Template matching for benchmarking hospital performance in the veterans affairs healthcare system
| العنوان: | Template matching for benchmarking hospital performance in the veterans affairs healthcare system |
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| المؤلفون: | Wyndy L. Wiitala, Kaitlyn A. Luginbill, Hallie C. Prescott, Andrew M. Ryan, Timothy P. Hofer, Daniel Molling, Brenda M. Vincent |
| المصدر: | Medicine |
| بيانات النشر: | Ovid Technologies (Wolters Kluwer Health), 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Male, medicine.medical_specialty, Observational Study, Severity of Illness Index, outcomes research, 03 medical and health sciences, 0302 clinical medicine, quality of care, Severity of illness, Electronic Health Records, Humans, Medicine, 030212 general & internal medicine, Mortality, Veterans Affairs, Diagnosis-Related Groups, Quality Indicators, Health Care, business.industry, Template matching, Medical record, health care research, Contrast (statistics), Regression analysis, General Medicine, Benchmarking, Length of Stay, United States, Hospitalization, United States Department of Veterans Affairs, 030220 oncology & carcinogenesis, Emergency medicine, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, Female, Observational study, business, Hospitals, High-Volume, Research Article |
| الوصف: | Supplemental Digital Content is available in the text Comparing hospital performance in a health system is traditionally done with multilevel regression models that adjust for differences in hospitals’ patient case-mix. In contrast, “template matching” compares outcomes of similar patients at different hospitals but has been used only in limited patient settings. Our objective was to test a basic template matching approach in the nationwide Veterans Affairs healthcare system (VA), compared with a more standard regression approach. We performed various simulations using observational data from VA electronic health records whereby we randomly assigned patients to “pseudo hospitals,” eliminating true hospital level effects. We randomly selected a representative template of 240 patients and matched 240 patients on demographic and physiological factors from each pseudo hospital to the template. We varied hospital performance for different simulations such that some pseudo hospitals negatively impacted patient mortality. Electronic health record data of 460,213 hospitalizations at 111 VA hospitals across the United States in 2015. We assessed 30-day mortality at each pseudo hospital and identified lowest quintile hospitals by template matching and regression. The regression model adjusted for predicted 30-day mortality (as a measure of illness severity). Regression identified the lowest quintile hospitals with 100% accuracy compared with 80.3% to 82.0% for template matching when systematic differences in 30-day mortality existed. The current standard practice of risk-adjusted regression incorporating patient-level illness severity was better able to identify lower-performing hospitals than the simplistic template matching algorithm. |
| تدمد: | 1536-5964 0025-7974 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::099fab63bc4a994a8e76b4fdaaa11c60Test https://doi.org/10.1097/md.0000000000015644Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....099fab63bc4a994a8e76b4fdaaa11c60 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15365964 00257974 |
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