Epic's Foundation Model: A Vendor-Built EHR "Event Model" for Operational Triage

Generative Model DevelopmentPredictive Process Outcomes
Journal: Arxiv

Epic has released Curiosity, a foundation model pretrained on 118M patients and 115B events from Epic Cosmos—signaling that major EHR vendors are entering the clinical AI space with vendor-native models. In retrospective testing, the 1B-parameter model outperformed supervised baselines for 30-day readmission (AUC 0.770 vs 0.717) and LOS prediction, but lacks dose/route/frequency tokenization needed for medication-safety CDS.

A Unit Test for LLM Medication Orders—and Weak Spots

Generative Model Development
Journal: Arxiv

MedMatch turns messy medication-order language into six standardized JSON templates plus a 100-order clinician benchmark, giving pharmacy teams a practical “unit test” for LLM ordering pilots and regression checks. On strict exact-match scoring, LLMs reached ~64–84% on oral solids/IV intermittent but fell to 23–43% for oral liquids and 0–18% for titratable infusions—signaling governance value, not autonomous ordering readiness.

When Discharge-Note Style Makes LLM Misinformation Sound True

Predictive External Validation
Journal: Lancet Digit Health

LLMs can “launder” misinformation into credible-sounding clinical prose: across 20 models, false medical claims were accepted 31.7% of the time, rising to 46.1% in discharge-note style text. That matters most to pharmacists reviewing summaries and discharge instructions—especially because authority-framed wording increased acceptance—so grounding, citations, and routine pharmacist verification should be built into workflows.

Stop Silent-Failure CDS: Demand Brier-Scored Uncertainty

Predictive Model DevelopmentPredictive External Validation
Journal: Artificial Intelligence in Medicine

Uncertainty that actually tracks error under dataset shift may be the difference between safe CDS and “silent failure.” In EEG Normal/MCI/Dementia prediction, independently trained multi-run ensembles held up best (AUC ~0.84; Brier ~0.45) while some checkpoint ensembles degraded (AUC ~0.71; Brier ~0.57), pointing pharmacy leaders to demand Brier-based validation and route high-uncertainty cases to pharmacist review.