Difference between revisions of "Journal:Deployment of analytics into the healthcare safety net: Lessons learned"
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As analytics are applied to ever-larger amounts of data and become both more important and more necessary, questions about their use become inevitable. How is data quality influenced by the use of [[health information technology]] (HIT) such as electronic health records (EHR), or acquisition through other means? On an operational level, how can analytic results best be understood and used to address and improve healthcare practice? Patient outcomes? Cost reduction? What are the implications of problematic data quality on operational capacity?{{Efn|''c.f.'' Nambiar, et al. 2013<ref name="NambiarALook13">{{cite journal |title=A look at challenges and opportunities of Big Data analytics in healthcare |journal=2013 IEEE International Conference on Big Data |author=Nambiar, R.; Bhardwaj, R.; Sethi, A. et al. |volume=2013 |year=2013 |doi=10.1109/BigData.2013.6691753}}</ref>; Raghupathi, et al. 2013<ref name="RaghupathiAnOver13">{{cite journal |title=An overview of health analytics |journal=Journal of Health & Medical Informatics |author=Raghupathi, W.; Raghupathi, V. |volume=4 |pages=132 |year=2013 |doi=10.4172/2157-7420.1000132}}</ref> and Ward, et al. 2014<ref name="WardApp14">{{cite journal |title=Applications of business analytics in healthcare |journal=Business Horizons |author=Ward, M.J.; Karsolo, K.A.; Froehle, C.M. |volume=57 |issue=5 |pages=571–582 |year=2014 |pmid=25429161 |pmc=PMC4242091 |doi=10.1016/j.bushor.2014.06.003}}</ref>}} | As analytics are applied to ever-larger amounts of data and become both more important and more necessary, questions about their use become inevitable. How is data quality influenced by the use of [[health information technology]] (HIT) such as electronic health records (EHR), or acquisition through other means? On an operational level, how can analytic results best be understood and used to address and improve healthcare practice? Patient outcomes? Cost reduction? What are the implications of problematic data quality on operational capacity?{{Efn|''c.f.'' Nambiar, et al. 2013<ref name="NambiarALook13">{{cite journal |title=A look at challenges and opportunities of Big Data analytics in healthcare |journal=2013 IEEE International Conference on Big Data |author=Nambiar, R.; Bhardwaj, R.; Sethi, A. et al. |volume=2013 |year=2013 |doi=10.1109/BigData.2013.6691753}}</ref>; Raghupathi, et al. 2013<ref name="RaghupathiAnOver13">{{cite journal |title=An overview of health analytics |journal=Journal of Health & Medical Informatics |author=Raghupathi, W.; Raghupathi, V. |volume=4 |pages=132 |year=2013 |doi=10.4172/2157-7420.1000132}}</ref> and Ward, et al. 2014<ref name="WardApp14">{{cite journal |title=Applications of business analytics in healthcare |journal=Business Horizons |author=Ward, M.J.; Karsolo, K.A.; Froehle, C.M. |volume=57 |issue=5 |pages=571–582 |year=2014 |pmid=25429161 |pmc=PMC4242091 |doi=10.1016/j.bushor.2014.06.003}}</ref>}} | ||
To address these questions and help community health center organizations plan for future use and integration of contemporary analytics, several health center organizations were recruited to engage in a project to evaluate: | |||
* Health center data accuracy: Do health center data systems ensure correct values and consistent formats for data? | |||
* Health center data reliability: Do health center data systems collect and report results that are consistent and correspond to results from CDC data sources? | |||
* Health center data completeness: Do health center data meet the criteria for all mandatory data items? | |||
At each participating organization, which included several community health centers and one state primary care association, a Hadoop-based analytic stack was deployed alongside the organization’s other data systems. Population-level statistics were compared for specific diagnoses and comorbidities calculated through the organization’s normal means and through the analytic stack for comparability and utility. | |||
==Background and literature== | |||
