- Food and Drug Administration (FDA)
- 4/1/2019
- Use of Enhanced Publically-Funded Data Systems for Research
- Linking of Clinical and Other Data for Research
STATUS: Completed Project
BACKGROUND
Every day, physicians must make conclusions about a patient’s health status based on a snapshot in time during a brief visit, a conversation with the patient, or limited historical (often rhetorical) health information. A simple and meaningful representation of a patient’s diagnostic history can be invaluable in contextualizing a patient’s personal health care challenges at any single point in time. Laboratory data, however, is commonly excluded from research efforts due to challenges with data ambiguity. The absence of laboratory semantic interoperability—or the ability of two or more systems to exchange, use, and analyze information the same way for in vitro diagnostic (IVD) devices—has been frequently cited as a significant impediment to the continuity of patient care, research, and overall public health care. While it is largely digitized, IVD data that should be represented the same way is often described differently across institutions (or even within an institution), leading to ambiguity and incapacitating its utility in research or for other purposes. To address this critical need to improve laboratory data infrastructure, multi-agency sponsored public workshops were held in 2015 and 2016, leading to the formation of the SHIELD (Systemic Harmonization and Interoperability Enhancement for Laboratory Data) Collaborative. The SHIELD Collaborative is a multi-agency/stakeholder network consisting of the FDA, Centers for Disease Control and Prevention (CDC), National Institutes of Health (NIH), Office of the National Coordinator for Health Information Technology (ONC), Centers for Medicare and Medicaid Services (CMS), U.S. Department of Veterans Affairs (VA), IVD manufacturers, electronic health record (EHR) vendors, laboratories, College of American Pathologists, standards developers, Pew Charitable Trusts, National Evaluation System for Healthcare Technology, and academia.
This project expanded the collaborative efforts of the FDA and other stakeholders involved in the SHIELD Collaborative to ensure laboratory information interoperability, such that the same type of device is described the same way in EHR systems. To realize this goal, this project developed code mapping manuals to consistently map the same LOINC (Logical Observations Identifiers Names and Codes) code to the same type of IVD device. This effort is important because, without specific guidance, manufacturers of IVD devices and laboratories often assign different (and frequently incorrect) LOINC codes for the same type of device. This project also piloted the implementation of SHIELD standard digital formats to update infrastructure in active health care provider laboratories, which included the seamless distribution of LOINC and SNOMED-CT (Systematized Nomenclature of Medicine—Clinical Terms) coding to those provider institutions and registries.
PROJECT PURPOSE & GOALS
This project aimed to improve the quality, interoperability, and portability of laboratory data within and across institutions so that diagnostic information can be pulled from different sources or shared between institutions to help illuminate clinical management and understand health outcomes.
Project Objectives:
- Develop LOINC code mapping manuals for the remainder of IVD domains—Chemistry, Drug/Toxicology, Allergy, Serology/Hematology, Cell Markers, and Molecular Pathology—using the process for Microbiology coding developed by the Regenstrief Institute (owner, developer, and curator of LOINC codes).
- Conduct pilot implementation and testing of an interoperability upgrade to existing laboratory information systems (LIS) and registries by incorporating a SHIELD-approved, high-quality, and industry-defined and supported format to facilitate the publication and exchange of LOINC codes for vendor IVD test results.
- Assess the interoperability and value of the systems and tools tested in the pilot in live LIS and EHR systems pre- and post-implementation.
PROJECT ACHIEVEMENTS AND HIGHLIGHTS
- The project team helped with the development of LOINC code mapping manuals, which are intended to provide clearer guidance to laboratories in assigning the correct code to each IVD test type.
- The project team sought to better understand the current state of laboratory data's integrity by conducting a pilot study with five high-profile academic medical center laboratories. The pilot study found significant variability (41 percent) in how laboratory test manufacturers and health care organizations curate and categorize laboratory data, and that LOINC IVD (LIVD) mappings alone were not enough to promote comprehensive data interoperability.
- The project’s findings have been leveraged to support the national response to the COVID-19 pandemic and the U.S. Department of Health and Human Services (HHS) released guidance requiring the use of SHIELD-harmonized standards for LIVD test code mapping for SARS-CoV-2 tests.
PUBLICATIONS, PRESENTATIONS, AND OTHER PUBLICALLY AVAILABLE RESOURCES
Resources:
- Shield (Systemic Harmonization and Interoperability Enhancement for Laboratory Data) - Standardization of Lab Data to Enhance Patient-Centered Outcomes Research and Value-Based Care: Final Report – This report summarizes the accomplishments of the FDA project SHIELD. The project sought to improve the quality, interoperability and portability of laboratory data within and between institutions so that diagnostic information can be pulled from different sources or shared between institutions to help illuminate clinical management and understand health outcomes. The project developed LOINC code mapping manuals for Chemistry, Drug/Toxicology, Allergy, Serology/Hematology, Cell Markers and Molecular Pathology. The also conducted pilot implementation and testing of an interoperability upgrade to existing laboratory information systems (LIS) and registries by incorporating SHIELD-approved, high-quality, industry-defined and supported format to facilitate the publication and exchange of LOINC codes for vendor IVD test results.
- The six LOINC Mapping Guides (allergy, cell markers, chemistry, drug, and toxicology, hematology and serology, and molecular pathology) are available here: https://loinc.org/guides/#in-development.
- The LIVD Test Code Mapping Tools, which define LOINC codes to support the standardized reporting of SARS-CoV-2 test results to public health agencies, are available here: https://www.cdc.gov/csels/dls/livd-codes.html?CDC_AA_refVal=https%3A%2F%2Fwww.cdc.gov%2Fcsels%2Fdls%2Fsars-cov-2-livd-codes.html.
- Webpages on the Medical Device Innovation Consortium and MDEpiNet websites include SARS-CoV-2 and COVID-19 test coding resources.
Publications:
- The peer-reviewed article, “Encoding laboratory testing data: case studies of the national implementation of HHS requirements and related standards in five laboratories,” published in the Journal of the American Medical Informatics Association (JAMIA) is available here: https://pubmed.ncbi.nlm.nih.gov/35639494/.
RELATED PROJECTS
Below is a list of ASPE-funded PCORTF projects that are related to this project
CURE ID: Aggregating and Analyzing COVID-19 Treatments from EHRs & Registries Globally – CURE ID is a joint initiative between the FDA and the NIH (implemented through the National Center for Advancing Translational Sciences) that includes an online platform and mobile app designed to capture case reports directly from health care providers worldwide. In these reports, health care providers describe how they use existing approved drug therapies in new ways (i.e., “drug repurposing”) for the treatment of infectious diseases for which there are currently limited or no effective therapeutic options. Recognizing the need to better understand the health outcomes of the different repurposed drugs used in the ongoing COVID-19 pandemic, this project will build a repository of COVID-19 case reports in CURE ID and will expand the platform’s capabilities to enable automated extraction and manual data collection from EHRs and clinical disease registries.