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Electronic clinical laboratory test results data tables: lessons from Mini‐Sentinel
Author(s) -
Raebel Marsha A.,
Haynes Kevin,
Woodworth Tiffany S.,
Saylor Gwyn,
Cavagnaro Elizabeth,
Coughlin Kara O.,
Curtis Lesley H.,
Weiner Mark G.,
Archdeacon Patrick,
Brown Jeffrey S.
Publication year - 2014
Publication title -
pharmacoepidemiology and drug safety
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.023
H-Index - 96
eISSN - 1099-1557
pISSN - 1053-8569
DOI - 10.1002/pds.3580
Subject(s) - medicine , identifier , harmonization , test (biology) , data quality , medical physics , data mining , computer science , information retrieval , data science , metric (unit) , operations management , engineering , paleontology , physics , acoustics , biology , programming language
Purpose Developing electronic clinical data into a common data model posed substantial challenges unique from those encountered with administrative data. We present here the design, implementation, and use of the Mini‐Sentinel Distributed Database laboratory results table (LRT). Methods We developed the LRT and guided Mini‐Sentinel data partners (DPs) in populating it from their source data. Data sources included electronic health records and internal and contracted clinical laboratory systems databases. We employed the Logical Observation Identifiers, Names, and Codes (LOINC®) results reporting standards. We evaluated transformed results data using data checks and an iterative, ongoing characterization and harmonization process. Results Key LRT variables included test name, subcategory, specimen source, LOINC, patient location, specimen date and time, result unit, and unique person identifier. Selected blood and urine chemistry, hematology, coagulation, and influenza tests were included. Twelve DPs with outpatient test results participated; four also contributed inpatient test results. As of September 2013, the LRT included 385,516,239 laboratory test results; data are refreshed at least quarterly. LOINC availability and use varied across DP. Multiple data quality and content issues were identified and addressed. Conclusion Developing the LRT brought together disparate data sources with no common coding structure. Clinical laboratory test results obtained during routine healthcare delivery are neither uniformly coded nor documented in a standardized manner. Applying a systematic approach with data harmonization efforts and ongoing oversight and management is necessary for a clinical laboratory results data table to remain valid and useful. Copyright © 2014 John Wiley & Sons, Ltd.