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Testing an Automated Approach to Identify Variation in Outcomes among Children with Type 1 Diabetes across Multiple
Jessica Addison1, Hanieh Razzaghi2,3, Charles Bailey2,3
1From the Division of Adolescent and Young Adult Medicine, Boston Children's Hospital, Boston, Mass.
Insights
Automated electronic health record (EHR) data effectively assesses quality of care for youth with type 1 diabetes (T1D). Significant institutional variations in glycemic control and outcomes highlight a need for improved diabetes management strategies.
Area of Science:
- Pediatric Endocrinology
- Health Services Research
- Data Science in Healthcare
Background:
- Comparative quality assessment for type 1 diabetes (T1D) in children requires efficient data collection methods.
- PCORnet's Common Data Model (CDM) standardizes electronic health record (EHR)-derived data across multiple institutions.
Purpose of the Study:
- To determine the feasibility of using automated EHR data for comparative quality assessment in pediatric T1D.
- To evaluate the reliability and validity of EHR data for measuring T1D care and outcomes.
Main Methods:
- Assessed glycemic control, diabetic ketoacidosis (DKA) admissions, and clinic visits from 2016-2018 in two PCORnet networks (PEDSnet, OneFlorida).
- Developed EHR-based measure specifications and identified institution-specific rates using CDM data.
- Validated findings through agreement assessment with manual chart review.
Main Results:
- Analyzed data from 9,740 youth with T1D across 12 institutions.
- Found 26% had HbA1c >9% annually; median HbA1c was 8.5%.
- 4% were hospitalized for DKA; PCORnet CDM predictive value exceeded 75% for all measures.
Conclusions:
- Automated EHR data is a valid, efficient, and reliable tool for assessing T1D care quality.
- Observed significant institutional variations in care, with many institutions not meeting American Diabetes Association HbA1c goals (<7.5%).
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%).
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