Developing a Machine Learning Risk-adjustment Method for Hospitalizations and Emergency Department Visits of Nursing Home Residents With Dementia

Author:

Xu Huiwen12,Bowblis John R.34,Becerra Adan Z.5,Intrator Orna67

Affiliation:

1. School of Public and Population Health, University of Texas Medical Branch

2. Sealy Center on Aging, University of Texas Medical Branch, Galveston, TX

3. Department of Economics, Farmer School of Business, Miami University

4. Scripps Gerontology Center, Miami University, Oxford, OH

5. Department of Surgery, Rush University Medical Center, Chicago, IL

6. Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester

7. Geriatrics & Extended Care Data Analysis Center (GECDAC), Canandaigua VA Medical Center, Canandaigua, NY

Abstract

Background: Long-stay nursing home (NH) residents with Alzheimer disease and related dementias (ADRD) are at high risk of hospital transfers. Machine learning might improve risk-adjustment methods for NHs. Objectives: The objective of this study was to develop and compare NH risk-adjusted rates of hospitalizations and emergency department (ED) visits among long-stay residents with ADRD using Extreme Gradient Boosting (XGBoost) and logistic regression. Research Design: Secondary analysis of national Medicare claims and NH assessment data in 2012 Q3. Data were equally split into the training and test sets. Both XGBoost and logistic regression predicted any hospitalization and ED visit using 58 predictors. NH-level risk-adjusted rates from XGBoost and logistic regression were constructed and compared. Multivariate regressions examined NH and market factors associated with rates of hospitalization and ED visits. Subjects: Long-stay Medicare residents with ADRD (N=413,557) from 14,057 NHs. Results: A total of 8.1% and 8.9% residents experienced any hospitalization and ED visit in a quarter, respectively. XGBoost slightly outperformed logistic regression in area under the curve (0.88 vs. 0.86 for hospitalization; 0.85 vs. 0.83 for ED visit). NH-level risk-adjusted rates from XGBoost were slightly lower than logistic regression (hospitalization=8.3% and 8.4%; ED=8.9% and 9.0%, respectively), but were highly correlated. Facility and market factors associated with the XGBoost and logistic regression-adjusted hospitalization and ED rates were similar. NHs serving more residents with ADRD and having a higher registered nurse-to-total nursing staff ratio had lower rates. Conclusions: XGBoost and logistic regression provide comparable estimates of risk-adjusted hospitalization and ED rates.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Public Health, Environmental and Occupational Health

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