Capturing Concerns about Patient Deterioration in Narrative Documentation in Home Healthcare.
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:
2022
2022
Historique:
medline:
3
5
2023
pubmed:
2
5
2023
entrez:
2
5
2023
Statut:
epublish
Résumé
Home healthcare (HHC) agencies provide care to more than 3.4 million adults per year. There is value in studying HHC narrative notes to identify patients at risk for deterioration. This study aimed to build machine learning algorithms to identify "concerning" narrative notes of HHC patients and identify emerging themes. Six algorithms were applied to narrative notes (n = 4,000) from a HHC agency to classify notes as either "concerning" or "not concerning." Topic modeling using Latent Dirichlet Allocation bag of words was conducted to identify emerging themes from the concerning notes. Gradient Boosted Trees demonstrated the best performance with a F-score = 0.74 and AUC = 0.96. Emerging themes were related to patient-clinician communication, HHC services provided, gait challenges, mobility concerns, wounds, and caregivers. Most themes have been cited by previous literature as increasing risk for adverse events. In the future, such algorithms can support early identification of patients at risk for deterioration.
Types de publication
Journal Article
Research Support, U.S. Gov't, P.H.S.
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
552-559Subventions
Organisme : AHRQ HHS
ID : R01 HS027742
Pays : United States
Organisme : NINR NIH HHS
ID : T32 NR007969
Pays : United States
Organisme : NINR NIH HHS
ID : F31 NR019919
Pays : United States
Informations de copyright
©2022 AMIA - All rights reserved.
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