Journal
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
Volume 23, Issue 6, Pages 1060-1067Publisher
OXFORD UNIV PRESS
DOI: 10.1093/jamia/ocv207
Keywords
automated algorithm; ascertainment and classification; childhood diabetes; electronic health records; surveillance
Categories
Funding
- Centers for Disease Control and Prevention [00097, DP-05-069, DP-10-001]
- National Institute of Diabetes and Digestive and Kidney Diseases [U58/CCU019235-4, U01-DP-000244, U18-DP-002710-01, U01-DP-000250, 200-2010-35171]
- National Institutes of Health Clinical and Translational Science Awards [UL1 TR00423]
- University of Colorado Pediatric Clinical and Translational Research Center [UL1 TR000154]
- National Center for Advancing Translational Sciences, National Institutes of Health [8 UL1 TR000077]
- NIH - National Center for Advancing Translational Sciences [UL1 TR000062]
- The National Institute of Diabetes and Digestive and Kidney Diseases [U48/CCU919219, U01-DP-000246, U18-DP-002714, U48/CCU819241-3, U01-DP-000247, U18-DP-000247-06A1, U48/CCU519239, U01-DP-000248, 1U18-DP-002709, U48/CCU419249, U01-DP-000254, U18-DP-002708-01]
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Objective To develop an efficient surveillance approach for childhood diabetes by type across 2 large US health care systems, using phenotyping algorithms derived from electronic health record (EHR) data. Materials and Methods Presumptive diabetes cases < 20 years of age from 2 large independent health care systems were identified as those having >= 1 of the 5 indicators in the past 3.5 years, including elevated HbA1c, elevated blood glucose, diabetes-related billing codes, patient problem list, and outpatient anti-diabetic medications. EHRs of all the presumptive cases were manually reviewed, and true diabetes status and diabetes type were determined. Algorithms for identifying diabetes cases overall and classifying diabetes type were either prespecified or derived from classification and regression tree analysis. Surveillance approach was developed based on the best algorithms identified. Results We developed a stepwise surveillance approach using billing code-based prespecified algorithms and targeted manual EHR review, which efficiently and accurately ascertained and classified diabetes cases by type, in both health care systems. The sensitivity and positive predictive values in both systems were approximately >= 90% for ascertaining diabetes cases overall and classifying cases with type 1 or type 2 diabetes. About 80% of the cases with other type were also correctly classified. This stepwise surveillance approach resulted in a > 70% reduction in the number of cases requiring manual validation compared to traditional surveillance methods. Conclusion EHR data may be used to establish an efficient approach for large-scale surveillance for childhood diabetes by type, although some manual effort is still needed.
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