Automated Identification of Implausible Values in Growth Data from Pediatric Electronic Health Records

Reports the development of an automated method for identifying implausible values in pediatric EHR growth (weight and height) data, tested via data points collected in the primary care environment on over 280,000 patiets. The method compares each measurement’s z-score to a weighted moving average of prior measurements. The method had a sensitivity of 97% and a specificity of 90% for identifying implausible values compared to physician judgment, and identified almost all simulated errors.

Daymont 2017 (JAMIA) | PubMed 28453637 | Author Search

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