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J Am Med Inform Assoc. 2017 Apr 26. doi: 10.1093/jamia/ocx037. [Epub ahead of print]

Automated identification of implausible values in growth data from pediatric electronic health records.

Author information

Departments of Pediatrics and Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA.
Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Department of Biomedical and Health Informatics, Pediatric Research Consortium, Center for Pediatric Clinical Effectiveness, and PolicyLab, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Pediatric Research in Office Settings, American Academy of Pediatrics, Elk Grove, IL, USA.
Department of Pediatrics, University of Vermont, Burlington, VT, USA.
Department of Biomedical and Health Informatics, 4Pediatric Research Consortium, 5Center for Pediatric Clinical Effectiveness, and 6PolicyLab, Children's Hospital of Philadelphia, Philadelphia, PA, USA.



Large electronic health record (EHR) datasets are increasingly used to facilitate research on growth, but measurement and recording errors can lead to biased results. We developed and tested an automated method for identifying implausible values in pediatric EHR growth data.

Materials and Methods:

Using deidentified data from 46 primary care sites, we developed an algorithm to identify weight and height values that should be excluded from analysis, including implausible values and values that were recorded repeatedly without remeasurement. The foundation of the algorithm is a comparison of each measurement, expressed as a standard deviation score, with a weighted moving average of a child's other measurements. We evaluated the performance of the algorithm by (1) comparing its results with the judgment of physician reviewers for a stratified random selection of 400 measurements and (2) evaluating its accuracy in a dataset with simulated errors.


Of 2 000 595 growth measurements from 280 610 patients 1 to 21 years old, 3.8% of weight and 4.5% of height values were identified as implausible or excluded for other reasons. The proportion excluded varied widely by primary care site. The automated method had a sensitivity of 97% (95% confidence interval [CI], 94-99%) and a specificity of 90% (95% CI, 85-94%) for identifying implausible values compared to physician judgment, and identified 95% (weight) and 98% (height) of simulated errors.

Discussion and Conclusion:

This automated, flexible, and validated method for preparing large datasets will facilitate the use of pediatric EHR growth datasets for research.


data quality; growth; research methodology

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