Efficient Surveillance of Childhood Diabetes Using Electronic Health Record Data


  • Victor W. Zhong
  • Jihad S. Obeid
  • Jean B. Craig
  • Emily R. Pfaff
  • Joan Thomas
  • Lindsay M. Jaacks
  • Daniel P. Beavers
  • Timothy S. Carey
  • Jean M. Lawrence
  • Dana Dabelea
  • Richard F. Hamman
  • Deborah A. Bowlby
  • Catherine Pihoker
  • Sharon H. Saydah
  • Elizabeth J. Mayer-Davis




We aimed to develop an efficient surveillance approach for childhood diabetes. We analyzed EHR data from two independent US academic health care systems. Presumptive diabetes cases were identified as those having >1 of the five diabetes indicators in the past 3.5 years. EHRs of the presumptive cases were manually reviewed. We developed a stepwise surveillance approach using billing codes-based pre-specified algorithms and targeted manual EHRs review. The sensitivity and positive predictive value in both systems were approximately >90%. This stepwise surveillance approach resulted in a >70% reduction in the number of cases requiring manual validation compared to traditional surveillance methods.




How to Cite

Zhong, V. W., Obeid, J. S., Craig, J. B., Pfaff, E. R., Thomas, J., Jaacks, L. M., Beavers, D. P., Carey, T. S., Lawrence, J. M., Dabelea, D., Hamman, R. F., Bowlby, D. A., Pihoker, C., Saydah, S. H., & Mayer-Davis, E. J. (2016). Efficient Surveillance of Childhood Diabetes Using Electronic Health Record Data. Online Journal of Public Health Informatics, 8(1). https://doi.org/10.5210/ojphi.v8i1.6459



Oral Presentations