. 2025 Jan;27(1):102-110.

doi: 10.1111/dom.15987. Epub 2024 Sep 30.

Developing an automated algorithm for identification of children and adolescents with diabetes using electronic health records from the OneFlorida+ clinical research network

Piaopiao Li¹, Eliot Spector², Khalid Alkhuzam¹, Rahul Patel¹, William T Donahoo³, Sarah Bost², Tianchen Lyu², Yonghui Wu², William Hogan², Mattia Prosperi², Brian E Dixon⁴, Dana Dabelea⁵, Levon H Utidjian⁶, Tessa L Crume⁷, Lorna Thorpe⁸, Angela D Liese⁹, Desmond A Schatz¹⁰, Mark A Atkinson¹¹, Michael J Haller¹⁰, Elizabeth A Shenkman², Yi Guo², Jiang Bian², Hui Shao^{1

12

13}

Affiliations

¹ Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, Florida, USA.
² Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.
³ Division of Endocrinology, Diabetes and Metabolism, College of Medicine, University of Florida, Gainesville, Florida, USA.
⁴ Department of Epidemiology, Indiana University (IU) Richard M. Fairbanks School of Public Health, Indianapolis, Indiana, USA.
⁵ Lifecourse Epidemiology of Adiposity and Diabetes Centre, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.
⁶ Division of General Paediatrics and Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
⁷ Department of Epidemiology, LEAD Centre, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.
⁸ Department of Population Health, NYU Langone Health, New York, New York, USA.
⁹ Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA.
¹⁰ Department of Paediatrics, University of Florida College of Medicine, Gainesville, Florida, USA.
¹¹ Diabetes Institute, University of Florida, Gainesville, Florida, USA.
¹² Hubert Department of Global Health, Rollin School of Public Health, Emory University, Atlanta, Georgia, USA.
¹³ Department of Family and Preventive Medicine, School of Medicine, Emory University, Atlanta, Georgia, USA.

PMID: 39344840
PMCID: PMC11620941 (available on 2026-01-01)
DOI: 10.1111/dom.15987

Developing an automated algorithm for identification of children and adolescents with diabetes using electronic health records from the OneFlorida+ clinical research network

Piaopiao Li et al. Diabetes Obes Metab. 2025 Jan.

. 2025 Jan;27(1):102-110.

doi: 10.1111/dom.15987. Epub 2024 Sep 30.

Authors

Affiliations

¹ Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, Florida, USA.
² Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.
³ Division of Endocrinology, Diabetes and Metabolism, College of Medicine, University of Florida, Gainesville, Florida, USA.
⁴ Department of Epidemiology, Indiana University (IU) Richard M. Fairbanks School of Public Health, Indianapolis, Indiana, USA.
⁵ Lifecourse Epidemiology of Adiposity and Diabetes Centre, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.
⁶ Division of General Paediatrics and Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
⁷ Department of Epidemiology, LEAD Centre, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.
⁸ Department of Population Health, NYU Langone Health, New York, New York, USA.
⁹ Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA.
¹⁰ Department of Paediatrics, University of Florida College of Medicine, Gainesville, Florida, USA.
¹¹ Diabetes Institute, University of Florida, Gainesville, Florida, USA.
¹² Hubert Department of Global Health, Rollin School of Public Health, Emory University, Atlanta, Georgia, USA.
¹³ Department of Family and Preventive Medicine, School of Medicine, Emory University, Atlanta, Georgia, USA.

PMID: 39344840
PMCID: PMC11620941 (available on 2026-01-01)
DOI: 10.1111/dom.15987

Abstract

Aim: To develop an automated computable phenotype (CP) algorithm for identifying diabetes cases in children and adolescents using electronic health records (EHRs) from the UF Health System.

Materials and methods: The CP algorithm was iteratively derived based on structured data from EHRs (UF Health System 2012-2020). We randomly selected 536 presumed cases among individuals aged <18 years who had (1) glycated haemoglobin levels ≥ 6.5%; or (2) fasting glucose levels ≥126 mg/dL; or (3) random plasma glucose levels ≥200 mg/dL; or (4) a diabetes-related diagnosis code from an inpatient or outpatient encounter; or (5) prescribed, administered, or dispensed diabetes-related medication. Four reviewers independently reviewed the patient charts to determine diabetes status and type.

Results: Presumed cases without type 1 (T1D) or type 2 diabetes (T2D) diagnosis codes were categorized as non-diabetes/other types of diabetes. The rest were categorized as T1D if the most recent diagnosis was T1D, or otherwise categorized as T2D if the most recent diagnosis was T2D. Next, we applied a list of diagnoses and procedures that can determine diabetes type (e.g., steroid use suggests induced diabetes) to correct misclassifications from Step 1. Among the 536 reviewed cases, 159 and 64 had T1D and T2D, respectively. The sensitivity, specificity, and positive predictive values of the CP algorithm were 94%, 98% and 96%, respectively, for T1D and 95%, 95% and 73% for T2D.

Conclusion: We developed a highly accurate EHR-based CP for diabetes in youth based on EHR data from UF Health. Consistent with prior studies, T2D was more difficult to identify using these methods.

Keywords: database research; real‐world evidence; type 1 diabetes; type 2 diabetes.

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Conflict of interest statement

Conflicts of Interest: The authors have disclosed no conflicts of interest.

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Li P, Lyu T, Alkhuzam K, Spector E, Donahoo WT, Bost S, Wu Y, Hogan WR, Prosperi M, Schatz DA, Atkinson MA, Haller MJ, Shenkman EA, Guo Y, Bian J, Shao H. Li P, et al. J Am Med Inform Assoc. 2023 Dec 22;31(1):165-173. doi: 10.1093/jamia/ocad194. J Am Med Inform Assoc. 2023. PMID: 37812771 Free PMC article.
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Shao H, Thorpe LE, Islam S, Bian J, Guo Y, Li P, Bost S, Dabelea D, Conway R, Crume T, Schwartz BS, Hirsch AG, Allen KS, Dixon BE, Grannis SJ, Lustigova E, Reynolds K, Rosenman M, Zhong VW, Wong A, Rivera P, Le T, Akerman M, Conderino S, Rajan A, Liese AD, Rudisill C, Obeid JS, Ewing JA, Bailey C, Mendonca EA, Zaganjor I, Rolka D, Imperatore G, Pavkov ME, Divers J; DiCAYA Study Group. Shao H, et al. Diabetes Care. 2025 Jun 1;48(6):914-921. doi: 10.2337/dc24-1972. Diabetes Care. 2025. PMID: 40163581

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Developing an automated algorithm for identification of children and adolescents with diabetes using electronic health records from the OneFlorida+ clinical research network

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Developing an automated algorithm for identification of children and adolescents with diabetes using electronic health records from the OneFlorida+ clinical research network

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