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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 1 of 1 Research Studies DisplayedRay M, Zhao S, Wang S
Improving hospital quality risk-adjustment models using interactions identified by hierarchical group lasso regularisation.
This study’s goal was to see if using hierarchical group lasso regularization (HGLR) improved hospital quality risk adjustment (RA) models. The authors analyzed patient discharge de-identified data from 14 State Inpatient Databases, AHRQ Healthcare Cost and Utilization Project, California Department of Health Care Access and Information, and New York State Department of Health. They used HGLR to identify first-order interactions in several AHRQ inpatient quality indicators (IQI) - IQI 09 (Pancreatic Resection Mortality Rate), IQI 11 (Abdominal Aortic Aneurysm Repair Mortality Rate), and Patient Safety Indicator 14 (Postoperative Wound Dehiscence Rate). These RA models were compared with stratum-specific and composite main effects models with covariates selected by least absolute shrinkage and selection operator (LASSO). HGLR identified clinical meaning interactions for all models, with model performance similar or superior for composite models with HGLR-selected features, compared to those with LASSO-selected features. HGLR was found to be scalable to handle a large number of covariates and their interactions and is customizable to use multiple CPU cores to reduce analysis time.
AHRQ-funded; 290201200003I.
Citation: Ray M, Zhao S, Wang S .
Improving hospital quality risk-adjustment models using interactions identified by hierarchical group lasso regularisation.
BMC Health Serv Res 2023 Dec 15; 23(1):1419. doi: 10.1186/s12913-023-10423-9..
Keywords: Quality of Care, Hospitals, Risk