Documentation, reporting accuracy and data quality have been the focus of numerous studies. Yang and Colditz<ref name="YangPrev15">{{cite journal |title=Prevalence of overweight and obesity in the United States, 2007-2012 |journal=JAMA Internal Medicine |author=Yang, L.; Colditz, G.A. |volume=175 |issue=8 |pages=1412–3 |year=2015 |pmid=26098405 |pmc=PMC4625533 |doi=10.1001/jamainternmed.2015.2405}}</ref> recently undertook a review of NHANES survey data in an effort to benchmark the prevalence of obesity nationally. Al Kazzi et al.<ref name="AlKazziDiff15">{{cite journal |title=Differences in the prevalence of obesity, smoking and alcohol in the United States Nationwide Inpatient Sample and the Behavioral Risk Factor Surveillance System |journal=PLoS One |author=Al Kazzi, E.S.; Lau, B.; Li, T. et al. |volume=10 |issue=11 |pages=e0140165 |year=2015 |pmid=26536469 |pmc=PMC4633065 |doi=10.1371/journal.pone.0140165}}</ref> examined the prevalence of obesity and tobacco and alcohol use, comparing the data in a direct survey (the Behavioral Risk Factor Surveillance System - BRFSS) with that in the Nationwide Inpatient Sample administrative database, finding substantial differences between the two. O’Malley et al.<ref name="O'MalleyMeasuring05">{{cite journal |title=Measuring diagnoses: ICD code accuracy |journal=Health Services Research |author=O'Malley, K.J.; Cook, K.F.; Price, M.D. et al. |volume=40 |issue=5 Pt. 2 |pages=1620–39 |year=2005 |pmid=16178999 |pmc=PMC1361216 |doi=10.1111/j.1475-6773.2005.00444.x}}</ref> examined the [[International Statistical Classification of Diseases and Related Health Problems|ICD diagnostic coding process]] and potential sources of error in code accuracy. They found the principal sources of error to be related to both communication and documentation, citing lack of baseline information, communication errors, physician familiarity and experience with the presenting condition, and insufficient attention to detail, as well as training and experience of coders and discrepancies between electronic and paper record systems. Their prescription for improvement was the specification of clear coding processes and a focus on heightening the awareness of all staff engaged in documentation with respect to data quality. | |||
==Footnotes== | ==Footnotes== | ||
Revision as of 20:46, 4 January 2017
| 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
Introduction
Community health centers are the backbone of the health care safety net, providing comprehensive primary care for the nation’s medically underserved communities and populations. In 2015, 1,429 community health centers operated in nearly 10,000 urban and rural sites across the country, serving over 25 million people. Buoyed by HRSA’s long-standing focus on quality improvement and substantial investments in health center HIT systems, health center organizations have implemented electronic health record applications in record numbers. Ninety-two percent of all federally qualified community health centers, and 85 percent of health center “look-alikes” — those entities that meet all requirements of the health center program but are supported by state and local funds rather than federal grants — report that an EHR was in use for all sites and all providers in 2015; only 2.4 percent have no EHR installed at any site, and virtually all expect to adopt an EHR. In addition, 95.5 percent report using clinical decision support applications, and 64.1 percent exchange clinical information electronically with other key providers, health care settings or subspecialty clinicians.[c] In addition, 88.9 percent participate in the Centers for Medicare and Medicaid Services (CMS) EHR Incentive Program commonly known as "Meaningful Use." These statistics reflect a commitment to the adoption of new technologies to support the provision of high-quality clinical care and streamline operations. Yet as the movement to value-based payment accelerates and strategic planning becomes more complex, community health center organizations, along with all other providers, must be prepared for new and increasingly sophisticated analytics to support clinical care and operations.
