Matthew C Lenert1, Randolph A Miller2, Yevgeniy Vorobeychik3, Colin G Walsh2. 1. Dept. of Biomedical Informatics, Vanderbilt University, 2525 West End Ave. Suite 1475, Nashville, TN 37203, USA. Electronic address: matthew.c.lenert@vanderbilt.edu. 2. Dept. of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA. 3. Dept. of Computer Science and Engineering, Washington University, St. Louis, MO, USA.
Abstract
OBJECTIVE: Administrators assess care variability through chart review or cost variability to inform care standardization efforts. Chart review is costly and cost variability is imprecise. This study explores the potential of physician orders as an alternative measure of care variability. MATERIALS & METHODS: The authors constructed an order variability metric from adult Vanderbilt University Hospital patients treated between 2013 and 2016. The study compared how well a cost variability model predicts variability in the length of stay compared to an order variability model. Both models adjusted for covariates such as severity of illness, comorbidities, and hospital transfers. RESULTS: The order variability model significantly minimized the Akaike information criterion (superior outcome) compared to the cost variability model. This result also held when excluding patients who received intensive care. CONCLUSION: Order variability can potentially typify care variability better than cost variability. Order variability is a scalable metric, calculable during the course of care.
OBJECTIVE: Administrators assess care variability through chart review or cost variability to inform care standardization efforts. Chart review is costly and cost variability is imprecise. This study explores the potential of physician orders as an alternative measure of care variability. MATERIALS & METHODS: The authors constructed an order variability metric from adult Vanderbilt University Hospital patients treated between 2013 and 2016. The study compared how well a cost variability model predicts variability in the length of stay compared to an order variability model. Both models adjusted for covariates such as severity of illness, comorbidities, and hospital transfers. RESULTS: The order variability model significantly minimized the Akaike information criterion (superior outcome) compared to the cost variability model. This result also held when excluding patients who received intensive care. CONCLUSION: Order variability can potentially typify care variability better than cost variability. Order variability is a scalable metric, calculable during the course of care.
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