Predicting physician departure with machine learning on EHR use patterns: A longitudinal cohort from a large multi-specialty ambulatory practice.

Lopez, Kevin; Li, Huan; Paek, Hyung; Williams, Brian; Nath, Bidisha; Melnick, Edward R; Loza, Andrew J

Lopez, Kevin; Li, Huan; Paek, Hyung; Williams, Brian; Nath, Bidisha; Melnick, Edward R; Loza, Andrew J.

Afiliación

Lopez K; Department of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America.
Li H; Department of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America.
Paek H; Computational Biology and Bioinformatics, Yale School of Medicine, New Haven, Connecticut, United States of America.
Williams B; Information Technology Services, Yale New Haven Health, Stratford, Connecticut, United States of America.
Nath B; Northeast Medical Group, Yale New Haven Health, New London, Connecticut, United States of America.
Melnick ER; Northeast Medical Group, Yale New Haven Health, New London, Connecticut, United States of America.
Loza AJ; Department of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America.

PLoS One ; 18(2): e0280251, 2023.

Article en En | MEDLINE | ID: mdl-36724149

Asunto(s)

Medicina; Médicos; Humanos; Registros Electrónicos de Salud; Reorganización del Personal; Aprendizaje Automático

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Médicos / Medicina Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google