Prospects for Care Coordination Measurement Using Electronic Data Sources
Table of Contents
Care coordination is defined as the deliberate organization of patient care activities between two or more participants (including the patient) involved in a patient's care to facilitate the appropriate delivery of health care services.1 It has been recognized as a priority area for improving health care delivery in the U.S.2 and is a target for efforts to improve the quality and patient-centeredness of care. Efforts to understand and improve deficits in care coordination are abundant, and robust measures of care coordination processes will be essential tools to evaluate, guide, and support these efforts.
Recognizing that care coordination measures that do not require new data collection are of particular interest to the Agency for Healthcare Research and Quality (AHRQ) and the field, we assessed the potential for measurement using data from electronic data sources, in particular from existing and emerging health information technology (IT) systems such as electronic health records (EHR), health information exchanges (HIE) and all-payer claims databases (APCD). Relying on background research and input from experts, we aimed to provide information relevant to decisions about where to focus measure development efforts, where the most fertile ground exists for measures that rely on electronic data sources, and barriers to developing such measures.
Scope and Approach
This project aimed to understand measurement that would be feasible for a wide range of outpatient practices or hospitals that use health IT systems, not only those that are most advanced in their health IT usage. This effort did not aim to develop new measures of care coordination, but to synthesize the background relevant to such future work. To understand the potential and challenges of measuring care coordination with current and emerging technologies, we sought input from a panel of informants with expertise in health IT systems development and use, health information exchanges, electronic health records, all-payer claims databases, insurance plans, health data standards, and quality measurement. We spoke with these experts individually during 1-hour information-gathering calls, and convened two duplicative group calls (to accommodate schedules) to discuss specific measurement possibilities. For further details of methods, go to Appendix A. In this report, we present themes and lessons learned through these discussions, and offer an evaluation of the most promising near-term and long-term opportunities for measuring care coordination using electronic data.
We define several key terms that are important for understanding the contents of this report. A complete glossary that includes these key terms and others may be found in Appendix C, and links to additional sources are available in Appendix D.
All-Payer Claims Databases (APCD)—Large-scale databases that systematically collect health care claims data (medical claims, pharmacy claims, eligibility files, provider files, and dental claims) from a variety of payer sources and that include claims from most health care providers.i
Data Element/Field—A basic unit of information collected about anything of interest—for example, a medication name or a patient diagnosis. A data element is a unit of data for which the definition, identification, representation, and permissible values are specified by means of a set of attributes.ii
Electronic Health Records (EHR)—A longitudinal electronic record of patient health information generated by one or more encounters in any care delivery setting. These records usually include patient demographics, progress notes, problems, medications, vital signs, past medical history, immunizations, laboratory data, and radiology reports.iii Though often used interchangeably with the term electronic medical record (EMR), EHRs and EMRs differ in the scope of the information they contain. While EMRs contain information pertaining to a single practice or hospital, EHRs are designed to incorporate information from other providers or settings into a single record. In keeping with this broader scope, and the practice of the Office of the National Coordinator for Health IT (ONC),iv throughout this report we use the term EHR unless a particular comment applies specifically to the more limited EMR technology.
Health Care Entity—Discrete units of the health care system that play distinct roles in the delivery of care. Examples include individual nurses or physicians, primary care practices, multispecialty practices, or hospitals.v
Health Information Exchange (HIE)—Those organizations formed as an entity to provide services that focus on data exchange and sharing of patient data across disparate stakeholders at the local, State, regional and national level.vi
Health Information Technology (Health IT)—The application of information processing involving both computer hardware and computer software that deals with the storage, retrieval, sharing, and use of health care information, data, and knowledge for communication and decisionmaking.vii
Meaningful Use (MU)—Medicare and Medicaid EHR Incentive Programs provide incentive payments to eligible professionals, eligible hospitals, and critical access hospitals as they adopt, implement, upgrade, or demonstrate Meaningful Use of certified EHR technology.viii The Office of the National Coordinator for Health IT is developing measures (Meaningful Use measures) to be used by participants in these incentive programs to demonstrate their Meaningful Use of EHR technology.
Promise for Measuring Care Coordination in the Current Health IT Environment
Two key objectives for any quality measurement effort are to reduce the burden of data collection associated with the measures and increase the ability of the measure to detect true differences in quality. To date, a majority of publicly available care coordination process measures have used time-intensive data collection methods.3 In a previous review of the care coordination measurement landscape, we found that 70% of 84 existing care coordination measures use survey methods, while 26% use chart review and only 27% use administrative claims data (some measures use multiple data sources). Of those measures that use administrative data, fewer than one quarter relied exclusively on administrative data; most required additional data collection through chart review or surveys.4 Since little development has occurred in the area of measures that use existing data sources, little is known about the potential to use these data to decrease measurement burden and expand the types of measures available for care coordination.
While the current survey-based approach has advantages in capturing the experience of coordination and being highly adaptable to capturing care coordination activities (because survey questions are designed specifically to capture the activity of interest), it also has several disadvantages. Survey methodology is highly time-intensive and limits the number of patients or providers that can be included in a sample. In addition, sampling methodologies can be subject to selection bias and can require complex designs. Finally, survey-based measures often cannot be collected and calculated at the point of care, and thus reduce the timeliness of measurement. These issues add to the burden of a measure and potentially decrease the feasibility and usability of a measure. In addition to burden, survey-based measures often capture primarily the experience of care coordination, rather than objective measures of processes, proximal outcomes, or the ultimate outcomes achieved by a health system. This experience, while an essential aspect of care coordination, is subject to reporting bias. Additional measure types could offer the ability to create a fuller picture of care coordination by capturing supplementary objective data, in a timely and less burdensome manner.
