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

ABSTRACT:

Objective

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) com

SUBMITTER: Daymont C 

PROVIDER: S-EPMC7651915 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature