Using Phenotypic Data from the Electronic Health Record (EHR) to Predict Discharge Destination: A Predictive Model based on A Single-Center Retrospective Cohort

Abstract

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 hours 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 derive the model from the derivation cohort of the available records. A secondary analysis was then conducted to evaluate 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), taking more home medications (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 clinical factors and risk assessments has excellent model performance in predicting discharge to a PAC facility.