TY - JOUR
T1 - Algorithm to detect pediatric provider attention to high BMI and associated medical risk
AU - Turer, Christy B.
AU - Skinner, Celette S
AU - Barlow, Sarah Endicott
N1 - Publisher Copyright:
© 2018 The Author(s) 2018. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - We developed and validated an algorithm that uses combinations of extractable electronic-health-record (EHR) indicators (diagnosis codes, orders for laboratories, medications, and referrals) that denote widely-recommended clinician practice behaviors: attention to overweight/obesity/body mass index alone (BMI Alone), with attention to hypertension/other comorbidities (BMI/Medical Risk), or neither (No Attention). Data inputs used for each EHR indicator were refined through iterative chart review to identify and resolve modifiable coding errors. Validation was performed through manual review of randomly selected visit encounters (n = 308) coded by the refined algorithm. Of 104 encounters coded as No Attention, 89.4% lacked any evidence (specificity) of attention to BMI/Medical Risk. Corresponding evidence (sensitivity) of attention to BMI Alone was identified in 96.0% (of 101 encounters coded as BMI Alone) and BMI/Medical Risk in 96.1% (of 103 encounters coded as BMI/Medical Risk). Our EHR data algorithm can validly determine provider attention to BMI alone, with Medical Risk, or neither.
AB - We developed and validated an algorithm that uses combinations of extractable electronic-health-record (EHR) indicators (diagnosis codes, orders for laboratories, medications, and referrals) that denote widely-recommended clinician practice behaviors: attention to overweight/obesity/body mass index alone (BMI Alone), with attention to hypertension/other comorbidities (BMI/Medical Risk), or neither (No Attention). Data inputs used for each EHR indicator were refined through iterative chart review to identify and resolve modifiable coding errors. Validation was performed through manual review of randomly selected visit encounters (n = 308) coded by the refined algorithm. Of 104 encounters coded as No Attention, 89.4% lacked any evidence (specificity) of attention to BMI/Medical Risk. Corresponding evidence (sensitivity) of attention to BMI Alone was identified in 96.0% (of 101 encounters coded as BMI Alone) and BMI/Medical Risk in 96.1% (of 103 encounters coded as BMI/Medical Risk). Our EHR data algorithm can validly determine provider attention to BMI alone, with Medical Risk, or neither.
KW - body mass index
KW - electronic health record
KW - validation
UR - http://www.scopus.com/inward/record.url?scp=85059254012&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85059254012&partnerID=8YFLogxK
U2 - 10.1093/jamia/ocy126
DO - 10.1093/jamia/ocy126
M3 - Article
C2 - 30445547
AN - SCOPUS:85059254012
SN - 1067-5027
VL - 26
SP - 55
EP - 60
JO - Journal of the American Medical Informatics Association
JF - Journal of the American Medical Informatics Association
IS - 1
ER -