LabRS: A Rosetta stone for retrospective standardization of clinical laboratory test results

Objective Clinical laboratories in the United States do not have an explicit result standard to report the 7 billion laboratory tests results they produce each year. The absence of standardized test results creates inefficiencies and ambiguities for secondary data users. We developed and tested a tool to standardize the results of laboratory tests in a large, multicenter clinical data warehouse. Methods Laboratory records, each of which consisted of a laboratory result and a test identifier, from 27 diverse facilities were captured from 2000 through 2015. Each record underwent a standardization process to convert the original result into a format amenable to secondary data analysis. The standardization process included the correction of typos, normalization of categorical results, separation of inequalities from numbers, and conversion of numbers represented by words (eg, "million") to numerals. Quality control included expert review. Results We obtained 1.266 × 109 laboratory records and standardized 1.252 × 109 records (98.9%). Of the unique unstandardized records (78.887 × 103), most appeared <5 times (96%, eg, typos), did not have a test identifier (47%), or belonged to an esoteric test with <100 results (2%). Overall, these 3 reasons accounted for nearly all unstandardized results (98%). Conclusion Current results suggest that the tool is both scalable and generalizable among diverse clinical laboratories. Based on observed trends, the tool will require ongoing maintenance to stay current with new tests and result formats. Future work to develop and implement an explicit standard for test results would reduce the need to retrospectively standardize test results.

[1]  George D. Lundberg,et al.  Adding outcome as the 10th step in the brain-to-brain laboratory test loop. , 2014, American journal of clinical pathology.

[2]  Julie J McGowan,et al.  Electronic laboratory data quality and the value of a health information exchange to support public health reporting processes. , 2011, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[3]  Kevin Haynes,et al.  Electronic clinical laboratory test results data tables: lessons from Mini‐Sentinel , 2014, Pharmacoepidemiology and drug safety.

[4]  Tim Benson,et al.  The history of the Read Codes: the inaugural James Read Memorial Lecture 2011. , 2011, Informatics in primary care.

[5]  Sheldon Brown,et al.  Veterans Aging Cohort Study (VACS): Overview and Description , 2006, Medical care.

[6]  Daniel J. Vreeman,et al.  Logical Observation Identifiers Names and Codes (LOINC®) users' guide , 2010 .

[7]  Peter L Perrotta,et al.  Test Cancellation: A College of American Pathologists Q-Probes Study. , 2016, Archives of pathology & laboratory medicine.

[8]  Robert Gillespie,et al.  One Size Does Not Fit All: Interpreting Laboratory Data in Pediatric Patients , 2003, AMIA.

[9]  Antoine Zimmermann,et al.  The Unified Code for Units of Measure in RDF: cdt: ucum and other UCUM Datatypes , 2018, ESWC.

[10]  Brian H Shirts,et al.  Do we now know what inappropriate laboratory utilization is? An expanded systematic review of laboratory clinical audits. , 2014, American journal of clinical pathology.

[11]  C. McDonald,et al.  Logical observation identifier names and codes (LOINC) database: a public use set of codes and names for electronic reporting of clinical laboratory test results. , 1996, Clinical chemistry.