Toward continuous quality observability in medicine

Abstract

Hospitals often detect complications retrospectively via error-susceptible mechanisms including self-reporting, manual abstraction, or billing codes. We propose a paradigm of continuous clinical quality observability: a background, non-interruptive layer where AI agents continuously read the EHR narrative to surface emerging safety signals. Continuous observability may shorten the time from event to detection from weeks to hours or days and reduce manual case-finding, accelerating learning consistent with high-reliability care principles.