Medication Safety


Identifying Sepsis Phenotypes Associated with Antibiotic-Resistant Pathogens Using Large Language Models and Machine Learning

Description

This research uses large language models and machine learning to retrospectively analyze electronic health records of patients with suspected sepsis and identify patterns in treatment outcomes, with the goal of shaping future clinical guidelines that help doctors select the most effective antibiotics for each patient, reduce unnecessary use of broad-spectrum antibiotics, lower the risks of drug resistance, and ultimately improve patient outcomes.

Grant Number
K08 HS030118
Principal Investigator(s)

Scaling E.Q.U.I.P.P.E.D. Clinical Decision Support

Description

This research successfully adapted and evaluated scaling of the Enhancing Quality of Prescribing Practices for Older Adults Discharged from the Emergency Department medication safety program to an additional commercial electronic health record and added additional sites, finding a significant reduction in potentially inappropriate medication prescribing in the emergency department setting. 

Grant Number
R18 HS026877
Principal Investigator(s)