Big Data in Health Care: Using Analytics to Identify and Manage Outcomes and Costs

With more providers and hospitals using electronic record keeping, many health organizations are analyzing and interpreting large quantities of patient information, known as big data, to better manage high-risk and high-cost patients.

 The July 2014 issue of the journal Health Affairs examines the use of big data to improve health care. The authors examine six examples in which data mining can improve care and reduce expenses in hospital settings
including:

1.  Identifying high-cost patients can help determine which patients are most likely to benefit from interventions and which care plans can best improve care

2.  Using predictive analytics and modeling to foresee potential readmissions can enable more precise interventions and care coordination after discharge

3.  Integrating triage practices into the clinical workflow can help manage staffing, patient transfers, and beds

4.  Some Intensive Care Units are using analytics to evaluate multiple data streams from patient monitors to predict whether a patient's condition is likely to worsen

5.  By uncovering unique data patterns, such as prescription drug use and vital sign changes, other systems can help prevent renal failure, infections, and adverse drug events.

6.  Data from multisite disease registries and clinical networks will help manage patients with chronic conditions that span more than one organ system.

The analysis of big data and its integration into provider practices hold great promise for cost containment and better patient outcomes. While big data and the related analytics are powerful tools, the authors say more evaluation is needed to realize the benefits.

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