Using AI to Improve Sepsis Quality of Care in the Emergency Department

Study Summary

Sepsis is a life-threatening condition caused by the body's response to infection and is a leading cause of death worldwide. Hospitals use a complex quality measure called SEP-1 to track whether patients with severe sepsis or septic shock receive recommended care, such as timely antibiotics, fluids, and laboratory testing. However, evaluating SEP-1 is difficult. It requires manual review of medical records, is time-consuming and expensive, and typically provides feedback to clinicians months after care is delivered. This delay limits the ability to improve care in real time. This study tested whether artificial intelligence (AI), specifically a type of system called a large language model (LLM), could improve the quality of sepsis care by providing faster and more detailed feedback to physicians. The study was conducted at two emergency departments within a large academic health system. Sixty-six attending physicians were randomly assigned to one of two groups. In the intervention group, the AI system reviewed each patient's medical record at the time of hospital discharge and determined whether SEP-1 care standards were met. Physicians then received near real-time, individualized feedback about their performance, including specific areas for improvement. In the control group, physicians received standard feedback based on a small sample of cases reviewed months later using traditional methods.

Primary Outcome

SEP-1 (the Severe Sepsis and Septic Shock Early Management Bundle) is a quality measure developed by the Centers for Medicare \& Medicaid Services that evaluates whether patients with sepsis receive a set of time-sensitive interventions and diagnostic tests. It is a binary process measure (e.g., either a "pass" or "fail").

Interventions

• BEHAVIORAL Near-real time automated feedback on SEP-1 performance