TY - JOUR
T1 - Testing an Automated Approach to Identify Variation in Outcomes among Children with Type 1 Diabetes across Multiple Sites
AU - Addison, Jessica
AU - Razzaghi, Hanieh
AU - Bailey, Charles
AU - Dickinson, Kimberley
AU - Corathers, Sarah D.
AU - Hartley, David M.
AU - Utidjian, Levon
AU - Carle, Adam C.
AU - Rhodes, Erinn T.
AU - Alonso, G. Todd
AU - Haller, Michael J.
AU - Gannon, Anthony W.
AU - Indyk, Justin A.
AU - Arbeláez, Ana Maria
AU - Shenkman, Elizabeth
AU - Forrest, Christopher B.
AU - Eckrich, Daniel
AU - Magnusen, Brianna
AU - Davies, Sara Deakyne
AU - Walsh, Kathleen E.
N1 - Funding Information:
Research reported in this report was funded through a Patient-Centered Outcomes Research Institute (PCORI) Award (PCORI HSD-1604-35160). The views, statements, and opinions in this report are solely the authors’ responsibility. They do not necessarily represent the views of the PCORI, its Board of Governors, or the Methodology Committee. Research reported in this publication was supported in part by the OneFlorida+ Clinical Research Network, funded by the PCORI numbers CDRN-1501-26692; in part by the OneFlorida+ Cancer Control Alliance, funded by the Florida Department of Health’s James and Esther King Biomedical Research Program number 4KB16; and in part by the University of Florida Clinical and Translational Science Institute, which is supported in part by the NIH National Center for Advancing Translational Sciences under award number UL1TR001427. However, the content is solely the responsibility of the authors. It does not necessarily represent the official views of the PCORI, its Board of Governors or Methodology, the OneFlorida+ Clinical Research Network, the UF-FSU Clinical and Translational Science Institute, the Florida Department of Health, or the National Institutes of Health.
Publisher Copyright:
© 2022 by the Author(s).
PY - 2022/9/8
Y1 - 2022/9/8
N2 - 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%).
AB - 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%).
UR - http://www.scopus.com/inward/record.url?scp=85139281866&partnerID=8YFLogxK
U2 - 10.1097/pq9.0000000000000602
DO - 10.1097/pq9.0000000000000602
M3 - Article
AN - SCOPUS:85139281866
SN - 2472-0054
VL - 7
SP - E602
JO - Pediatric Quality and Safety
JF - Pediatric Quality and Safety
IS - 5
ER -