Electronic Health Record Optimization for Artificial Intelligence.

Artificial intelligence Clinical decision support Clinical laboratory Electronic health record

Journal

Clinics in laboratory medicine
ISSN: 1557-9832
Titre abrégé: Clin Lab Med
Pays: United States
ID NLM: 8100174

Informations de publication

Date de publication:
03 2023
Historique:
entrez: 10 2 2023
pubmed: 11 2 2023
medline: 15 2 2023
Statut: ppublish

Résumé

Laboratory clinical decision support (CDS) typically relies on data from the electronic health record (EHR). The implementation of a sustainable, effective laboratory CDS program requires a commitment to standardization and harmonization of key EHR data elements that are the foundation of laboratory CDS. The direct use of artificial intelligence algorithms in CDS programs will be limited unless key elements of the EHR are structured. The identification, curation, maintenance, and preprocessing steps necessary to implement robust laboratory-based algorithms must account for the heterogeneity of data present in a typical EHR.

Identifiants

pubmed: 36764806
pii: S0272-2712(22)00061-0
doi: 10.1016/j.cll.2022.09.003
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

17-28

Informations de copyright

Copyright © 2022 Elsevier Inc. All rights reserved.

Auteurs

Anand S Dighe (AS)

Department of Pathology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA 02114-2696, USA. Electronic address: asdighe@partners.org.

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Classifications MeSH