Predictive models for 30-day patient readmissions in a small community hospital
Lovejoy, Matthew Walter
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Presently, national healthcare initiatives have a strong emphasis on improving patient quality of care through a reduction in patient readmissions. Current federal regulations created through the Patient Protection and Affordable Care Act (PPACA); focus on the reduction in readmissions to improve patient quality of care (Stone & Hoffman, 2010). This legislation mandates decreased reimbursement for services if a facility has high 30-day patient readmissions related to the core measures Congestive Heart Failure (CHF), Acute Myocardial Infarction (AMI) and Pneumonia (PNM). This research focuses on building predictive models to aid Bozeman Deaconess Health Services (BDHS), a small community hospital, reduce their readmission rates. Assistance was performed through identification of patient characteristics influencing patient readmission risk, along with advanced statistical regression techniques used to develop readmission risk prediction models. Potential predictor variables and prediction models were obtained through retrospective analysis of patient readmission data from BDHS during January 2009 through December 2010. For increased prediction accuracy seven separate readmission dataset types were developed: General population, and ICD-9 code related populations for AMI, CHF, PNM, Alcoholic Psychoses (291), Cardiac Dysrhythmias (427) and Disorders of the Function of the Stomach (536). For the greatest benefit from readmission reduction, analysis focused on readmissions categorized as Potentially Preventable Readmissions (PPR); defined as unplanned, medically related readmissions within 30-days of a patient's previous inpatient visit. General exploratory analysis was performed on the PPR patient data to discover patterns which may indicate certain variables as good predictors of patient readmission risk. The prediction model methods compared were binary logistic regression, and multivariate adaptive regression splines (MARS). Usable binary logistic regression models for 536 (Nagelkerke R 2=0.676) and CHF (Nagelkerke R 2=0.974) were achieved. MARS developed usable models for 427 (Naïve Adj. R 2=0.63288), 536 (Naïve Adj. R 2=0.77395), AMI (Naïve Adj. R 2=0.76705), CHF (Naïve Adj. R 2=0.99385) and PNM (Naïve Adj. R 2=0.82615). Comparison of the modeling methods suggest MARS is more accurate at developing usable prediction models, however a tradeoff between model complexity and predictability is present. The usable readmission risk prediction models developed for BDHS will aid BDHS in reducing their readmissions rates, consequently improving patient quality of care.