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

被引：14

作者：

Bradwell, Katie R. ^{[1
]}

Wooldridge, Jacob T. ^{[2
]}

Amor, Benjamin ^{[1
]}

Bennett, Tellen D. ^{[3
]}

Anand, Adit ^{[2
]}

Bremer, Carolyn ^{[2
]}

Yoo, Yun Jae ^{[2
]}

Qian, Zhenglong ^{[2
]}

Johnson, Steven G. ^{[4
]}

Pfaff, Emily R. ^{[5
]}

Girvin, Andrew T. ^{[1
]}

Manna, Amin ^{[1
]}

Niehaus, Emily A. ^{[1
]}

Hong, Stephanie S. ^{[6
]}

Zhang, Xiaohan Tanner ^{[7
]}

Zhu, Richard L. ^{[7
]}

Bissell, Mark ^{[1
]}

Qureshi, Nabeel ^{[1
]}

Saltz, Joel ^{[2
]}

Haendel, Melissa A. ^{[8
]}

Chute, Christopher G. ^{[9
,10
,11
]}

Lehmann, Harold P. ^{[7
]}

Moffitt, Richard A. ^{[2
]}

机构：

[1] Palantir Technol, Denver, CO USA

[2] SUNY Stony Brook, Dept Biomed Informat, MART L7 0810, Stony Brook, NY 11794 USA

[3] Univ Colorado, Sch Med, Dept Pediat, Sect Informat & Data Sci, Aurora, CO USA

[4] Univ Minnesota, Inst Hlth Informat, Minneapolis, MN USA

[5] Univ N Carolina, North Carolina Translat & Clin Sci Inst, Dept Med, Chapel Hill, NC 27515 USA

[6] Johns Hopkins Univ, Sch Med, Sect Biomed Informat & Data Sci, Baltimore, MD USA

[7] Johns Hopkins, Dept Med, Baltimore, MD USA

[8] Univ Colorado, Ctr Hlth AI, Aurora, CO USA

[9] Johns Hopkins Univ, Sch Med, Baltimore, MD USA

[10] Johns Hopkins Univ, Sch Publ Hlth, Baltimore, MD USA

[11] Johns Hopkins Univ, Sch Nursing, Baltimore, MD USA

来源：

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION | 2022年 / 29卷 / 07期

基金：

美国国家卫生研究院;

关键词：

reference standards; SARS-CoV-2; electronic health records; data accuracy; data collection; LOINC; CONVERSIONS;

D O I：

10.1093/jamia/ocac054

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：1172 / 1182

页数：11

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