Testing an Automated Approach to Identify Variation in Outcomes among Children with Type 1 Diabetes across Multiple Sites
- PMID: 38584961
- PMCID: PMC10997286
- DOI: 10.1097/pq9.0000000000000602
Testing an Automated Approach to Identify Variation in Outcomes among Children with Type 1 Diabetes across Multiple Sites
Abstract
Introduction: Efficient methods to obtain and benchmark national data are needed to improve comparative quality assessment for children with type 1 diabetes (T1D). PCORnet is a network of clinical data research networks whose infrastructure includes standardization to a Common Data Model (CDM) incorporating electronic health record (EHR)-derived data across multiple clinical institutions. The study aimed to determine the feasibility of the automated use of EHR data to assess comparative quality for T1D.
Methods: In two PCORnet networks, PEDSnet and OneFlorida, the study assessed measures of glycemic control, diabetic ketoacidosis admissions, and clinic visits in 2016-2018 among youth 0-20 years of age. The study team developed measure EHR-based specifications, identified institution-specific rates using data stored in the CDM, and assessed agreement with manual chart review.
Results: Among 9,740 youth with T1D across 12 institutions, one quarter (26%) had two or more measures of A1c greater than 9% annually (min 5%, max 47%). The median A1c was 8.5% (min site 7.9, max site 10.2). Overall, 4% were hospitalized for diabetic ketoacidosis (min 2%, max 8%). The predictive value of the PCORnet CDM was >75% for all measures and >90% for three measures.
Conclusions: Using EHR-derived data to assess comparative quality for T1D is a valid, efficient, and reliable data collection tool for measuring T1D care and outcomes. Wide variations across institutions were observed, and even the best-performing institutions often failed to achieve the American Diabetes Association HbA1C goals (<7.5%).
Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc.
Conflict of interest statement
K.E.W., within the past 36 months, has served as a consultant for Sanofi and Research Triangle Institute. The other authors have no financial interest to declare in relation to the content of this article.
Figures
Similar articles
-
Structured, intensive education maximising engagement, motivation and long-term change for children and young people with diabetes: a cluster randomised controlled trial with integral process and economic evaluation - the CASCADE study.Health Technol Assess. 2014 Mar;18(20):1-202. doi: 10.3310/hta18200. Health Technol Assess. 2014. PMID: 24690402 Free PMC article. Clinical Trial.
-
Evaluation of PCORnet as an Approach to Accessing Electronic Health Record (EHR) Data for Cleft Outcomes Research: Advantages and Limitations.Cleft Palate Craniofac J. 2025 Jan 15:10556656241312747. doi: 10.1177/10556656241312747. Online ahead of print. Cleft Palate Craniofac J. 2025. PMID: 39814520
-
Testing the Use of Data Drawn from the Electronic Health Record to Compare Quality.Pediatr Qual Saf. 2021 Jul 28;6(4):e432. doi: 10.1097/pq9.0000000000000432. eCollection 2021 Jul-Aug. Pediatr Qual Saf. 2021. PMID: 34345748 Free PMC article.
-
Adult patient access to electronic health records.Cochrane Database Syst Rev. 2021 Feb 26;2(2):CD012707. doi: 10.1002/14651858.CD012707.pub2. Cochrane Database Syst Rev. 2021. PMID: 33634854 Free PMC article.
-
Prognostic factors for the development and progression of proliferative diabetic retinopathy in people with diabetic retinopathy.Cochrane Database Syst Rev. 2023 Feb 22;2(2):CD013775. doi: 10.1002/14651858.CD013775.pub2. Cochrane Database Syst Rev. 2023. PMID: 36815723 Free PMC article. Review.
Cited by
-
Hypertension, proteinuria, and RAAS inhibition use in children with systemic lupus erythematosus: Data from a multi-institutional pediatric learning health system.Lupus. 2025 Jul;34(8):832-843. doi: 10.1177/09612033251345201. Epub 2025 May 20. Lupus. 2025. PMID: 40393065 Free PMC article. Clinical Trial.
References
-
- Meltzer LJ, Johnson SB, Pappachan S, et al. . Blood glucose estimations in adolescents with type 1 diabetes: predictors of accuracy and error. J Pediatr Psychol. 2003;28:203–211. - PubMed
-
- Gagnum V, Stene LC, Jenssen TG, et al. . Causes of death in childhood-onset type 1 diabetes: long-term follow-up. Diabet Med. 2017;34:56–63. - PubMed
-
- Provost LP, Murray S. The Health Care Data Guide: Learning from Data for Improvement. John Wiley & Sons; 2011.
Grants and funding
LinkOut - more resources
Full Text Sources
Miscellaneous