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. 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 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. 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?

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 recently undertook a review of NHANES survey data in an effort to benchmark the prevalence of obesity nationally. Al Kazzi et al. 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. 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.

Devoe, et al. compared the entries in EHRs with the same data in the Medicaid claims data set for a group of 50 community health centers in Oregon. They found gaps in data congruence across the study group, with some services documented in the Medicaid data set but not the EHRs, and others documented in the EHRs but not in the Medicaid data set. For the latter group, nearly 50 percent of services documented in the EHR were not found in the Medicaid claims for HbA1c, cholesterol screening, retinopathy screening and influenza vaccination. They also evaluated demographic characteristics and found that Spanish speaking patients, as well as those who had gaps in insurance coverage, were more likely to have services documented in the EHR but not in the Medicaid claims data, a finding especially relevant to community health centers, which disproportionately serve poor, uninsured individuals and those best served in languages other than English.

Outside of health care, other industries — including discrete manufacturing and financial services — have struggled with the overall issue of data quality. Over time, both of these industries have, for the most part, achieved very high levels of data quality and high levels of user confidence in their data, and the experience in these industries might provide some insight into data quality improvement in healthcare. Two projects are especially instructive in this area. The C4 Project at General Motors, which began in 1986, was an attempt to develop an entirely paperless design and manufacturing specification system for automotive manufacturing. The data quality effort associated with this project was immense. A staff of close to 50 people was assigned to the various parts of data acquisition, normalization, maintenance and life cycle management. The project emphasized the design of processes to ensure data quality and integrity. In particular, data governance was monitored at least as much as data entry, storage and usage. This went a long way toward ensuring a high level of data quality.

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.