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Area of Science:

  • Health Informatics
  • Clinical Quality Measurement
  • Preventive Services Research

Background:

  • Electronic health records (EHRs) are a rich data source for assessing preventive service utilization.
  • EHR-derived quality measures are susceptible to errors from misclassifying screening versus diagnostic tests.
  • The impact of this misclassification on screening utilization estimates remains underexplored.

Purpose of the Study:

  • To quantify bias in EHR-based screening colonoscopy utilization estimates.
  • To propose and validate simple correction methods for EHR-derived screening utilization data.
  • To obtain adjusted screening colonoscopy utilization estimates using real-world EHR data.

Main Methods:

  • Calculated bias in screening colonoscopy utilization using multiple published EHR algorithms.
  • Developed two novel methods to correct for classification bias.
  • Applied correction methods to EHR data from an integrated healthcare system.

Main Results:

  • Bias in estimates ranged from -3 to +12 percentage points.
  • Correction methods yielded unbiased estimates with minimal loss of precision.
  • An unadjusted estimate was 4 percentage points higher than the adjusted estimate in the study population.

Conclusions:

  • Accurate classification of screening tests in EHR data is crucial for reliable utilization estimates.
  • Accounting for misclassification bias prevents spurious findings in quality assessment.
  • Proposed correction methods enhance the validity of EHR-based quality measures.