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arXiv · 1706.00487

Learning Bundled Care Opportunities from Electronic Medical Records

You Chen·Abel N. Kho·David Liebovitz·Catherine Ivory·Sarah Osmundson·Jiang Bian·Bradley A. Malin
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Abstract

Objectives: The fee-for-service approach to healthcare leads to the management of a patient's conditions in an independent manner, inducing various negative consequences. It is recognized that a bundled care approach to healthcare-one that manages a collection of health conditions together-may enable greater efficacy and cost savings. However, it is not always evident which sets of conditions should be managed in a bundled program. Study Design: Retrospective inference of clusters of health conditions from an electronic medical record (EMR) system. A survey of healthcare experts to ascertain the plausibility of the clusters for bundled care programs. Methods: We designed a data-driven framework to infer clusters of health conditions via their shared clinical workflows according to EMR utilization by healthcare employees. We evaluated the framework with approximately 16,500 inpatient stays from a large medical center. The plausibility of the clusters for bundled care was assessed through a survey of a panel of healthcare experts using an analysis of variance (ANOVA) under a 95% confidence interval. Results: The framework inferred four condition clusters: 1) fetal abnormalities, 2) late pregnancies, 3) prostate problems, and 4) chronic diseases (with congestive heart failure featuring prominently). Each cluster was deemed plausible by the experts for bundled care. Conclusions: The findings suggest that data from EMRs may provide a basis for discovering new directions in bundled care. Still, translating such findings into actual care management will require further refinement, implementation, and evaluation.

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You Chen, Abel N. Kho, David Liebovitz, Catherine Ivory, Sarah Osmundson, Jiang Bian, Bradley A. Malin. 2017-05-26. Learning Bundled Care Opportunities from Electronic Medical Records. https://arxiv.org/abs/1706.00487

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