Developing and Validating a Model for Detecting Longitudinal Inconsistencies in the Electronic Problem List.
Journal
AMIA ... Annual Symposium proceedings. AMIA Symposium
ISSN: 1942-597X
Titre abrégé: AMIA Annu Symp Proc
Pays: United States
ID NLM: 101209213
Informations de publication
Date de publication:
2020
2020
Historique:
entrez:
3
5
2021
pubmed:
4
5
2021
medline:
5
6
2021
Statut:
epublish
Résumé
Clinicians from different care settings can distort the problem list from conveying a patient's actual health status, affecting quality and patient safety. To measure this effect, a reference standard was built to derive a problem-list based model. Real-world problem lists were used to derive an ideal categorization cutoff score. The model was tested against patient records to categorize problem lists as either having longitudinal inconsistencies or not. The model was able to successfully categorize these events with ~87% accuracy, ~83% sensitivity, and ~89% specificity. This new model can be used to quantify intervention effects, can be reported in problem list studies, and can be used to measure problem list changes based on policy, workflow, or system changes.
Types de publication
Journal Article
Validation Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
563-572Informations de copyright
©2020 AMIA - All rights reserved.
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