Predicting avoidable hospital events in Maryland
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Author/Creator
Author/Creator ORCID
Date
2021-10-14
Type of Work
Department
Program
Citation of Original Publication
Henderson, M, Han, F, Perman, C, Haft, H, Stockwell, I. "Predicting avoidable hospital events in Maryland." Health Serv Res. 2022; 57( 1): 192- 199. doi:10.1111/1475-6773.13891.
Rights
This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Public Domain Mark 1.0
Public Domain Mark 1.0
Subjects
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.