Using Electronic Health Record Data in Pragmatic Clinical Trials
Section 2
Interoperability
Interoperability between electronic health record systems has the potential to benefit patients, payers, health systems, researchers, and clinicians because it would enable data gathered as a part of care to be used to support care between systems, quality improvement, and research. However, in actual practice, interoperability has been hard to achieve.
Historically, the focus of interoperability of data has been on the ability to share information between settings, and the strategy has been to identify various standards for data (eg, data formatting). Over the years, these standards have become more mature, and health organizations have growing incentives (due to both regulation and changes in healthcare reimbursement, and payment to reward quality care) to share patient data across care teams and organizations. Most recently, on May 1, 2020, the Office of the National Coordinator for Health Information Technology (ONC) and the Centers for Medicare and Medicaid Services (CMS) announced a final rule to implement provisions of the 21st Century Cures Act.
The final rule is intended to advance interoperability and support the access, exchange, and use of electronic health information by patients and their caregivers. The rule establishes the United States Core Data for Interoperability (USCDI) standard, which sets forth data classes and elements that support nationwide interoperability; it also includes a broad range of data elements, such as clinical notes, test results, and medications. Specific standards that are mandated as part of the final rule include:
- Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT)
- SNOMED is a standard set of clinical terminology that includes clinical findings, disorders, and observable findings related to health, designed to support electronic health information exchange.
- Logical Observation Identifiers, Names and Codes (LOINC)
- LOINC is a standard that allows users to map health data, such as labs, vital signs, and clinical documents to support data exchange across systems.
- RxNorm
- RxNorm is a free, publicly available resource from the National Library of Medicine that provides “normalized” names and unique identifiers that make it possible to clearly identify a given drug. RxNorm coding allows information about medications to be exchanged across EHRs.
- Concept Unique Identifier (RxCUI)
- The RxCUI is a unique, unambiguous identifier that is assigned to an individual drug entity in RxNorm and used to relate to all things associated with that drug. The RxCUI is used to link one entity in RxNorm to every other entity it’s related to, such as name to ingredient to class.
- International Classification of Diseases, version 10 (ICD-10)
- Provides standard codes for medical conditions, diagnoses, and institutional procedures
While having a defined interface standard solves one part of the interoperability problem (ie, syntatic interoperability), as long as data are natively captured in nonstandard formats such as using local codes for greater specificity, they will need to be mapped to terminologies before they are truly interoperable (ie, semantic interoperability). Previous federal attempts to foster interoperability resulted in highly variable implementations (D’Amore et al. 2014), so study teams should be prepared to validate the data that they receive via these new interfaces. Additionally, teams on retrospective studies also should be prepared to budget for staff to map internal use codes to standard terminologies.
Interfaces like application programming interfaces (APIs) make it very difficult to determine what information is NOT being delivered. For instance, if requesting hemoglobin lab results using a LOINC code, investigators will not have access to the percentage of the actual hemoglobin tests that have been mapped to that code. Ideally all of them will be captured, but it is possible that the data were generated starting on the day they turned on the interface (ie, only prospective results), or perhaps represent results from the most recent lab system upgrade. Since it is not possible to request all data for an entire population (the way data would be validated by looking at trends and distributions), users view results one patient at a time, making it very difficult to identify problems since any given patient may have significant gaps in their data due to normal practice patterns. In other words, APIs make it easier to get bad data faster, while also making it more difficult to troubleshoot and validate. So while it’s a good thing overall, we’re still somewhere between “buyer beware” and “trust, but verify.”
Additionally, care must be taken to make sure that information collected at one site actually matches information at another site. The process of extracting, transforming, and loading data from one system to another can require the “janitorial” work of mapping and data cleaning (Lohr 2014).
To advance interoperability among EHRs and registries, the Pew Data Interoperability Project was developed as a collaboration between the Duke Clinical Research Institute and the Pew Charitable Trusts. In a Grand Rounds describing the project (Envisioning Data Liquidity – The Pew Data Interoperability Project). Dr. James E. Tcheng, suggests that standardization is best accomplished through “native” data standardization, as opposed to standardizing data after it has been collected. For registries, the keys to success are well-defined clinical concepts, specified representation of the concepts and data in database systems, and integrating data capture into the workflow.
SECTIONS
sections
- Introduction
- Interoperability
- Data as a Surrogate for Clinical Phenomena
- Developing and Refining the Research Questions
- Specific Uses for EHR Data in PCTs
- Estimating and Identifying the Study Population and Assessing Baseline Prognostic Characteristics
- Implementing and Monitoring the Delivery of an Intervention
- Assessing Outcomes
- The Research Question Drives the Data Requirements
- Patient Access to Data
- Additional Resources
Resources
National Health IT Priorities for Research - A Policy and Development Agenda; NIH Collaboratory EHR Workshop Video Module (12:09)
Dr. Teresa Zayas-Cabán of the Office of the National Coordinator for Health Information Technology describes the challenges and opportunities of data quality, access, and management as electronic health data become more widely available for research.
Grand Rounds
May 10, 2019: Treating Data as an Asset: Data Entrepreneurship in the Service of Patients (Eric Perakslis, PhD)
November 9, 2018: Data Linkage Within, Across, and Beyond PCORnet (Thomas W. Carton, PhD, MS, Keith Marsolo, PhD)
October 19, 2018: A New Path Forward for Using Decentralized Clinical Trials (Jeffry Florian, PhD, Annemarie Forrest, Penny Randall, MD, MBA)
Podcast
November 20, 2018: Data Linkage Within, Across, and Beyond PCORnet (Thomas W. Carton, PhD, MS, Keith Marsolo, PhD)
REFERENCES
D’Amore, JD, Mandel JC, Kreda DA, et al. 2014. Are Meaningful Use Stage 2 Certified EHRs ready for interoperability? Findings from the SMART C-CDA Collaborative. J Am Med Informat Assoc. 21:1060-1068. https://doi.org/10.1136/amiajnl-2014-002883.
Lohr S. 2014. For big-data scientists, ‘janitor work’ is key hurdle to insights. New York Times. August 17, 2014. https://www.nytimes.com/2014/08/18/technology/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html.
current section : Interoperability
- Introduction
- Interoperability
- Data as a Surrogate for Clinical Phenomena
- Developing and Refining the Research Questions
- Specific Uses for EHR Data in PCTs
- Estimating and Identifying the Study Population and Assessing Baseline Prognostic Characteristics
- Implementing and Monitoring the Delivery of an Intervention
- Assessing Outcomes
- The Research Question Drives the Data Requirements
- Patient Access to Data
- Additional Resources