Researchers have identified some of the clearest opportunities to reduce healthcare costs through the use of big data.
In a study published in the July issue of Health Affairs, researchers discuss the role of algorithms in reducing cost in the following categories: high-cost patients, readmissions, triage, de-compensation, adverse events, and treatment optimization for diseases affecting multiple organ systems.
The examples we present in this study provide key insights to the low hanging fruit in healthcare big data and have implications for regulatory oversight, offer suggestions for addressing privacy concerns and underscore the need for support of research on analytics, said David Bates, M.D., chief quality officer at Brigham and Womens Hospital, Boston, and lead author on the study.
The researchers emphasize that these six cases are not an exhaustive list of the ways in which big data can be useful in improving value in healthcare. Specifically, they note that these examples, which focus on inpatient settings, will likely be transferable to the outpatient setting as well.
Support for research that evaluates the use of analytics and big data to address these six use cases, as well as thoughtful consideration of regulation and payment is warranted, Bates said. Additionally, as multiple streams of data become available for analytic purposes, consideration of patients privacy and their desire to link disparate sources of data will be of the utmost importance.
The work was supported in part by a grant from the Gordon and Betty Moore Foundation.
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