Large language model triaging of simulated nephrology patient inbox messages.

ABSTRACT:

Background

Efficient triage of patient communications is crucial for timely medical attention and improved care. This study evaluates ChatGPT's accuracy in categorizing nephrology patient inbox messages, assessing its potential in outpatient settings.

Methods

One hundred and fifty simulated patient inbox messages were created based on cases typically encountered in everyday practice at a nephrology outpatient clinic. These messages were triaged as non-urgent, urgent, and emergent by two nephrologists. The messages were then submitted to ChatGPT-4 for independent triage into the same categories. The inquiry process was performed twice with a two-week period in between. ChatGPT responses were graded as correct (agreement with physicians), overestimation (higher priority), or u

SUBMITTER: Pham JH 

PROVIDER: S-EPMC11417033 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature