Is AI Patient Safety 4.0?: From Find-and-Fix to Continuous Intelligence: Toward Care That Needs No Advocate

Three decades after To Err Is Human catalyzed the modern patient safety movement, roughly one in four hospitalized patients still experiences an adverse event, and nearly one in four patients who die or deteriorate in U.S. hospitals does so in the setting of a missed or delayed diagnosis. These figures have prompted an appropriately skeptical question: has patient safety made any real progress? This special article argues that the skepticism mistakes an unfinished agenda for a failed one, and that the safety field has evolved through a recognizable sequence of framings. It also argues that artificial intelligence (AI) presents a "step-change" opportunity to accelerate progress in patient safety. Building on the foundational work of Jeffrey Braithwaite and colleagues, we are beginning to evolve from find-and-fix accounting for failure ("Safety 1.0"), to studying why care succeeds ("Safety 2.0"), to a combined, 360-degree view that examines failure and success together ("Safety 3.0"). None of these framings, however, were built to operate at the speed, scale, and complexity of contemporary care, where a single intensive care unit patient can generate more than 1,000 clinically relevant data points a day, which is several orders of magnitude beyond what unaided human cognition can reliably integrate. This article proposes "Safety 4.0": the disciplined use of AI, deployed across a defined autonomy spectrum and governed by explicit guardrails as continuous safety intelligence that senses risk earlier, synthesizes complexity, predicts deterioration, and reliably integrates care needs. Drawing on evidence organized around the National Academy of Medicine's six aims for healthcare (safety, timeliness, effectiveness, efficiency, equity, and patient-centeredness) together with case examples from sepsis surveillance, autonomous diagnostics and prescribing, among others, the article argues that the ultimate test of progress is not whether a system adopts AI, but whether care becomes reliably safe, effective, and compassionate without depending on a patient having an advocate.