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Master of Public Health

Cleaning of anthropometric data from PCORnet electronic health records using automated algorithms

AUTHORS

Lin PD , Rifas-Shiman SL , Aris IM , Daley MF , Janicke DM , Heerman WJ , Chudnov DL , Freedman DS , Block JP , . JAMIA open. 2022 11 2; 5(4). ooac089

ABSTRACT

Objective: To demonstrate the utility of , an anthropometric data cleaning method designed for electronic health records (EHR).

Materials and Methods: We used all available pediatric and adult height and weight data from an ongoing observational study that includes EHR data from 15 healthcare systems and applied to identify outliers and errors and compared its performance in pediatric data with 2 other pediatric data cleaning methods: (1) conditional percentile () and (2) PaEdiatric ANthropometric measurement Outlier Flagging pipeline ().

Results: 687 226 children (<20 years) and 3 267 293 adults contributed 71 246 369 weight and 51 525 487 height measurements. flagged 18% of pediatric and 12% of adult measurements for exclusion, mostly as carried-forward measures for pediatric data and duplicates for adult and pediatric data. After removing the flagged measurements, 0.5% and 0.6% of the pediatric heights and weights and 0.3% and 1.4% of the adult heights and weights, respectively, were biologically implausible according to the CDC and other established cut points. Compared with other pediatric cleaning methods, flagged the most measurements for exclusion; however, it did not flag some more extreme measurements. The prevalence of severe pediatric obesity was 9.0%, 9.2%, and 8.0% after cleaning by , , and , respectively.

Conclusion: is useful for cleaning pediatric and adult height and weight data. It is the only method with the ability to clean adult data and identify carried-forward and duplicates, which are prevalent in EHR. Findings of this study can be used to improve the algorithm.

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