Distributed Health Data Networks A Practical and Preferred Approach to Multi-Institutional Evaluations of Comparative Effectiveness, Safety, and Quality of Care

Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA.
Medical care (Impact Factor: 3.23). 06/2010; 48(6 Suppl):S45-51. DOI: 10.1097/MLR.0b013e3181d9919f
Source: PubMed


Comparative effectiveness research, medical product safety evaluation, and quality measurement will require the ability to use electronic health data held by multiple organizations. There is no consensus about whether to create regional or national combined (eg, "all payer") databases for these purposes, or distributed data networks that leave most Protected Health Information and proprietary data in the possession of the original data holders.
Demonstrate functions of a distributed research network that supports research needs and also address data holders concerns about participation. Key design functions included strong local control of data uses and a centralized web-based querying interface.
We implemented a pilot distributed research network and evaluated the design considerations, utility for research, and the acceptability to data holders of methods for menu-driven querying. We developed and tested a central, web-based interface with supporting network software. Specific functions assessed include query formation and distribution, query execution and review, and aggregation of results.
This pilot successfully evaluated temporal trends in medication use and diagnoses at 5 separate sites, demonstrating some of the possibilities of using a distributed research network. The pilot demonstrated the potential utility of the design, which addressed the major concerns of both users and data holders. No serious obstacles were identified that would prevent development of a fully functional, scalable network.
Distributed networks are capable of addressing nearly all anticipated uses of routinely collected electronic healthcare data. Distributed networks would obviate the need for centralized databases, thus avoiding numerous obstacles.

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Available from: Ross Lazarus, Oct 24, 2014
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    • "The distributed network will enable research studies to be conducted, while allowing each participating organization to maintain physical and operational control over their data. This structure lowers institutional barriers to participation and ensures availability of local experts who can interpret the data.11 12 The Data Standards, Security and Network Infrastructure (DSSNI) task force will identify minimal data standards and technical specifications for data standardization across CDRNs and PPRNs and develop an approach to cross-network querying that meets the security, patient privacy, institutional confidentiality, and governance needs of the network participants.13 "
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    ABSTRACT: The Patient-Centered Outcomes Research Institute (PCORI) has launched PCORnet, a major initiative to support an effective, sustainable national research infrastructure that will advance the use of electronic health data in comparative effectiveness research (CER) and other types of research. In December 2013, PCORI's board of governors funded 11 clinical data research networks (CDRNs) and 18 patient-powered research networks (PPRNs) for a period of 18 months. CDRNs are based on the electronic health records and other electronic sources of very large populations receiving healthcare within integrated or networked delivery systems. PPRNs are built primarily by communities of motivated patients, forming partnerships with researchers. These patients intend to participate in clinical research, by generating questions, sharing data, volunteering for interventional trials, and interpreting and disseminating results. Rapidly building a new national resource to facilitate a large-scale, patient-centered CER is associated with a number of technical, regulatory, and organizational challenges, which are described here.
    Journal of the American Medical Informatics Association 05/2014; 21(4). DOI:10.1136/amiajnl-2014-002747 · 3.50 Impact Factor
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    • "PopMedNet is an open-source distributed data-sharing platform funded by the Agency for Healthcare Research and Quality, the FDA, ONC, and the National Institutes of Health (NIH).16 37 It is a key component of several large-scale distributed networks, including the FDA Mini-Sentinel, the NIH Health Care System Research Collaboratory Distributed Research Network, and the Massachusetts Department of Public Health MDPHnet system.9–11 PopMedNet will be used by the newly funded Patient-Centered Outcomes Research Institute (PCORI) National Patient-Centered Clinical Research Network (PCORnet) to help create and operate a ‘network-of-networks’ to support clinical research.38 "
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    ABSTRACT: Understanding population-level health trends is essential to effectively monitor and improve public health. The Office of the National Coordinator for Health Information Technology (ONC) Query Health initiative is a collaboration to develop a national architecture for distributed, population-level health queries across diverse clinical systems with disparate data models. Here we review Query Health activities, including a standards-based methodology, an open-source reference implementation, and three pilot projects. Query Health defined a standards-based approach for distributed population health queries, using an ontology based on the Quality Data Model and Consolidated Clinical Document Architecture, Health Quality Measures Format (HQMF) as the query language, the Query Envelope as the secure transport layer, and the Quality Reporting Document Architecture as the result language. We implemented this approach using Informatics for Integrating Biology and the Bedside (i2b2) and hQuery for data analytics and PopMedNet for access control, secure query distribution, and response. We deployed the reference implementation at three pilot sites: two public health departments (New York City and Massachusetts) and one pilot designed to support Food and Drug Administration post-market safety surveillance activities. The pilots were successful, although improved cross-platform data normalization is needed. This initiative resulted in a standards-based methodology for population health queries, a reference implementation, and revision of the HQMF standard. It also informed future directions regarding interoperability and data access for ONC's Data Access Framework initiative. Query Health was a test of the learning health system that supplied a functional methodology and reference implementation for distributed population health queries that has been validated at three sites.
    Journal of the American Medical Informatics Association 04/2014; 21(4). DOI:10.1136/amiajnl-2014-002707 · 3.50 Impact Factor
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    • "For example, the HMO Research Network includes 18 independent healthcare organizations that work together on a broad range of health studies through joint participation in a virtual data warehouse [2]. Similarly, the FDA Mini-Sentinel initiative combines data from 19 collaborating institutions through a variety of distributed programming techniques [9]. In addition, the Commonwealth Government of Australia is making health data integration across institutions a high priority to support health research [10], while the United Kingdom has nationwide initiatives to support the use of electronic databases for health research across the UK [11]. "
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    ABSTRACT: Studying rare outcomes, new interventions and diverse populations often requires collaborations across multiple health research partners. However, transferring healthcare research data from one institution to another can increase the risk of data privacy and security breaches. A working group of multi-site research programmers evaluated the need for tools to support data security and data privacy. The group determined that data privacy support tools should: 1) allow for a range of allowable Protected Health Information (PHI); 2) clearly identify what type of data should be protected under the Health Insurance Portability and Accountability Act (HIPAA); and 3) help analysts identify which protected health information data elements are allowable in a given project and how they should be protected during data transfer. Based on these requirements we developed two performance support tools to support data programmers and site analysts in exchanging research data. The first tool, a workplan template, guides the lead programmer through effectively communicating the details of multi-site programming, including how to run the program, what output the program will create, and whether the output is expected to contain protected health information. The second performance support tool is a checklist that site analysts can use to ensure that multi-site program output conforms to expectations and does not contain protected health information beyond what is allowed under the multi-site research agreements. Together the two tools create a formal multi-site programming workflow designed to reduce the chance of accidental PHI disclosure.
    BMC Medical Informatics and Decision Making 10/2013; 13(1):116. DOI:10.1186/1472-6947-13-116 · 1.83 Impact Factor
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