RESUMO
BACKGROUND: Electronic health record (EHR) data, collected primarily for individual patient care and billing purposes, compiled in health information exchanges (HIEs) may have a secondary use for population health surveillance of noncommunicable diseases. However, data compilation across fragmented data sources into HIEs presents potential barriers and quality of data is unknown. METHODS: We compared 2015 patient data from a mid-size health system (Database A) to data from System A patients in the Utah HIE (Database B). We calculated concordance of structured data (sex and age) and unstructured data (blood pressure reading and A1C). We estimated adjusted hypertension and diabetes prevalence in each database and compared these across age groups. RESULTS: Matching resulted in 72,356 unique patients. Concordance between Database A and Database B exceeded 99% for sex and age, but was 89% for A1C results and 54% for blood pressure readings. Sensitivity, using Database A as the standard, was 57% for hypertension and 55% for diabetes. Age and sex adjusted prevalence of diabetes (8.4% vs 5.8%, Database A and B, respectively) and hypertension (14.5% vs 11.6%, respectively) differed, but this difference was consistent with parallel slopes in prevalence over age groups in both databases. CONCLUSIONS: We identified several gaps in the use of HIE data for surveillance of diabetes and hypertension. High concordance of structured data demonstrate some promise in HIEs capacity to capture patient data. Improving HIE data quality through increased use of structured variables may help make HIE data useful for population health surveillance in places with fragmented EHR systems.