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Healthcare Analytics: Creating a Prioritized Improvement System with Performance Benchmarking
Eugene Kolker1,2,3,4, Evelyne Kolker4,5
11 Bioinformatics and High-throughput Analysis Laboratory, Seattle Children's Research Institute, University of Washington , Seattle, WA, USA .
Abstract:
The importance of healthcare improvement is difficult to overstate. This article describes our collaborative work with experts at Seattle Children's to create a prioritized improvement system using performance benchmarking. We applied analytics and modeling approaches to compare and assess performance metrics derived from U.S. News and World Report benchmarking data. We then compared a wide range of departmental performance metrics, including patient outcomes, structural and process metrics, survival rates, clinical practices, and subspecialist quality. By applying empirically simulated transformations and imputation methods, we built a predictive model that achieves departments' average rank correlation of 0.98 and average score correlation of 0.99. The results are then translated into prioritized departmental and enterprise-wide improvements, following a data to knowledge to outcomes paradigm. These approaches, which translate data into sustainable outcomes, are essential to solving a wide array of healthcare issues, improving patient care, and reducing costs.
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