ENHANCING REAL-WORLD DATA COMPLETENESS IN TYPE 2 DIABETES THROUGH LINKAGE OF CLAIMS, EMR, AND LABORATORY DATA
Author(s)
Kacper Perkowski, MA1, Laura Graf, MPH2;
1Pittsburgh, PA, USA, 2HealthVerity, Philadelphia, PA, USA
1Pittsburgh, PA, USA, 2HealthVerity, Philadelphia, PA, USA
OBJECTIVES: Evaluate the completeness and analytical value of linking closed claims, electronic medical records (EMR), and laboratory data in capturing body mass index (BMI) and hemoglobin A1c (HbA1c) among patients with Type 2 diabetes (T2D).
METHODS: Patients with T2D diagnoses between January 1, 2021 and August 31, 2025, were identified in closed medical claims and EMR. Separate cohorts were constructed for BMI and HbA1c. BMI was obtained from ICD-10 codes in claims and clinical observations in EMR. HbA1c values were derived from EMR, laboratory data, and diagnosis codes in claims. Completeness and research value were assessed using patient overlap among sources, measurement distribution, measurements by source, and measurement recency.
RESULTS: 12,921,621 patients met inclusion criteria. Adding EMR to claims data increased the BMI cohort by 39.4%, while inclusion of EMR and laboratory data expanded the HbA1c cohort by 87.1%. Variation in BMI distributions across claims and EMR reflect HealthVerity’s privacy certification and differences in coding practices. HbA1c distributions were consistent across EMR and laboratory sources. Claims exhibited greater measurement density for BMI during 2021-2023 and greater density for HbA1c during 2021-2024; laboratory data surpassed claims in HbA1c measurements during 2025. EMR provided more recent BMI observations than claims, and laboratory data contained the most recent HbA1c values. Concordance analyses showed moderate alignment: 85.7% of BMI claims aligned with EMR values, and 76.4% of hyperglycemia claims corresponded with an elevated HbA1c result.
CONCLUSIONS: In T2D research, claims provide value through broad volume and measurement density. Complementing claims with EMR and laboratory data enhances completeness and clinical depth. Variability in measurement distributions reflect nuances in data origin, while temporal analyses highlight complementary strengths across sources. These findings underscore the value of linked data assets to enhance completeness, timeliness, and validity of evidence for diabetes outcomes research.
METHODS: Patients with T2D diagnoses between January 1, 2021 and August 31, 2025, were identified in closed medical claims and EMR. Separate cohorts were constructed for BMI and HbA1c. BMI was obtained from ICD-10 codes in claims and clinical observations in EMR. HbA1c values were derived from EMR, laboratory data, and diagnosis codes in claims. Completeness and research value were assessed using patient overlap among sources, measurement distribution, measurements by source, and measurement recency.
RESULTS: 12,921,621 patients met inclusion criteria. Adding EMR to claims data increased the BMI cohort by 39.4%, while inclusion of EMR and laboratory data expanded the HbA1c cohort by 87.1%. Variation in BMI distributions across claims and EMR reflect HealthVerity’s privacy certification and differences in coding practices. HbA1c distributions were consistent across EMR and laboratory sources. Claims exhibited greater measurement density for BMI during 2021-2023 and greater density for HbA1c during 2021-2024; laboratory data surpassed claims in HbA1c measurements during 2025. EMR provided more recent BMI observations than claims, and laboratory data contained the most recent HbA1c values. Concordance analyses showed moderate alignment: 85.7% of BMI claims aligned with EMR values, and 76.4% of hyperglycemia claims corresponded with an elevated HbA1c result.
CONCLUSIONS: In T2D research, claims provide value through broad volume and measurement density. Complementing claims with EMR and laboratory data enhances completeness and clinical depth. Variability in measurement distributions reflect nuances in data origin, while temporal analyses highlight complementary strengths across sources. These findings underscore the value of linked data assets to enhance completeness, timeliness, and validity of evidence for diabetes outcomes research.
Conference/Value in Health Info
2026-05, ISPOR 2026, Philadelphia, PA, USA
Value in Health, Volume 29, Issue S6
Code
SA1
Topic
Study Approaches
Disease
SDC: Diabetes/Endocrine/Metabolic Disorders (including obesity)