Using Calibration to Reduce Measurement Error in Prevalence Estimates Based on Electronic Health Records

INTRODUCTION: Increasing adoption of electronic health record (EHR) systems by health care providers presents an opportunity for EHR-based population health surveillance. EHR data, however, may be subject to measurement error because of factors such as data entry errors and lack of documentation by physicians. We investigated the use of a calibration model to reduce bias of prevalence estimates from the New York City (NYC) Macroscope, an EHR-based surveillance system. METHODS: We calibrated 6 health indicators to the 2013–2014 NYC Health and Nutrition Examination Survey (NYC HANES) data: hypertension, diabetes, smoking, obesity, influenza vaccination, and depression. We classified indicators into having low measurement error or high measurement error on the basis of whether the proportion of misclassification (ie, false-negative or false-positive cases) was greater than 15% in 190 reviewed charts. We compared bias (ie, absolute difference between NYC Macroscope estimates and NYC HANES estimates) before and after calibration. RESULTS: The health indicators with low measurement error had the same bias after calibration as before calibration (diabetes, 2.5 percentage points; smoking, 2.5 percentage points; obesity, 3.5 percentage points; hypertension, 1.1 percentage points). For indicators with high measurement error, bias decreased from 10.8 to 2.5 percentage points for depression, and from 26.7 to 8.4 percentage points for influenza vaccination. CONCLUSION: The calibration model has the potential to reduce bias of prevalence estimates from EHR-based surveillance systems for indicators with high measurement errors. Further research is warranted to assess the utility of the current calibration model for other EHR data and additional indicators.