As analytics are applied to ever-larger amounts of data and become both more important and more necessary, questions about their use become inevitable. How is data quality influenced by the use of health information technology (HIT) such as electronic health records (EHR), or acquisition through other means? On an operational level, how can analytic results best be understood and used to address and improve healthcare practice? Patient outcomes? Cost reduction? What are the implications of problematic data quality on operational capacity?[d]
To address these questions and help community health center organizations plan for future use and integration of contemporary analytics, several health center organizations were recruited to engage in a project to evaluate:
- Health center data accuracy: Do health center data systems ensure correct values and consistent formats for data?
- Health center data reliability: Do health center data systems collect and report results that are consistent and correspond to results from CDC data sources?
- Health center data completeness: Do health center data meet the criteria for all mandatory data items?
At each participating organization, which included several community health centers and one state primary care association, a Hadoop-based analytic stack was deployed alongside the organization’s other data systems. Population-level statistics were compared for specific diagnoses and comorbidities calculated through the organization’s normal means and through the analytic stack for comparability and utility.
Background and literature
Documentation, reporting accuracy and data quality have been the focus of numerous studies. Yang and Colditz[4] recently undertook a review of NHANES survey data in an effort to benchmark the prevalence of obesity nationally. Al Kazzi et al.[5] examined the prevalence of obesity and tobacco and alcohol use, comparing the data in a direct survey (the Behavioral Risk Factor Surveillance System - BRFSS) with that in the Nationwide Inpatient Sample administrative database, finding substantial differences between the two. O’Malley et al.[6] examined the ICD diagnostic coding process and potential sources of error in code accuracy. They found the principal sources of error to be related to both communication and documentation, citing lack of baseline information, communication errors, physician familiarity and experience with the presenting condition, and insufficient attention to detail, as well as training and experience of coders and discrepancies between electronic and paper record systems. Their prescription for improvement was the specification of clear coding processes and a focus on heightening the awareness of all staff engaged in documentation with respect to data quality.
Footnotes
- ↑ 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."
- ↑ See HRSA's 2015 Health Center Data, Table 5 - Staffing and Utilization
- ↑ c.f. Nambiar, et al. 2013[1]; Raghupathi, et al. 2013[2] and Ward, et al. 2014[3]
References
- ↑ Nambiar, R.; Bhardwaj, R.; Sethi, A. et al. (2013). "A look at challenges and opportunities of Big Data analytics in healthcare". 2013 IEEE International Conference on Big Data 2013. doi:10.1109/BigData.2013.6691753.
- ↑ Raghupathi, W.; Raghupathi, V. (2013). "An overview of health analytics". Journal of Health & Medical Informatics 4: 132. doi:10.4172/2157-7420.1000132.
- ↑ Ward, M.J.; Karsolo, K.A.; Froehle, C.M. (2014). "Applications of business analytics in healthcare". Business Horizons 57 (5): 571–582. doi:10.1016/j.bushor.2014.06.003. PMC PMC4242091. PMID 25429161. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4242091.
- ↑ Yang, L.; Colditz, G.A. (2015). "Prevalence of overweight and obesity in the United States, 2007-2012". JAMA Internal Medicine 175 (8): 1412–3. doi:10.1001/jamainternmed.2015.2405. PMC PMC4625533. PMID 26098405. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4625533.
- ↑ Al Kazzi, E.S.; Lau, B.; Li, T. et al. (2015). "Differences in the prevalence of obesity, smoking and alcohol in the United States Nationwide Inpatient Sample and the Behavioral Risk Factor Surveillance System". PLoS One 10 (11): e0140165. doi:10.1371/journal.pone.0140165. PMC PMC4633065. PMID 26536469. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4633065.
- ↑ O'Malley, K.J.; Cook, K.F.; Price, M.D. et al. (2005). "Measuring diagnoses: ICD code accuracy". Health Services Research 40 (5 Pt. 2): 1620–39. doi:10.1111/j.1475-6773.2005.00444.x. PMC PMC1361216. PMID 16178999. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1361216.
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. The citation information for the first reference was incorrect and has been updated.
