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

Part of paid clinical trials in San Diego, California.

Sponsor
University of California, San Diego
Study ID
NCT07581340
Status
Completed

Conditions

  • Sepsis

Eligibility Criteria

Sex
ALL
Age
18 Years - N/A
Healthy Volunteers
Not accepted

Interventions

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

Study Details

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.

Key Dates

Start date
Dec 1, 2024
Status verified
Oct 2024
Primary completion
Aug 1, 2025
Completion
Dec 12, 2025

Study Design

Enrollment
66 participants (actual)
Allocation
RANDOMIZED
Intervention model
SINGLE_GROUP
Primary purpose
OTHER

Arms

  • Experimental: Intervention group
    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.
  • No Intervention: Control group
    Participants in the control arm receive standard sepsis quality feedback processes without real-time augmentation. This is much less than the intervention group and typically 3-4 months after a particular interaction.

Primary Outcome Measure

SEP-1 Compliance [ Time Frame: This was assessed from time of the event (e.g., development of severe sepsis / septic shock) up to 1 month after the event. ]

Locations (1)

FacilityCityStateZIPSite coordinators
UC San Diego HealthSan DiegoCalifornia92103-1911-

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By research site
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