Predicting avoidable hospital events in Maryland
Morgan Henderson, Fei Han, Chad Perman, Howard Haft, Ian Stockwell
Abstract
OBJECTIVE: To develop and validate a prediction model of avoidable hospital events among Medicare fee-for-service (FFS) beneficiaries in Maryland. DATA SOURCES: Medicare FFS claims from Maryland from 2017 to 2020 and other publicly available ZIP code-level data sets. STUDY DESIGN: Multivariable logistic regression models were used to estimate the relationship between a variety of risk factors and future avoidable hospital events. The predictive power of the resulting risk scores was gauged using a concentration curve. DATA COLLECTION/EXTRACTION METHODS: One hundred and ninety-eight individual- and ZIP code-level risk factors were used to create an analytic person-month data set of over 11.6 million person-month observations. PRINCIPAL FINDINGS: We included 198 risk factors for the model based on the results of a targeted literature review, both at the individual and neighborhood levels. These risk factors span six domains as follows: diagnoses, pharmacy utilization, procedure history, prior utilization, social determinants of health, and demographic information. Feature selection retained 73 highly statistically significant risk factors (p < 0.0012) in the primary model. Risk scores were estimated for each individual in the cohort, and, for scores released in April 2020, the top 10% riskiest individuals in the cohort account for 48.7% of avoidable hospital events in the following month. These scores significantly outperform the Centers for Medicare & Medicaid Services hierarchical condition category risk scores in terms of predictive power. CONCLUSIONS: A risk prediction model based on standard administrative claims data can identify individuals at risk of incurring a future avoidable hospital event with good accuracy.