NDLI: Using phenotypic data from the Electronic Health Record (EHR) to predict discharge

Content Provider	Springer Nature : BioMed Central
Author	Bhatia, Monisha C. Wanderer, Jonathan P. Li, Gen Ehrenfeld, Jesse M. Vasilevskis, Eduard E.
Abstract	Background Timely discharge to post-acute care (PAC) settings, such as skilled nursing facilities, requires early identification of eligible patients. We sought to develop and internally validate a model which predicts a patient’s likelihood of requiring PAC based on information obtained in the first 24 h of hospitalization. Methods This was a retrospective observational cohort study. We collected clinical data and commonly used nursing assessments from the electronic health record (EHR) for all adult inpatient admissions at our academic tertiary care center from September 1, 2017 to August 1, 2018. We performed a multivariable logistic regression to develop the model from the derivation cohort of the available records. We then evaluated the capability of the model to predict discharge destination on an internal validation cohort. Results Age (adjusted odds ratio [AOR], 1.04 [per year]; 95% Confidence Interval [CI], 1.03 to 1.04), admission to the intensive care unit (AOR, 1.51; 95% CI, 1.27 to 1.79), admission from the emergency department (AOR, 1.53; 95% CI, 1.31 to 1.78), more home medication prescriptions (AOR, 1.06 [per medication count increase]; 95% CI 1.05 to 1.07), and higher Morse fall risk scores at admission (AOR, 1.03 [per unit increase]; 95% CI 1.02 to 1.03) were independently associated with higher likelihood of being discharged to PAC facility. The c-statistic of the model derived from the primary analysis was 0.875, and the model predicted the correct discharge destination in 81.2% of the validation cases. Conclusions A model that utilizes baseline clinical factors and risk assessments has excellent model performance in predicting discharge to a PAC facility.
Related Links	https://bmcgeriatr.biomedcentral.com/counter/pdf/10.1186/s12877-023-04147-y.pdf
Ending Page	9
Page Count	9
Starting Page	1
File Format	HTM / HTML
ISSN	14712318
DOI	10.1186/s12877-023-04147-y
Journal	BMC Geriatrics
Issue Number	1
Volume Number	23
Language	English
Publisher	BioMed Central
Publisher Date	2023-07-11
Access Restriction	Open
Subject Keyword	Geriatrics Gerontology Aging Rehabilitation Post-acute care Prediction models Frailty Functional status Health systems Geriatrics/Gerontology
Content Type	Text
Resource Type	Article
Subject	Geriatrics and Gerontology
Journal Impact Factor	3.4/2023
5-Year Journal Impact Factor	4.1/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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