Dataset Information

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

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.

SUBMITTER: Bradwell KR

PROVIDER: S-EPMC9196692 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Publications

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

Bradwell Katie R KR Wooldridge Jacob T JT Amor Benjamin B Bennett Tellen D TD Anand Adit A Bremer Carolyn C Yoo Yun Jae YJ Qian Zhenglong Z Johnson Steven G SG Pfaff Emily R ER Girvin Andrew T AT Manna Amin A Niehaus Emily A EA Hong Stephanie S SS Zhang Xiaohan Tanner XT Zhu Richard L RL Bissell Mark M Qureshi Nabeel N Saltz Joel J Haendel Melissa A MA Chute Christopher G CG Lehmann Harold P HP Moffitt Richard A RA

Journal of the American Medical Informatics Association : JAMIA 20220601 7

<h4>Objective</h4>The goals of this study were to harmonize data from electronic health records (EHRs) into common units, and impute units that were missing.<h4>Materials and methods</h4>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 t ...[more]

PMID: 35435957

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Dataset Information

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

Objective

Materials and methods

Results

Discussion

Conclusion

Publications

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

Similar Datasets

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