{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T21:11:47Z","timestamp":1775164307678,"version":"3.50.1"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>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\u2019s 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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Of 2\u2009000\u2009595 growth measurements from 280\u2009610 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\u201399%) and a specificity of 90% (95% CI, 85\u201394%) for identifying implausible values compared to physician judgment, and identified 95% (weight) and 98% (height) of simulated errors.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Discussion and Conclusion<\/jats:title>\n                  <jats:p>This automated, flexible, and validated method for preparing large datasets will facilitate the use of pediatric EHR growth datasets for research.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocx037","type":"journal-article","created":{"date-parts":[[2017,3,17]],"date-time":"2017-03-17T20:10:26Z","timestamp":1489781426000},"page":"1080-1087","source":"Crossref","is-referenced-by-count":117,"title":["Automated identification of implausible values in growth data from pediatric electronic health records"],"prefix":"10.1093","volume":"24","author":[{"given":"Carrie","family":"Daymont","sequence":"first","affiliation":[{"name":"Departments of Pediatrics and Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA"}]},{"given":"Michelle E","family":"Ross","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA"}]},{"given":"A","family":"Russell Localio","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA"}]},{"given":"Alexander G","family":"Fiks","sequence":"additional","affiliation":[{"name":"Department of Biomedical and Health Informatics,"},{"name":"Pediatric Research Consortium,"},{"name":"Center for Pediatric Clinical Effectiveness,"},{"name":"PolicyLab, Children\u2019s Hospital of Philadelphia, Philadelphia, PA, USA,"},{"name":"Pediatric Research in Office Settings, American Academy of Pediatrics, Elk Grove, IL, USA"}]},{"given":"Richard C","family":"Wasserman","sequence":"additional","affiliation":[{"name":"Pediatric Research in Office Settings, American Academy of Pediatrics, Elk Grove, IL, USA"},{"name":"Department of Pediatrics, University of Vermont, Burlington, VT, USA"}]},{"given":"Robert W","family":"Grundmeier","sequence":"additional","affiliation":[{"name":"Department of Biomedical and Health Informatics,"}]}],"member":"286","published-online":{"date-parts":[[2017,4,26]]},"reference":[{"issue":"3","key":"2020110612380147200_ocx037-B1","doi-asserted-by":"crossref","first-page":"237","DOI":"10.3109\/17477160903268308","article-title":"Body weight and height data in electronic medical records of children","volume":"5","author":"Smith","year":"2010","journal-title":"Int J Pediatr Obes."},{"issue":"3","key":"2020110612380147200_ocx037-B2","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1093\/ije\/dyq252","article-title":"Cohort profile: the KPSC Children\u2019s Health Study, a population-based study of 920\u2009000 children and adolescents in southern California","volume":"41","author":"Koebnick","year":"2012","journal-title":"Int J Epidemiol."},{"key":"2020110612380147200_ocx037-B3","volume-title":"The Learning Healthcare System: Workshop Summary","author":"Institute of Medicine","year":"2007"},{"key":"2020110612380147200_ocx037-B4","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511661655","volume-title":"Growth, Maturation, and Body Composition: The Fels Longitudinal Study 1929\u20131991","author":"Roche","year":"1992"},{"issue":"4","key":"2020110612380147200_ocx037-B5","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1093\/aje\/kwv057","article-title":"Comparing methods for identifying biologically implausible values in height, weight, and body mass index among youth","volume":"182","author":"Lawman","year":"2015","journal-title":"Am J Epidemiol."},{"issue":"15","key":"2020110612380147200_ocx037-B6","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1056\/NEJM199910073411501","article-title":"Body-mass index and mortality in a prospective cohort of U.S. adults","volume":"341","author":"Calle","year":"1999","journal-title":"N Engl J Med."},{"issue":"4","key":"2020110612380147200_ocx037-B7","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1079\/PHN2001322","article-title":"Validity of self-reported height and weight in 4808 EPIC-Oxford participants","volume":"5","author":"Spencer","year":"2002","journal-title":"Public Health Nutr."},{"key":"2020110612380147200_ocx037-B8","unstructured":"Winkler\n              W\n            \n          . \nProblems with Inliers. 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