Digital Infrastructure for the Learning Health System The Foundation for Continuous Improvement in Health and Health Care: Workshop Series Summary (2011) / Chapter Skim
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3 Technical Issues for the Digital Health Infrastructure
3 Technical Issues for the Digital Health Infrastructure
Pages 99-124
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This chapter explores these issues with a vision for leveraging current technologies and identifying priorities for innovation in order to ensure that data collected in one system can be utilized across many others for a variety of different uses -- for example, quality, research, public health -- all of which will improve health and health care. Douglas Fridsma from the Office of the National Coordinator for Health IT provides an update on the current standards and interoperability framework being developed.
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Furthermore, she posits that a standardized core dataset of electronic health record information that could be repurposed for research, safety monitoring, quality reporting, and population health would help facilitate an interoperable digital health infrastructure.
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He suggests that new strategies to deal with patient and provider identity management, vocabulary standardization, and value set maintenance by addressing elements including patient- and provider-level aggregation, and health system metadata, and value set maintenance should be prioritized. BUILDING A STANDARDS AND INTEROPERABILITY FRAMEWORK Douglas Fridsma, M.D., Ph.D.
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Creating tools such • as vocabulary and terminology registries will facilitate adoption. Furthermore, it is necessary to make implementation specifications easy to use.
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Cer • tification needs to be tightly linked to the process of developing standards and implementation specifications. ONC is working to develop certification criteria and a certification process that makes it possible to test whether people are following suggested standards and specifications.
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104 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM for computable interoperability. If stakeholders cannot agree on what a term or a concept is (or means)
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Successfully harmonizing core concepts is a necessary step in developing implementation specifications. In many ways, developing successful imple Standards Pilot Demonstration Development Projects Use Case Development Harmonization of Implementation Reference Certification and Functional Core Concepts (NIEM Specifications Implementation and Testing Requirements framework)
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As part of these functions, pilot environments are also created so that ONC and its partners in the private and public healthcare sectors can test and evaluate how the reference implementations work. Without reference implementations it is not possible to test whether or not implementation specifications that have been developed are usable without unforeseen problems.
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The Health Information Technology Standards Committee evaluates the work completed through the S&I framework process and may propose additional standards that need to be incorporated. Other programs are also integrated into the framework as important stakeholders, including the joint Department of Defense–Department of Veterans' Affairs Virtual Lifetime Electronic Record (VLER)
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Kush, Ph.D. Clinical Data Interchange Standards Consortium The digital infrastructure for a learning health system must be based upon information possessing integrity and quality such that users can trust in the system and important medical decisions can be made based upon accurate information and knowledge.
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of sufficient high quality, meaningful information upon which trustworthy decisions can be leveraged to improve health care for all of us, as patients. The path to interoperability, in an ideal situation would rely on both a common information exchange reference model as well as controlled terminology.
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The standards enable ready data aggregation and semantic interoperability among proprietary or unique technologies as long as these technologies inherently support the research standards. One key enabler of a learning health system would be to have a core dataset (with common value sets and terminology)
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The term eSource pertains to collecting data electronically initially through such technologies as e-diaries, e-patient-reported outcomes, e-data collection instruments, and EHRs. The overarching goals of this initiative were to make it easier for physicians to conduct clinical research, to collect data only once in a global research standard format for multiple downstream uses, and to improve data quality and patient safety.
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The harmonized global research standards are depicted in Figure 3-5. One particular standard that would pave the way for a learning health system is the Clinical Data Acquisition Standards Harmonization (CDASH)
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FIGURE 3-6 Schematic depicting a sample interoperability specification involving Figure 3-6.eps the CCD, the IHE RFD integration profile, and CDASH.
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Standards-Inspired Innovation A core dataset with standard value sets and terminology can dramatically reduce time and effort to report key information for safety, research, and public health; accommodate e-diaries and other patient-entered data; improve data quality; enable data aggregation and analysis or queries; be extensible and pave the way for more complex research and clinical genomics for personalized health care; and be readily implemented by EHR vendors. While search engines and signal detection have their place in the learning health system, ensuring the integrity of the search results -- and thus trust in the knowledge upon which clinical decisions are based -- a learning health system must support rigorous scientific research.
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Secure data liquidity is a catch phrase for a functional architecture that enables an explosion of new uses of healthcare data by making two
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. We need to break through from having data locked up and unlinked, to a situation where data flow for many purposes, with provable security in regard to who is permitted to use, and is, using, which data for what purpose ("secure data liquidity")
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Both grids and clouds leverage core Internet protocols and services and are typically deployed in a services-oriented approach. Thus, combining characteristics of grids and clouds in a hosted federation of services is required for the digital infrastructure of the learning health system.
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Summary The issues facing health and biomedical systems of multiple -- often competing and differentially motivated -- entities that need to work together to care for patients, improve population health, and conduct research can be addressed by federated (grid) approaches in combination with hosted (cloud)
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These challenges are driving HIEs to become emergent centers of innovation in health data management with core competencies that focus on standardizing and integrating clinical data to support the informational needs of myriad healthcare processes. Stated simply, addressing the complex task of managing information heterogeneity is an intrinsic HIE function.
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The Indiana Network for Patient Care The INPC contains more than 3.1 billion coded standardized clinical observations, and a global patient index that holds more than 20 million person:source entities that represent more than 12 million unique individuals. Since the mid-1990s, the INPC global patient identity resolution service resolves identities from real-time clinical data streams provided by myriad sources with widely varying data quality.
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We also need to standardize physician data since a single doctor can practice at multiple hospitals and be at multiple clinics throughout the week. Finally, we need to standardize metadata including business rules, knowledge basis, and so forth.
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Even on the billing side, Medicaid had to push back the deadline an entire year because hospitals were not ready to implement physician identifiers. In addition, many clinical transactions do not contain sufficient physician data to generate quality reports for providers.
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Maintaining Value Sets The final component to our research on data standardization and interoperability research is on maintaining value sets. A value set is a collection of concepts drawn from one or more controlled terminology systems and grouped together for a specific purpose -- for example, ICD-9, SNOMED, and LOINC®.
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New strategies are also needed in the areas of patient matching, physician linkage, and value set maintenance to provide the most comprehensive health information for the benefit of individual and public health. REFERENCES Foster, I
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