Can diverse population characteristics be leveraged in a machine learning pipeline to predict resource intensive healthcare utilization among hospital service areas?

Abstract

Background: Super-utilizers represent approximately 5% of the population in the United States (U.S.) and yet they are responsible for over 50% of healthcare expenditures. Using characteristics of hospital service areas (HSAs) to predict utilization of resource intensive healthcare (RIHC) may offer a novel and actionable tool for identifying super-utilizer segments in the population. Consumer expenditures may offer additional value in predicting RIHC beyond typical population characteristics alone.

Methods: Cross-sectional data from 2017 was extracted from 5 unique sources. The outcome was RIHC and included emergency room (ER) visits, inpatient days, and hospital expenditures, all expressed as log per capita. Candidate predictors from 4 broad groups were used, including demographics, adults and child health characteristics, community characteristics, and consumer expenditures. Candidate predictors were expressed as per capita or per capita percent and were aggregated from zip-codes to HSAs using weighed means. Machine learning approaches (Random Forrest, LASSO) selected important features from nearly 1,000 available candidate predictors and used them to generate 4 distinct models, including non-regularized and LASSO regression, random forest, and gradient boosting. Candidate predictors from the best performing models, for each outcome, were used as independent variables in multiple linear regression models. Relative contribution of variables from each candidate predictor group to regression model fit were calculated.

Results: The median ER visits per capita was 0.482 [IQR:0.351-0.646], the median inpatient days per capita was 0.395 [IQR:0.214-0.806], and the median hospital expenditures per capita was $2,302 [1$,544.70-$3,469.80]. Using 1,106 variables, the test-set coefficient of determination (R²) from the best performing models ranged between 0.184-0.782. The adjusted R² values from multiple linear regression models ranged from 0.311-0.8293. Relative contribution of consumer expenditures to model fit ranged from 23.4-33.6%.

Discussion: Machine learning models predicted RIHC among HSAs using diverse population data, including novel consumer expenditures and provides an innovative tool to predict population-based healthcare utilization and expenditures. Geographic variation in utilization and spending were identified.

Fig. 1

Systematic model development schematic. This approach allowed for evaluation of 4 data inputs, 2 features selection techniques, and 4 machine learning models. Ultimately, it generated 32 models per outcome, for a total of 96 models for the entire study

Fig. 2

Per capita values for resource intensive healthcare outcomes among Hospital Service Areas. Heat map of annual per capita emergency room visits, inpatient days, and hospital expenditures from hospital service areas in 2017, broken into quintiles. White areas reflect ineligible Hospital Service Areas

Fig. 3

Observed v. Expected plots from best performing prediction models for resource intensive healthcare outcomes. A Log Emergency Room Visits per capita | test-set R2 0.247 | test-set MSE: 0.003. B Log Inpatient Days per capita | test-set R2 0.184 | test-set MSE:0.011. C Log Hospital Expenditures per capita | test-set R2 0.782 | test-set MSE:0.004

Fig. 4

Top 5% of Predicted HSAs from Best Performing Prediction Models for resource intensive healthcare outcomes. A 2017 Log Emergency Room Visits per capita. B 2017 Log Inpatient Days per capita. C 2017 Log Hospital Expenditures per capita