Deployment of Analytics into the Healthcare Safety Net: Lessons Learned
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How to Cite

Hartzband, D., & Jacobs, F. (2016). Deployment of Analytics into the Healthcare Safety Net: Lessons Learned. Online Journal of Public Health Informatics, 8(3). https://doi.org/10.5210/ojphi.v8i3.7000

Abstract

To better understand existing capacity and help organizations plan for the strategic and expanded uses of data, a project was initiated that deployed contemporary, Hadoop-based, analytic technology into several multi-site community health centers (CHCs) and a primary care association (PCA). An initial data quality exercise was carried out after deployment, in which a number of analytic queries were executed using both the existing electronic health record (EHR) applications and in parallel, the analytic stack. Each organization carried out the EHR analysis using the definitions typically applied for routine reporting. The analysis using the analytic stack was carried out using those common definitions established for the Uniform Data System (UDS) by the Health Resources and Service Administration.  In addition, interviews with health center leadership and staff were completed to understand the context for the findings.

The analysis uncovered many challenges and inconsistencies with respect to the definition of core terms (patient, encounter, etc.), data formatting, and missing, incorrect and unavailable data. At a population level, apparent underreporting of a number of diagnoses, specifically obesity and heart disease, was also evident in the results of the data quality exercise, for both the EHR-derived and stack analytic results.

Data awareness, that is, an appreciation of the importance of data integrity, data hygiene and the potential uses of data, needs to be prioritized and developed by health centers and other healthcare organizations if analytics are to be used in an effective manner to support strategic objectives. While this analysis was conducted exclusively with community health center organizations, its conclusions and recommendations may be more broadly applicable.


 

https://doi.org/10.5210/ojphi.v8i3.7000
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