Your browser doesn't support javascript.
loading
Contextual Variation of Clinical Notes induced by EHR Migration.
Miller, Kurt; Moon, Sungrim; Fu, Sunyang; Liu, Hongfang.
Afiliación
  • Miller K; Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA.
  • Moon S; Bioinformatics and Computational Biology Program, University of Minnesota, Minneapolis, MN, USA.
  • Fu S; Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA.
  • Liu H; Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA.
AMIA Annu Symp Proc ; 2023: 1155-1164, 2023.
Article en En | MEDLINE | ID: mdl-38222426
ABSTRACT
The structure and semantics of clinical notes vary considerably across different Electronic Health Record (EHR) systems, sites, and institutions. Such heterogeneity hampers the portability of natural language processing (NLP) models in extracting information from the text for clinical research or practice. In this study, we evaluate the contextual variation of clinical notes by measuring the semantic and syntactic similarity of the notes of two sets of physicians comprising four medical specialties across EHR migrations at two Mayo Clinic sites. We find significant semantic and syntactic variation imposed by the context of the EHR system and between medical specialties whereas only minor variation is caused by variation of spatial context across sites. Our findings suggest that clinical language models need to account for process differences at the specialty sublanguage level to be generalizable.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Médicos / Registros Electrónicos de Salud Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: AMIA Annu Symp Proc Asunto de la revista: INFORMATICA MEDICA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Médicos / Registros Electrónicos de Salud Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: AMIA Annu Symp Proc Asunto de la revista: INFORMATICA MEDICA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos