI tools that extend clinician capacity (not replace clinician judgment) are the builds that will survive the political and regulatory scrutiny. Reframe: if you’re building mental health AI, design it so the therapist is visibly in the loop. The tool that makes the clinician faster wins. The tool that makes the clinician invisible loses.

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🧰 Builder’s Tip

Mindset / Strategy: Build the Transition, Not the Replacement

Glenn Steele says health systems need to replace huge numbers of administrative jobs with AI. UnitedHealth is spending $3 billion to do it. But here’s what neither of them is building: the transition layer. The tools that help a revenue cycle specialist become an AI operations manager. The dashboards that let a billing team audit what the algorithm decided. The training interfaces that bridge “I used to do this manually” to “I now supervise the agent that does it.”

The highest-value builds in the next two years won’t replace humans — they’ll translate between humans and the AI that just took their workflow. If you’re a clinician-builder, that’s your edge. You understand the workflow the AI is automating. Build the tool that makes the handoff safe.

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What are you building this week? Reply and tell me — I read every one.

— Kevin