Importance: Hospital quality reporting remains a manual, costly process with critical limitations as a mechanism to improve care outcomes. Objective: To assess whether near-real-time quality measurement, enabled by large language models (LLMs), can improve quality performance as measured by the Centers for Medicare median age, 64.3 IQR, 51.1-75.7 years; 171 56.8% male; 52 17.3% with chronic kidney disease; 52 17.3% with chronic heart failure) who met CMS inclusion criteria for SEP-1. Physicians in the control group had a SEP-1 compliance rate of 70.1%, while those in the intervention group had a rate of 82.9%. Assignment to the intervention group resulted in a 13.0% absolute improvement in SEP-1 compliance (95% CI, 2.5%-23.4%; odds ratio, 2.10 95% CI, 1.15-3.81; P = .02) in the mixed-effects model. The largest difference between the intervention group and control group was in noncompletion of the 30-mL/kg fluid bolus component (3 of 180 1.7% vs 16 of 121 13.2%), a documentation-sensitive component of the quality measure. Agreement between LLM determination and expert review was 92%. No significant differences existed in intensive care unit admissions or 30-day mortality. Conclusions and Relevance: In this cluster randomized trial of artificial intelligence (AI)-enabled medical record abstraction for sepsis care, rapid assessment of SEP-1 performance and targeted feedback improved overall compliance with the measure. AI-driven quality clinical integration may address limitations in existing hospital quality reporting and better support a learning health system. Trial Registration: ClinicalTrials.gov Identifier: NCT07581340.
Boussina et al. (Thu,) studied this question.
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