Date of Award

January 2020

Document Type

Open Access Thesis

Degree Name

Medical Doctor (MD)



First Advisor

Mona Sharifi


Pediatric obesity is a growing national and global concern with nearly 1 in 5 children in the U.S. affected [1].The American Academy of Pediatrics endorsed expert committee recommendations in 2007 to assist clinicians in pediatric weight management; however, adherence to these recommendations among primary care providers is suboptimal, and measuring adherence in feasible and pragmatic ways is challenging[2-4]. Commonly used quality measures that rely on billing data alone are an inadequate measure of provider attention to weight status in pediatric populations as they do not capture whether providers communicate about elevated body mass index (BMI) and associated medical risks with families. Electronic phenotyping is a unique tool that has the ability to use multiple areas of stored clinical data to group individuals according to pre-defined characteristics such as diagnostic codes, laboratory values or medications. We examined the external validity of a phenotyping algorithm, developed previously by Turer et al and validated in a single health system in Texas, that assesses pediatric providers’ attention to obesity and overweight using structured data from the electronic health record (EHR), to three pediatric primary care practices affiliated with Yale New Haven Health. Well child visit encounters were labeled either “no attention”, “attention to BMI only”, “attention to comorbidity only,” or “attention to BMI and comorbidity”. The performance of the algorithm was evaluated on the ability to predict “no attention”, using chart review as the reference standard. The application of the minimally altered algorithm yielded a sensitivity of 94.0% and a specificity of 79.2% for predicting “no attention”, compared to a sensitivity of 97.9% and a specificity of 94.8% in the original study. Our findings suggest that while electronic phenotyping using structured EHR inputs provides a better evaluation of clinic encounters than use of diagnostic codes alone, methods that incorporate information in unstructured (“free text”) clinical notes may yield better results.


This is an Open Access Thesis.

Open Access

This Article is Open Access