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Encoding laboratory testing data: Case studies of the national implementation of HHS requirements and related standards in five laboratories

Citation

Cholan, Raja et al. (2022), Encoding laboratory testing data: Case studies of the national implementation of HHS requirements and related standards in five laboratories, Dryad, Dataset, https://doi.org/10.5061/dryad.0cfxpnw55

Abstract

OBJECTIVE: Assess the effectiveness of providing Logical Observation Identifiers Names and Codes (LOINC®)-to-In Vitro Diagnostic (LIVD) coding specification, required by the Department of Health and Human Services for SARS-CoV-2 reporting, in medical center laboratories, and utilize findings to inform future Food and Drug Administration policy on the use of real-world evidence in regulatory decisions.

MATERIALS AND METHODS: We compared gaps and similarities between diagnostic test manufacturers’ recommended LOINC® codes and the LOINC® codes used in medical center laboratories for the same tests.

RESULTS: Five medical centers and three test manufacturers extracted data from laboratory information systems (LIS) for prioritized tests of interest. The data submission ranged from 74 to 532 LOINC® codes per site. Three test manufacturers submitted 15 LIVD catalogs representing 26 distinct devices, 6,956 tests, and 686 LOINC® codes. We identified mismatches in how medical centers use LOINC® to encode laboratory tests compared to how test manufacturers encode the same laboratory tests. Of 331 tests available in the LIVD files, 136 (41%) were represented by a mismatched LOINC® code by the medical centers (Chi-square 45.0,4 df,p < .0001).

DISCUSSION: The five medical centers and three test manufacturers vary in how they organize, categorize, and store LIS catalog information. This variation impacts data quality and interoperability.

CONCLUSION: The results of the study indicate that providing the LIVD mappings was not sufficient to support laboratory data interoperability. National implementation of LIVD and further efforts to promote laboratory interoperability will require a more comprehensive effort and continuing evaluation and quality control.

Methods

Five medical centers and three test manufacturers extracted data from laboratory information systems (LIS) for prioritized tests of interest. The data submission ranged from 74 to 532 LOINC® codes per site. Three test manufacturers submitted 15 LIVD catalogs representing 26 distinct devices, 6,956 tests, and 686 LOINC® codes. We identified mismatches in how medical centers use LOINC® to encode laboratory tests compared to how test manufacturers encode the same laboratory tests. Of 331 tests available in the LIVD files, 136 (41%) were represented by a mismatched LOINC® code by the medical centers (Chi-square 45.0,4 df,p < .0001).

Data Collection from Medical Center Laboratory Pilot Sites: Each medical center was asked to extract about 100 LOINC® Codes from their LIS for prioritized tests of interest focused on high-risk conditions and SARS-CoV-2. For each selected test (e.g., SARS-CoV-2 RNA COVID-19), we collected the following data elements: test names/descriptions (e.g., SARS coronavirus 2 RNA [Presence] in Respiratory specimen by NAA with probe detection), associated instruments (e.g., IVD Vendor Model), and LOINC® codes (e.g., 94500-6). High risk conditions were defined by referencing the CDC’s published list of Underlying Medical Conditions Associated with High Risk for Severe COVID-19.[29] A data collection template spreadsheet was created and disseminated to the medical centers to help provide consistency and reporting clarity for data elements from sites.

Data Collection from IVD Manufacturers: We coordinated with SHIELD stakeholders and the IICC to request manufacturer LIVD catalogs containing the LOINC® codes per IVD instrument per test from manufacturers.

Funding

Food and Drug Administration, Award: 75F40119C10164