Integration of patient experience factors improves readmission prediction.
Journal
Medicine
ISSN: 1536-5964
Titre abrégé: Medicine (Baltimore)
Pays: United States
ID NLM: 2985248R
Informations de publication
Date de publication:
20 Jan 2023
20 Jan 2023
Historique:
entrez:
26
1
2023
pubmed:
27
1
2023
medline:
31
1
2023
Statut:
ppublish
Résumé
Many readmission prediction models have marginal accuracy and are based on clinical and demographic data that exclude patient response data. The objective of this study was to evaluate the accuracy of a 30-day hospital readmission prediction model that incorporates patient response data capturing the patient experience. This was a prospective cohort study of 30-day hospital readmissions. A logistic regression model to predict readmission risk was created using patient responses obtained during interviewer-administered questionnaires as well as demographic and clinical data. Participants (N = 846) were admitted to 2 inpatient adult medicine units at Massachusetts General Hospital from 2012 to 2016. The primary outcome was the accuracy (measured by receiver operating characteristic) of a 30-day readmission risk prediction model. Secondary analyses included a readmission-focused factor analysis of individual versus collective patient experience questions. Of 1754 eligible participants, 846 (48%) were enrolled and 201 (23.8%) had a 30-day readmission. Demographic factors had an accuracy of 0.56 (confidence interval [CI], 0.50-0.62), clinical disease factors had an accuracy of 0.59 (CI, 0.54-0.65), and the patient experience factors had an accuracy of 0.60 (CI, 0.56-0.64). Taken together, their combined accuracy of receiver operating characteristic = 0.78 (CI, 0.74-0.82) was significantly more accurate than these factors were individually. The individual accuracy of patient experience, demographic, and clinical data was relatively poor and consistent with other risk prediction models. The combination of the 3 types of data significantly improved the ability to predict 30-day readmissions. This study suggests that more accurate 30-day readmission risk prediction models can be generated by including information about the patient experience.
Identifiants
pubmed: 36701722
doi: 10.1097/MD.0000000000032632
pii: 00005792-202301200-00026
pmc: PMC9857268
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e32632Subventions
Organisme : NHLBI NIH HHS
ID : K23 HL150287
Pays : United States
Informations de copyright
Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc.
Déclaration de conflit d'intérêts
The authors have no conflicts of interest to disclose.
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