Abstract
Objectives The 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.
Methods We 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.
Results The 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 (p < 0.001).
Conclusion The 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.
Keywords
data validation and verification - primary care - pediatrics - specific conditions
- quality - EHR systems
