Learning Bundled Care Opportunities from Electronic Medical Records
You Chen, Abel N. Kho, David Liebovitz, Catherine Ivory, Sarah, Osmundson, Jiang Bian, and Bradley A. Malin

TL;DR
This study presents a data-driven method to identify groups of health conditions from electronic medical records that could be managed together in bundled care programs, potentially improving healthcare efficiency.
Contribution
The paper introduces a novel framework for inferring condition clusters from EMR data and validates their plausibility for bundled care through expert surveys.
Findings
Identified four plausible condition clusters for bundled care.
Healthcare experts found the clusters suitable for bundled management.
EMR data can inform new bundled care strategies.
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…
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Taxonomy
TopicsElectronic Health Records Systems · Chronic Disease Management Strategies · Healthcare Systems and Technology
