Chris Reedy

Proud of my friends Jared Conley, MD, PhD and Mike Senter-Zapata, MD and the entire team behind this important study with Mass General Brigham and Avo. This work meaningfully advances how we think about reducing cognitive burden for clinicians in emergent situations through true digital co-pilot approaches. Emergent care is fundamentally a cognitive challenge. The difficulty goes beyond the medicine itself. It’s the mental bandwidth required to track a deteriorating patient, keep the team aligned, and make the right call under real pressure. What Jared and colleagues explored is a question we should all be asking: How much better could rapid responses be if we systematically offloaded cognitive load to software that guides, clarifies, and standardizes in real time? Their study offers three core principles: 1. Lower cognitive load → higher clinical performance Physicians across training levels reported less stress, more structure, and higher perceived quality of care when using mobile, condition-specific pathways. This is the essence of a digital co-pilot: elevating the clinician’s judgment by reducing decision friction. 2. UX must match clinical experience One of the most insightful findings: PGY-2 residents rated navigation more difficult than senior attendings. The takeaway is clear — interfaces designed for experts can overwhelm novices. A true co-pilot must adapt to the operator’s cognitive state and experience level, especially when seconds matter. 3. Standardization makes care better These digital checklists, modeled after aviation safety, created a shared mental model across teams. In environments where fragmentation can cost lives, standardization becomes a force multiplier. This work goes far beyond rapid responses. It points to what the next generation of clinical support should look like. We’re already seeing parts of this take shape across the system: Avo — guiding clinicians through critical care in real time Bayesian Health — flagging deterioration early enough to change outcomes Aidin — cutting through the administrative fog that slows transfers Koda Health — removing uncertainty around a patient’s wishes before it reaches the bedside Different problems, different settings, but the same underlying idea: when you take cognitive burden off clinicians, the quality of care goes up. The term “co-pilot” only means something if the tool shows up the way clinicians do in tough moments — steady, useful, and out of the way. Jared and the team have produced an example of what that future can look like. Our healthcare system needs a lot more of this kind of work. https://lnkd.in/gWZ5PA5c?

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Many growth-stage medtech companies are hitting the same wall right now: they need senior RA/QA leadership, but not a full-time VP or lead… yet. Over the past few years, I’ve found myself supporting teams in exactly this gap. So I’ve formalized it: I now work with diagnostics, digital health, and SaMD companies as a Fractional RA/QA Executive. Here’s what that looks like in practice: 🔹 MDR/IVDR Submission Leadership Technical documentation architecture, gap assessments, remediation, and full submission strategy for IVDs and SaMD (including Rule 11). 🔹 FDA Compliance & QMS Leadership QMS build-outs, audit readiness, QMSR alignment, and remediation support, especially for companies scaling into regulated markets. 🔹 SaMD Technical Documentation Processes and documentation aligned with IEC 62304, 62366, ISO 14971, cybersecurity expectations, and FDA guidance. 🔹 EQMS Implementation & Scale-Up Hands-on leadership for implementing Arena, Propel, Veeva, SmartSolve, or ZenQMS without slowing engineering momentum. 🔹 Executive-Level Cross-Functional Guidance Practical coaching for engineering, product, quality, and regulatory teams to move quickly and meet global regulatory expectations. Why fractional? Because many companies need senior expertise during critical inflection points — submissions, audits, remediation, or commercialization — long before they need a full-time leader. Fractional support gives teams immediate momentum and clarity without the commitment of a permanent role. If your team is navigating MDR/IVDR transition, preparing for FDA inspections, submissions or QMSR alignment, building SaMD documentation, or scaling a QMS for the first time, I’m always happy to be a resource. Feel free to reach out or connect. Even a quick 15-minute discussion can prevent months of rework.

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Quick hot take on MSO margin % growth coming out of JPM Healthcare. Obviously AI was the #1 discussion point, but margin % was a big topic outside the formal talks -- at the coffee shops, at the in the Four Seasons and Palace lobbies and bars, etc.. TLDR ... MSO margin %s are about to pop 🔥 At Honey Health we are already seeing it ... and it's early innings. And it's not about chatbots, it's the full tilt to autonomous AI, which only ~5% of the industry has adopted. 💵 Labor costs eat 50-60% of practice outlays. Admin alone represents 25% of total US healthcare spending - thats $1.1 trillion annually. Now consider what fully autonomous AI can actually do: - Manual, mind numbing work like fax triage, data fetching, payment posting, etc. are just done without any labor - Prior authorizations that took 45 minutes now take 5 - Eligibility verification runs 24/7 without FTE overhead - Claims scrubbing happens before submission, not after denial - Patient scheduling optimizes itself based on no-show patterns etc. etc. The ROI data is 🤯. Early adopters are seeing 100-200% returns in Year 1, with 3-6 month payback periods. For a PE firm with a 5-7 year hold period, this isnt a nice-to-have. Its a margin expansion strategy that compounds every single month it's live. The MSOs that move first will set the new benchmark for what operational efficiency and profit % looks like ... and will look like 🌟

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Three weeks ago I launched Healthcare M&AI with zero subscribers and a thesis. Here's where things stand: Nearly 100 newsletter subscribers. PE partners and principals. Investment bankers running buy-side and sell-side mandates. Corporate development leaders at PE-backed platforms and health systems. Health tech executives. 60%+ open rates across all newsletters. My most recent issue hit nearly 70%. Discovery calls booked back-to-back for three straight weeks with fund managers at $500M+ funds, vice presidents at Big Four advisory, and senior corp dev professionals at PE-backed platforms. I've never been more certain I'm building the right thing at the right time. The inbound demand is real. The conversations I'm having every day confirm it. The need exists. The timing is now. I'm going to be real with you: the first three weeks have not been smooth. I've fumbled pitches on calls with people I respect. I've volunteered too much information in meetings I wasn't prepared for. I've learned the hard way what "solo founder risk" actually means when you're running sales, delivery, content, legal, and operations all at once. I'll be sharing more of these lessons in the coming weeks. Starting this week, I'm expanding the newsletter to cover four areas: 1. Healthcare M&A market intelligence 2. AI-powered deal workflows 3. Deal execution insights from the operator seat 4. The raw, unfiltered founder journey If you're in healthcare M&A, corporate development, or PE and you want to see how AI is reshaping deal execution in real time, the newsletter is where it all lives. Link in comments.

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Transforming healthcare with AI requires industry. Industry requires capital investment. Capital investment requires return. Return requires revenue. Revenue requires reimbursement. Medicare (and transitively private insurance) reimbursement policy for clinical AI will define the incentives for the technologies we build and deploy over the next decade. Our existing fee for service codes are what the military would call cost-plus contracting. We pay for every nail, screw, and hour of labor and don't care if that was a good and efficient way to build the tank or not. Our CMS Innovation Center director, Abe Sutton, believes cost-plus is the wrong path for the future of American healthcare. ACCESS and VBC models pay for outcomes. When we have AI that can manage heart failure can we link payment to making people healthier? Doing so would unleash the might of American techno-capitalism on exactly the thing we care about in society, but there are devils in the details.

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