However, in order to be a useful data source, the data must include features specific to measuring care coordination. This requires data with the ability to capture activities across the continuum of care and across settings (e.g., comprehensive longitudinal data that capture multiple loci of care). In fact, processes that occur during transitions between providers or settings are often of the greatest interest for care coordination (e.g., communication between a hospital and primary care facility). Many existing data sources, such as administrative data, pool cases according to the site of care (e.g., hospital data sets, emergency department data sets) and have limited ability to track patients longitudinally across settings. In addition, information surrounding the transitions across settings is entirely missing from these data. Our initial scan of potential data found a dearth of existing data sets with sufficient information for capturing the dynamic and inter-disciplinary nature of care coordination.
As use of health IT has expanded across the U.S. health care system, interest has grown in using electronic data from these systems as a data source for quality measurement in general (as opposed to an emphasis on assessing care coordination). This enthusiasm has centered around several advantages that health IT data may offer when technologies and their deployment are more mature:
- Minimal data collection burden. Health IT systems have the potential to provide access to structured electronic data that could be automatically extracted for quality measurement without requiring time and resource-intensive data collection efforts.
- Rich clinical context. Information stored within EMRs, EHRs or HIEs is far richer in content and detail than the claims data that have been the basis of many quality measures. This rich information could provide a view of processes of care and clinical outcomes not possible from data sets based only on claims data. For example, claims data generally lack information on physician orders, lab results, and clinical values which are more often included in EHRs. Clinical, rather than claims, data are also appealing for their ability to reflect additional context of a particular patient's clinical situation.
- Longitudinal patient data aggregated from multiple sources over time. Electronic health records and health information exchanges aim to aggregate clinical information temporally from multiple providers and settings into a single location. EHRs are intended to provide clinicians with a comprehensive view of patients' medical history and clinical status by integrating information from different settings within the health care system into a single record. (EMRs, in contrast, contain only information from a single care delivery organization). Making that vision a reality requires the ability to exchange information between locations. HIEs have been proposed as a channel to facilitate such information flow and a tool to aggregate clinical information from across the various settings where patients receive care, such as primary care and specialty clinics, hospitals, and emergency rooms. All-payer claims databases have also been suggested as potentially useful, because they aim to aggregate all health care claims associated with individual patients, and are generally backed by State-mandated reporting requirements that promise a high degree of data completeness, at least for those payers required to submit data. However, APCDs are limited in their reliance on claims data.
Quality measurement using electronic data from these systems is promising but largely untested. Through this project, we sought to understand the potential for using these sources to measure care coordination specifically, and to assess challenges associated with implementing such measurement.
Organization of This Report
The remainder of this report presents findings from our expert panel review process. This report is organized into two main sections:
- Challenges of Measuring Care Coordination Using Electronic Data and Recommendations to Address Those Challenges. Our panel of experts discussed a wide range of challenges as part of their assessments of the potential for measuring care coordination using electronic data sources. We begin with this discussion because it provides important context for understanding the measurement opportunities suggested by panelists. We also synthesize information and suggestions from panelists about some ways to address these challenges.
- Opportunities for Measuring Care Coordination Using Electronic Data Sources. This section outlines near-term and long-term measurement prospects identified by our expert panelists and barriers that must be overcome to bring those possibilities to fruition.
i. Adapted from APCD Council All-Payer Claims Database Fact Sheet. http://www.apcdcouncil.org/sites/apcdcouncil.org/files/APCD%20Fact%20Sheet_FINAL_2.pdf [Plugin Software Help]. Accessed August 18, 2011.
ii. Adapted from U.S. Health Information Knowledgebase (USHIK). http://ushik.ahrq.gov/dr.ui.drOrgDataAlph?Search=All&Referer=DataElement&System=mdr&ItemDisplaySize=50. Accessed 8-21-11.
iii. Adapted from Healthcare Information and Management Systems Society Web site: http://www.himss.org/asp/topics_ehr.asp. Accessed August 18, 2011.
iv. Adapted from Office of National Coordinator Health IT Buzz: https://www.healthit.gov/buzz-blog/
v. Adapted from Care Coordination Measures Atlas. McDonald KM, Schultz E, Albin L, Pineda N, Lonhart J, Sundaram V, Smith-Spangler C, Brustrom J, and Malcolm E. Care Coordination Atlas (Prepared by Stanford University under subcontract to Battelle on Contract No. 290-04-0020). AHRQ Publication No. 11-0023-EF. Rockville, MD: Agency for Healthcare Research and Quality. November 2010.
vi. Adapted from Office of the National Coordinator Homepage: https://www.healthit.gov/
vii. West Virginia State Medical Association Glossary of Health Information Technology Terms, http://www.wvsma.com/shared/content_objects/pdfs/glossary%20of%20hit%20acronyms%20and%20terms%20-%20revised.pdf [Plugin Software Help]. Accessed August 22, 2011.
viii. Adapted from Centers for Medicaid and Medicare Services EHR Incentive Program Web page: http://www.cms.gov/ehrincentiveprograms/. Accessed August 19, 2011.
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