Can use of a computerized clinical decision support system help decrease barriers to screening for and diagnosis of prediabetes and type 2 diabetes in pediatric patients?
In this cluster-randomized clinical trial performed in 4 pediatric clinics that included 1369 patients, computerized clinical decision support significantly increased the rates of screening for prediabetes and type 2 diabetes among pediatric patients meeting the risk criteria compared with patients in a control group of clinics.
Use of a computerized clinical decision support system can help overcome barriers and significantly increase the rates of screening and clinical follow-up for prediabetes and type 2 diabetes in pediatric patients.
Type 2 diabetes (T2D) is increasingly common in young individuals. Primary prevention and screening among children and adolescents who are at substantial risk for T2D are recommended, but implementation of T2D screening practices in the pediatric primary care setting is uncommon.
To determine the feasibility and effectiveness of a computerized clinical decision support system to identify pediatric patients at high risk for T2D and to coordinate screening for and diagnosis of prediabetes and T2D.
Design, Setting, and Participants
This cluster-randomized clinical trial included patients from 4 primary care pediatric clinics. Two clinics were randomized to the computerized clinical decision support intervention, aimed at physicians, and 2 were randomized to the control condition. Patients of interest included children, adolescents, and young adults 10 years or older. Data were collected from January 1, 2013, through December 1, 2016.
Comparison of physician screening and follow-up practices after adding a T2D module to an existing computer decision support system.
Main Outcomes and Measures
Electronic medical record (EMR) data from patients 10 years or older were reviewed to determine the rates at which pediatric patients were identified as having a body mass index (BMI) at or above the 85th percentile and 2 or more risk factors for T2D and underwent screening for T2D.
Medical records were reviewed for 1369 eligible children (712 boys [52.0%] and 657 girls [48.0%]; median [interquartile range] age, 12.9 [11.2-15.3]), of whom 684 were randomized to the control group and 685 to the intervention group. Of these, 663 (48.4%) had a BMI at or above the 85th percentile. Five hundred sixty-five patients (41.3%) met T2D screening criteria, with no difference between control and intervention sites. The T2D module led to a significant increase in the percentage of patients undergoing screening for T2D (89 of 283 [31.4%] vs 26 of 282 [9.2%]; adjusted odds ratio, 4.6; 95% CI, 1.5-14.7) and a greater proportion attending a scheduled follow-up appointment (45 of 153 [29.4%] vs 38 of 201 [18.9%]; adjusted odds ratio, 1.8; 95% CI, 1.5-2.2).
Conclusions and Relevance
Use of a computerized clinical decision support system to automate the identification and screening of pediatric patients at high risk for T2D can help overcome barriers to the screening process. The support system significantly increased screening among patients who met the American Diabetes Association criteria and adherence to follow-up appointments with primary care clinicians.
clinicaltrials.gov Identifier: NCT01814787
Hannon TS, Dugan TM, Saha CK, McKee SJ, Downs SM, Carroll AE. Effectiveness of Computer Automation for the Diagnosis and Management of Childhood Type 2 DiabetesA Randomized Clinical Trial. JAMA Pediatr. 2017;171(4):327-334. doi:10.1001/jamapediatrics.2016.4207