The exchange of laboratory data without loss of fidelity or clinical intent is the holy grail of health information technology (HIT) interoperability. However, the exchange of lab data and the shared understanding of that information becomes more complex when both LOINC® and SNOMED CT® terminologies are used in tandem to identify tests and report test results. Ultimately, that common understanding remains elusive for clinicians, healthcare organizations, researchers, and public health agencies for a variety of reasons:
- LOINC and SNOMED CT are disparate terminologies with different structures for categorizing and classifying information
- Senders and recipients of data often do not share the same understanding of the meaning of laboratory data, even when it is encoded in LOINC and SNOMED CT terminologies, since neither LOINC nor SNOMED CT are easily or consistently understood by humans
- Result values are specified using different scales and measures, making it difficult for one organization to interpret lab results from another organization
- Researchers, dependent on real-world evidence (RWE) generated by laboratory data to inform drug development and advances in clinical care, are hampered in their work by inconsistencies in data and the lack of a shared understanding of the meaning of data generated from clinical care
- Commercial laboratories handle thousands of tests each day and they depend on consistent, accurate information about tests to calibrate lab analyzers correctly for precise test results
- Public health surveillance and response depends on a shared understanding of the meaning of lab information. Misinterpretation of laboratory data could pose a significant health risk to many people
To understand these challenges, and develop solutions to bring interoperability to laboratory data, we need to look at these issues in depth.
Leveraging LOINC and SNOMED CT for effective clinical care
Clinical laboratory data consists of structured data that represents orders for tests, test results, test values, and interpretations of those tests. While tests are encoded in LOINC terminology, the use of LOINC is inconsistent among laboratories; not well implemented in electronic healthcare record (EHR) systems; and is not widely used in clinical care. Meanwhile, per the Office of the National Coordinator for Health Information Technology’s (ONC) requirements for certified health information systems, test results reported to public health agencies must be encoded in SNOMED CT terminology.1 However, these two terminologies manage the structure of information differently. LOINC is designed to capture laboratory tests and to ask the question what organism or condition is present here? SNOMED CT specifies the concepts, descriptions, and relationships between concepts, and provides answers to the LOINC question.
LOINC terms define the characteristics of the sample to be tested — such as the properties of what is being measured and the method of analysis. This information is critical, as an incorrect analytical method could result in clinically different results.
SNOMED CT concepts for lab results define a clinical finding by substance or microorganism — the properties or components of an observable entity, assessment scales, and qualifier values.
Mapping LOINC codes for capturing laboratory and clinical observations to SNOMED CT concepts that represent lab results is a time-consuming, manual process requiring lab personnel to have in-depth knowledge of each terminology and mastery of subject matter. Both LOINC and SNOMED CT are updated frequently, and keeping up to date with these changes remains a challenge. With many different laboratories processing both familiar and novel tests to inform patient care and research, it is critical that tests and results are correctly and consistently identified and coded in LOINC and SNOMED CT.
Harmonized reference intervals are crucial to patient safety
Lab results are interpreted based on a reference interval that distinguishes between health and disease. Even though a result might be flagged as abnormal by the lab, when lab results exchanged between different clinicians and healthcare organizations are represented in unfamiliar scales and measures, the clinician may be forced to make conversions. Viewing results in context is important, as an error here could lead to a misdiagnosis, incorrect treatment, or risk to patient safety.
The need for accuracy in IVD analyzer configuration
Other variables in this equation include the type and manufacturer of analyzer that a lab might use. During the COVID-19 pandemic, many new in vitro diagnostic (IVD) tests have become available under the FDA’s Emergency Use Authorization (EUA) to detect COVID-19. Laboratory personnel often bore the burden of interpreting analyzer setup from test documentation and selecting the correct coding for tests and results. While many laboratory analyzer manufacturers provided guidance directly to laboratories, it was difficult to maintain and update this information for each lab. In a public health emergency, lack of consistent information on how to identify the correct coding to represent tests and results for specific analyzers could be devastating.
SHIELD: A multi-stakeholder, public-private partnership
While all of these issues may seem insurmountable, they are not. Recent collaborations between professional societies and organizations; academic medical centers; commercial HIT developers; laboratory analyzer manufacturers; and federal agencies have sought new solutions. And a public-private partnership has been formed to drive the development of solutions to address the many obstacles to lab information interoperability.
The 2022 Systemic Harmonization and Interoperability Enhancement for Laboratory Data (SHIELD) plan represents significant progress in these efforts. The plan represents a collaborative effort to create a national strategy for laboratory interoperability and pandemic awareness, tackling each one of the issues described above. The goals of the SHIELD plan are to “protect patient safety, improve laboratory efficiency, reduce consumer and lab data user burden, and make RWE less expensive and more timely.”2 To achieve laboratory information interoperability, plan proposals include:
- Vendor-agnostic harmonization and standardization of coding for laboratory tests and results data
- Making calibration/specification information easily accessible to laboratories using various analyzers to interpret many types of tests
- Establishing an IVD data hub to support regulatory decision making and public health interventions
- Development of a tooling and knowledge management architecture to drive semantic interoperability for the exchange of meaningful data based on common semantic references
Intelligent Medical Objects (IMO) applauds this effort and endorses the goals of SHIELD to elevate laboratory information interoperability aimed at driving the use of data to protect and improve the health of populations, support groundbreaking research, and guide public health interventions. IMO’s 25+ years of facilitating semantic interoperability throughout the healthcare ecosystem – when applied to laboratory data – would be of great value in advancing the objectives of SHIELD. We look forward to following the progress of this exciting initiative and hope to see a reference implementation to demonstrate proof of concept soon.