ArticleApplied clinical informatics2024
External Validation of an Electronic Phenotyping Algorithm Detecting Attention to High Body Mass Index in Pediatric Primary Care.
Article in Applied clinical informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- A Novel Digital Phenotype for Burn Sepsis: Leveraging Electronic Health Record Data and Natural Language Processing to Improve Case Definition.Applied clinical informatics · 2026Article
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5 authors.
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Abstract
objectivesThe lack of feasible and meaningful measures of clinicians' behavior hinders efforts to assess and improve obesity management in pediatric primary care. In this study, we examined the external validity of a novel algorithm, previously validated in a single geographic region, using structured electronic health record (EHR) data to identify phenotypes of clinicians' attention to elevated body mass index (BMI) and weight-related comorbidities.
methodsWe extracted structured EHR data for 300 randomly selected 6- to 12-year-old children with elevated BMI seen for well-child visits from June 2018 to May 2019 at pediatric primary care practices affiliated with Yale. Using diagnosis codes, laboratory orders, referrals, and medications adapted from the original algorithm, we categorized encounters as having evidence of attention to BMI only, weight-related comorbidities only, or both BMI and comorbidities. We evaluated the algorithm's sensitivity and specificity for detecting any attention to BMI and/or comorbidities using chart review as the reference standard.
resultsThe adapted algorithm yielded a sensitivity of 79.2% and specificity of 94.0% for identifying any attention to high BMI/comorbidities in clinical documentation. Of 86 encounters labeled as "no attention" by the algorithm, 83% had evidence of attention in free-text components of the progress note. The likelihood of classification as "any attention" by both chart review and the algorithm varied by BMI category and by clinician type (
conclusionThe electronic phenotyping algorithm had high specificity for detecting attention to high BMI and/or comorbidities in structured EHR inputs. The algorithm's performance may be improved by incorporating unstructured data from clinical notes.
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