Predicting 30-Day Readmissions: Evidence From a Small Rural Psychiatric Hospital.
Journal
Journal of psychiatric practice
ISSN: 1538-1145
Titre abrégé: J Psychiatr Pract
Pays: United States
ID NLM: 100901141
Informations de publication
Date de publication:
16 09 2021
16 09 2021
Historique:
entrez:
16
9
2021
pubmed:
17
9
2021
medline:
3
11
2021
Statut:
epublish
Résumé
To improve quality of care and patient outcomes, and to reduce costs, hospitals in the United States are trying to mitigate readmissions that are potentially avoidable. By identifying high-risk patients, hospitals may be able to proactively adapt treatment and discharge planning to reduce the likelihood of readmission. Our objective in this study was to derive and validate a predictive model of 30-day readmissions for a small rural psychiatric hospital in the northeast. However, this model can be adapted by other rural psychiatric hospitals-a context that has been understudied in the literature. Our sample consisted of 1912 adult inpatients (1281 in the derivation cohort and 631 in the validation cohort), who were admitted between August 1, 2014, and July 31, 2016. We used deidentified data from the hospital's electronic medical record, including physician orders and discharge summaries. These data were merged with community-level variables that reflected the availability of care in the patients' zip codes. We first considered the correlates of 30-day readmission in a regression framework. We found that the probability of readmission increased with the number of previous admissions (vs. no readmissions). Moreover, the probability of readmission was much higher for patients with a depressive disorder (vs. no depressive disorder), with another mood disorder (vs. no other mood disorder), and/or with a psychotic disorder (vs. no psychotic disorder). We used these associations to derive a predictive model, in which we used the regression coefficients to construct a score for each patient. We then estimated the predicted probability of 30-day readmission on the basis of that score. After validating the model, we discuss the implications for clinical practice and the limitations of our approach.
Identifiants
pubmed: 34529601
doi: 10.1097/PRA.0000000000000574
pii: 00131746-202109000-00002
doi:
Types de publication
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
346-360Informations de copyright
Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.
Déclaration de conflit d'intérêts
A.D. recognizes support from the USDA National Institute of Food and Agriculture under Hatch project 1016011. B.S., J.S., and S.O. are employed by the hospital studied in this paper. J.B. declares no conflicts of interest.
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