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Semantically oriented EHR navigation with a patient specific knowledge base and a clinical context ontology.
Colicchio, Tiago K; Osborne, John D; Do Rosario, Clementino V; Anand, Ankit; Timkovich, Nicholas A; Wyatt, Matthew C; Cimino, James J.
Afiliación
  • Colicchio TK; Informatics Institute, University of Alabama at Birmingham.
  • Osborne JD; Informatics Institute, University of Alabama at Birmingham.
  • Do Rosario CV; Informatics Institute, University of Alabama at Birmingham.
  • Anand A; Informatics Institute, University of Alabama at Birmingham.
  • Timkovich NA; Informatics Institute, University of Alabama at Birmingham.
  • Wyatt MC; Informatics Institute, University of Alabama at Birmingham.
  • Cimino JJ; Informatics Institute, University of Alabama at Birmingham.
AMIA Annu Symp Proc ; 2023: 309-318, 2023.
Article en En | MEDLINE | ID: mdl-38222434
ABSTRACT
Widespread adoption of electronic health records (EHR) in the U.S. has been followed by unintended consequences, overexposing clinicians to widely reported EHR limitations. As an attempt to fixing the EHR, we propose the use of a clinical context ontology (CCO), applied to turn implicit contextual statements into formally represented data in the form of concept-relationship-concept tuples. These tuples form what we call a patient specific knowledge base (PSKB), a collection of formally defined tuples containing facts about the patient's care context. We report the process to create a CCO, which guides annotation of structured and narrative patient data to produce a PSKB. We also present an application of our PSKB using real patient data displayed on a semantically oriented patient summary to improve EHR navigation. Our approach can potentially save precious time spent by clinicians using today's EHRs, by showing a chronological view of the patient's record along with contextual statements needed for care decisions with minimum effort. We propose several other applications of a PSKB to improve multiple EHR functions to guide future research.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Contexto en salud: 1_ASSA2030 Problema de salud: 1_sistemas_informacao_saude Asunto principal: Narración / Registros Electrónicos de Salud Tipo de estudio: Prognostic_studies / Qualitative_research Límite: Humans Idioma: En Revista: AMIA Annu Symp Proc Asunto de la revista: INFORMATICA MEDICA Año: 2023 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Contexto en salud: 1_ASSA2030 Problema de salud: 1_sistemas_informacao_saude Asunto principal: Narración / Registros Electrónicos de Salud Tipo de estudio: Prognostic_studies / Qualitative_research Límite: Humans Idioma: En Revista: AMIA Annu Symp Proc Asunto de la revista: INFORMATICA MEDICA Año: 2023 Tipo del documento: Article
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