Morgan Cheatham, MD

excited to share @BreyerCap's latest healthcare thesis with my partner, @jimihendrixlive. healthcare is at an inflection point. scientific discovery is accelerating, but systems of translation remain structurally stagnant. the result is a widening chasm between what’s possible in principle and what’s practiced. we invest where scientific discovery, clinical necessity, and institutional transformation converge. computation, precision, and prevention are shaping the future of medicine, but only when anchored by economic models that reward long-term value. the next era of human health won’t be inherited. It will be engineered. if this vision resonates, we’d love to connect.

@ThisWeeknAI @ShivdevRao @AbridgeHQ prescient @ShivdevRao

"Physicians can't complete their daily workload in 24 hours." The math of modern medicine is broken. @ShivdevRao explains how @AbridgeHQ is combating physician burnout by offloading documentation, allowing clinicians to reclaim their time. This Week in AI Episode 5 00:00 Shiv Rao live at LAUNCH Festival 2026 01:20 Rural hospitals & the necessity of AI agents 03:33 Designing an AI intake process for primary care 06:01 Solving The 30-Hour Workday & Clerical Burnout 08:25 Misaligned stakeholders in healthcare 11:50 Patients Prefer AI Models Over Average Clinicians 14:27 Moving past the all-knowing doctor 17:01 Automated notes restore human clinical presence 27:29 Advances In Surgical Robotics & AI Precision @Jason @launchfestival

@ColtonOrtolf I like the way @morgancheatham described dissolving the boundary between research and care. The future should be a closed-loop system that gets smarter with every patient.

@IAmSamFin @EvidenceOpen @RareDiseases go Sam! this is awesome!

Excited to share this collaboration between @EvidenceOpen and @RareDiseases that I've been helping in part to spearhead (1/4): rarediseases.org/national-organ…

@tracyzhbme 🙌🏽

Looking forward to reading the thesis! Our industry has too many closed door discussions ;)

the gap between what we can do scientifically and clinically and what we can pay for has never been wider. how we close that gap will define the next era of healthcare. deeply grateful to our speakers and partners for their intellectual generosity. we look forward to hosting our 5th Annual Summit next spring.

last week we hosted Breyer Capital's 4th Annual Healthcare and Life Sciences Summit in collaboration with The University of Texas in Austin. it was a privilege to bring together the full breadth of healthcare and life sciences: from Centers for Medicare and Medicaid Services to Neuralink, from Google Research and Arc Institute to Lilly, from Abridge to UT MD Anderson.

the state of precision medicine was also a central theme, and we will soon share our updated thesis. as therapies become more targeted, the infrastructure required to support them grows more complex by an order of magnitude, and the tension between evidence-based medicine, which asks what works on average, and precision medicine, which asks what works for you, remains unresolved.

our conversations explored AI's renegotiation of foundational assumptions in healthcare: what it means to diagnose, to treat, and to pay for care in a world where the science is moving faster than the systems designed to deliver it. as implementation science becomes the next hard problem, a new divide is emerging: those optimizing what exists and those reimagining what comes next.

At the Breyer Capital/University of Texas AI/Healthcare Summit. With President Jim Davis, Dean of Dell Medical School Claudia Lucchinetti, and MD Anderson President Peter Pisters ⁦@UTAustin⁩ ⁦@DellMedSchool⁩ Terrific two days🙏

Sighting of @zakkohane in Longhorn country 🤠, great to hear Zak speak about our Human Values Project (led by @payal_chandak) at the UT Austin Life Science & Healthcare Innovation Summit. Thank you @morgancheatham @jimihendrixlive @BreyerCap for the invitation to @UTAustin.

one of the greatest public data drops of all time - this is what bringing transparency to healthcare looks like

Special thanks to Brian Gormley for featuring our perspective in today's @WSJ piece on @OpenAI and @AnthropicAI's entry into healthcare: wsj.com/articles/opena…

we expect this dynamic to introduce real friction into buying cycles for point solutions with thin moats. at the same time, it expands opportunity for companies with defensible data assets, deep workflow entrenchment, and true platform potential. @Artera_io and @AtroposHealth are two strong examples from Breyer Capital's portfolio, both highlighted in the article.

there's a new question in every health tech buying cycle that didn't exist 18 months ago: "is this already on the roadmap of our foundation model vendor?"

the TAM for any single shadow service looks small, but the stakes are high and the playbook is highly repeatable. these aren't net-new services, they're services hospitals already pay for but systematically underutilize. huge opportunity to build a horizontal platform that runs early identification and triage logic across all of them.

AI can read nursing notes and clinical data, flag and triage the right patients earlier, and of course capture diagnosis codes hospitals routinely miss. Apply that logic across every shadow service: • Wound Care / Pressure Injuries: early intervention prevents complications and reduces length of stay. also a CMS never event, so hospitals don't get reimbursed when it happens on their watch • Malnutrition / Nutrition: malnutrition slows recovery and worsens outcomes across nearly every disease state. there's also a well-known coding opportunity that boosts case mix index • SLP / Dysphagia Screening: catching aspiration risk early prevents pneumonia, one of the costliest and most dangerous inpatient complications • Palliative Care: early referrals improve patient and family experience and reduce ICU utilization. chronically under-referred despite strong evidence • Medication Reconciliation: catching drug interactions, polypharmacy issues, and renal dosing errors prevents adverse events. also ties directly into readmission penalties under HRRP • Respiratory Therapy: optimizing ventilator weaning and oxygen therapy gets patients out of the ICU faster. respiratory failure as a secondary diagnosis also meaningfully increases DRG weight and is frequently under-coded

there's a common refrain that AI hasn't meaningfully impacted inpatient medicine yet, and i'd generally agree. one area where i expect we'll see real near-term value: optimizing hospital "shadow services."