Maximizing efficiency in emergency care: triple interventions to minimize left without being seen: An observational study.

ABSTRACT: Left without being seen (LWBS) is a quality care metric associated with patient-centered outcomes. The risks affecting LWBS are complex and interventions targeting certain risks have diverse effects. We aimed to use different artificial intelligence and machine learning (AI/ML) algorithms to identify the risks affecting LWBS, implement triple interventions specifically targeted at such risks, and compare daily LWBS rate changes before and after the intervention. This is a retrospective observational study. Single urban Emergency Department (ED) daily throughput data from March 1, 2019, to February 28, 2023, were used for AI/ML model prediction. Model performance including accuracy, recall, precision, F1 score, and area under the receiver operating characteristics (AUC) were reported. The t

SUBMITTER: Kirby JJ 

PROVIDER: S-EPMC11688038 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature