A comparison of three methods in categorizing functional status to predict hospital readmission across post-acute care

Abstract

Background Methods used to categorize functional status to predict health outcomes across post-acute care settings vary significantly. Objectives We compared three methods that categorize functional status to predict 30-day and 90-day hospital readmission across inpatient rehabilitation facilities (IRF), skilled nursing facilities (SNF) and home health agencies (HHA). Research design Retrospective analysis of 2013–2014 Medicare claims data (N = 740,530). Data were randomly split into two subsets using a 1:1 ratio. We used half of the cohort (development subset) to develop functional status categories for three methods, and then used the rest (testing subset) to compare outcome prediction. Three methods to generate functional categories were labeled as: Method I, percentile based on proportional distribution; Method II, percentile based on change score distribution; and Method III, functional staging categories based on Rasch person strata. We used six differentiation and classification statistics to determine the optimal method of generating functional categories. Setting IRF, SNF and HHA. Subjects We included 130,670 (17.7%) Medicare beneficiaries with stroke, 498,576 (67.3%) with lower extremity joint replacement and 111,284 (15.0%) with hip and femur fracture. Measures Unplanned 30-day and 90-day hospital readmission. Results For all impairment conditions, Method III best predicted 30-day and 90-day hospital readmission. However, we observed overlapping confidence intervals among some comparisons of three methods. The bootstrapping of 30-day and 90-day hospital readmission predictive models showed the area under curve for Method III was statistically significantly higher than both Method I and Method II (all paired-comparisons, p