Harmonizing units and values of quantitative data elements in a very large nationally pooled electronic health record (EHR) dataset. (18th April 2022)

• Record Type: Journal Article
• Authors: Bradwell, Katie R; Wooldridge, Jacob T; Amor, Benjamin; Bennett, Tellen D; Anand, Adit; Bremer, Carolyn; Yoo, Yun Jae; Qian, Zhenglong; Johnson, Steven G; Pfaff, Emily R; Girvin, Andrew T; Manna, Amin; Niehaus, Emily A; Hong, Stephanie S; Zhang, Xiaohan Tanner; Zhu, Richard L; Bissell, Mark; Qureshi, Nabeel; Saltz, Joel; Haendel, Melissa A; Chute, Christopher G; Lehmann, Harold P; Moffitt, Richard A
• Is Part Of: Journal of the American Medical Informatics Association. Volume 29:Number 7(2022)
• Page Start: 1172
• Page End: 1182
• Publication Date: 2022-04-18
• Subjects: reference standards -- SARS-CoV-2 -- electronic health records -- data accuracy -- data collection; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocac054
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: The goals of this study were to harmonize data from electronic health records (EHRs) into common units, and impute units that were missing. Materials and Methods: The National COVID Cohort Collaborative (N3C) table of laboratory measurement data—over 3.1 billion patient records and over 19 000 unique measurement concepts in the Observational Medical Outcomes Partnership (OMOP) common-data-model format from 55 data partners. We grouped ontologically similar OMOP concepts together for 52 variables relevant to COVID-19 research, and developed a unit-harmonization pipeline comprised of (1) selecting a canonical unit for each measurement variable, (2) arriving at a formula for conversion, (3) obtaining clinical review of each formula, (4) applying the formula to convert data values in each unit into the target canonical unit, and (5) removing any harmonized value that fell outside of accepted value ranges for the variable. For data with missing units for all the results within a lab test for a data partner, we compared values with pooled values of all data partners, using the Kolmogorov-Smirnov test. Results: Of the concepts without missing values, we harmonized 88.1% of the values, and imputed units for 78.2% of records where units were absent (41% of contributors' records lacked units). Discussion: The harmonization and inference methods developed herein can serve as a resource for initiatives aiming to extract insight from heterogeneous EHR collections. Unique properties of centralized data are harnessed to enable unit inference. Conclusion: The pipeline we developed for the pooled N3C data enables use of measurements that would otherwise be unavailable for analysis.