Journal:Deployment of analytics into the healthcare safety net: Lessons learned
| Full article title | Deployment of analytics into the healthcare safety net: Lessons learned |
|---|---|
| Journal | Online Journal of Public Health Informatics |
| Author(s) | Hartzband, David; Jacobs, Feygele |
| Author affiliation(s) | RCHN Community Health Foundation |
| Primary contact | Email: dhartzband at rchnfoundation dot org |
| Year published | 2016 |
| Volume and issue | 8(3) |
| Page(s) | e203 |
| DOI | 10.5210/ojphi.v8i3.7000 |
| ISSN | 1947-2579 |
| Distribution license | Creative Commons Attribution-NonCommercial 3.0 Unported |
| Website | http://ojphi.org/ojs/index.php/ojphi/article/view/7000 |
| Download | http://ojphi.org/ojs/index.php/ojphi/article/download/7000/5812 (PDF) |
Abstract
Background: As payment reforms shift healthcare reimbursement toward value-based payment programs, providers need the capability to work with data of greater complexity, scope and scale. This will in many instances necessitate a change in understanding of the value of data and the types of data needed for analysis to support operations and clinical practice. It will also require the deployment of different infrastructure and analytic tools. Community health centers (CHCs), which serve more than 25 million people and together form the nation’s largest single source of primary care for medically underserved communities and populations, are expanding and will need to optimize their capacity to leverage data as new payer and organizational models emerge.
Methods: 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 CHCs and a primary care association (PCA) with an affiliated data warehouse supporting health centers across the state. 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 deploying the analytic stack was carried out using those common definitions established for the Uniform Data System (UDS) by the Health Resources and Service Administration.[a] In addition, interviews with health center leadership and staff were completed to understand the context for the findings.
Results: 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 under-reporting 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.
Conclusion: Data awareness — that is, an appreciation of the importance of data integrity, data hygiene[b] 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.
Keywords: Community health centers, analytics, decision-making, data
Notes
- ↑ As defined in Health Resources and Services Administration's Bureau of Primary Health Care, UDS Reporting Instructions for Health Centers, 2014 Edition (PDF)
- ↑ "Data hygiene is the collective processes conducted to ensure the cleanliness of data. Data is considered clean if it is relatively error-free."
References
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added. To more easily differentiate footnotes from references, the original footnotes (which where numbered) were updated to use lowercase letters.
