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

NCT07581340 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 66

Last updated 2026-05-12

No results posted yet for this 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.

Conditions

Interventions

BEHAVIORAL

Near-real time automated feedback on SEP-1 performance

Participants in the intervention arm receive near-real-time, individualized feedback on SEP-1 performance generated by a large language model (LLM) that performs automated chart abstraction at the time of emergency department discharge.

Sponsors & Collaborators

Study Design

Allocation
RANDOMIZED
Purpose
OTHER
Masking
NONE
Model
SINGLE_GROUP

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-12-01
Primary Completion
2025-08-01
Completion
2025-12-12

Countries

  • United States

Study Locations

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Entities

Diseases
Companies

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07581340 on ClinicalTrials.gov