Challenges in Predicting Discharge Disposition for Trauma and Emergency General Surgery Patients.
Discharge planning
Emergency general surgery
Frailty
Health services research
Trauma surgery
Journal
The Journal of surgical research
ISSN: 1095-8673
Titre abrégé: J Surg Res
Pays: United States
ID NLM: 0376340
Informations de publication
Date de publication:
09 2021
09 2021
Historique:
received:
11
09
2020
revised:
02
03
2021
accepted:
10
03
2021
pubmed:
9
5
2021
medline:
28
9
2021
entrez:
8
5
2021
Statut:
ppublish
Résumé
Changes in discharge disposition and delays in discharge negatively impact the patient and hospital system. Our objectives were Discharge dispositions and barriers to discharge for 200 TEGS patients were predicted individually by members of the multidisciplinary TEGS team within 24 h of patient admission. Univariate analyses and multivariable logistic least absolute shrinkage and selection operator regressions determined the associations between patient characteristics and correct predictions. A total of 1,498 predictions of discharge disposition were made by the multidisciplinary TEGS team for 200 TEGS patients. Providers correctly predicted 74% of discharge dispositions. Prediction accuracy was not associated with clinical experience or job title. Incorrect predictions were independently associated with older age (OR 0.98; P < 0.001), trauma admission as compared to emergency general surgery (OR 0.33; P < 0.001), higher Injury Severity Scores (OR 0.96; P < 0.001), longer lengths of stay (OR 0.90; P < 0.001), frailty (OR 0.43; P = 0.001), ICU admission (OR 0.54; P < 0.001), and higher Acute Physiology and Chronic Health Evaluation II scores (OR 0.94; P = 0.006). The TEGS team can accurately predict the majority of discharge dispositions. Patients with risk factors for unpredictable dispositions should be flagged to better allocate appropriate resources and more intensively plan their discharges.
Sections du résumé
BACKGROUND
Changes in discharge disposition and delays in discharge negatively impact the patient and hospital system. Our objectives were
METHODS
Discharge dispositions and barriers to discharge for 200 TEGS patients were predicted individually by members of the multidisciplinary TEGS team within 24 h of patient admission. Univariate analyses and multivariable logistic least absolute shrinkage and selection operator regressions determined the associations between patient characteristics and correct predictions.
RESULTS
A total of 1,498 predictions of discharge disposition were made by the multidisciplinary TEGS team for 200 TEGS patients. Providers correctly predicted 74% of discharge dispositions. Prediction accuracy was not associated with clinical experience or job title. Incorrect predictions were independently associated with older age (OR 0.98; P < 0.001), trauma admission as compared to emergency general surgery (OR 0.33; P < 0.001), higher Injury Severity Scores (OR 0.96; P < 0.001), longer lengths of stay (OR 0.90; P < 0.001), frailty (OR 0.43; P = 0.001), ICU admission (OR 0.54; P < 0.001), and higher Acute Physiology and Chronic Health Evaluation II scores (OR 0.94; P = 0.006).
CONCLUSION
The TEGS team can accurately predict the majority of discharge dispositions. Patients with risk factors for unpredictable dispositions should be flagged to better allocate appropriate resources and more intensively plan their discharges.
Identifiants
pubmed: 33964638
pii: S0022-4804(21)00150-5
doi: 10.1016/j.jss.2021.03.014
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
278-288Informations de copyright
Copyright © 2021. Published by Elsevier Inc.