Stanford Physician-Informaticists Debrief Three Clinical AI Pilots That Scaled or Stopped

Stanford Health Care's fifth computational medicine colloquium, "From Bits to Bedside: Three Clinical AI Pilots that Scaled - or Stopped," took place Thursday, October 15, 12:00-1:00pm PT, focusing on what happens after a clinical AI pilot: four physician-informaticists broke down three AI tools they built and deployed — surgical co-management risk screening, identification of duplicate low-value lab orders, and AI-generated discharge summaries — covering development, pilot workflows, outcomes, and what actually happened in real-world deployment, with links to three published studies.

Confirmed

• The event could be attended in person at Palo Alto's Tapao Hall (lunch provided) or via livestream (Webinar ID 978 8759 6012, passcode 420642).
• All four speakers are physician-informaticists: Stephen Ma (a clinical informatics hospitalist with an electrical engineering background who built patient-derived cardiac disease models during his MD/PhD and now leads implementation and evaluation of ambient AI documentation assistants, clinician-facing analytics tools, and ML workflows); April Liang (an internist and Chief Medical Information Officer for Hospital Medicine, whose prior work includes ML-driven EHR decision-support tools targeting lab overuse and measuring how ambient AI documentation assistants affect documentation time); Natasha Steele (MD, MPH, working at the intersection of health system operations and clinical informatics, focused on clinical workflows, patient experience, and sustainable health system change); the fourth speaker was not detailed in the post.
• The talks covered three pilot areas in order — surgical triage, labs, and discharge summary generation — with Stanford ARISE network updates plus links to talk recordings and the announcement mailing list.

Why it matters

• Poster jonc101x noted that Q&A sessions, benchmarks, and agent simulations are still not the real world, and stories of clinical AI actually landing in production are rarely told — pushing a good idea into a real clinical environment means grinding through dozens of stakeholder meetings and endless follow-ups. The talk is a useful reference for anyone tracking how LLMs are actually being deployed in hospital clinical workflows.

2026-10-10 ~ 2026-10-10 · 13 related posts

• Stanford Colloquium Dissects Three Clinical AI Pilots — Which Scaled and Which Stopped — jonc101x · 2026-10-10
• [source] Stanford colloquium: three clinical AI pilots that scaled or stopped — jonc101x · 2026-10-10
• Why clinical AI deployment stories are rare: dozens of stakeholder meetings — jonc101x · 2026-10-10
• [source] Stanford doctors detail three deployed clinical AI tools, post-pilot lessons — jonc101x · 2026-10-10
• Stanford Health Care physicians recount what happened after AI tool pilots — jonc101x · 2026-10-10
• Colloquium to cover development, pilot, and real-world results of clinical AI — jonc101x · 2026-10-10
• April Liang: ML decision support for lab overuse, AI scribe timing studies — jonc101x · 2026-10-10
• Natasha Steele works on clinician workflows and health-system change — jonc101x · 2026-10-10
• Stephen Ma: hospitalist implementing ambient AI scribes and ML workflows — jonc101x · 2026-10-10
• Stanford colloquium set for Oct 15, in person in Palo Alto or livestream — jonc101x · 2026-10-10
• Stanford team lists published work: triage, labs, discharge summaries — jonc101x · 2026-10-10
• Stanford computational medicine series wraps: surgical triage, labs, discharge summaries — jonc101x · 2026-10-10
• [source] Stanford Clinical AI Colloquium Livestream Details for Thursday Session — jonc101x · 2026-10-10