Studying Preventable Harm due to Diagnostic Errors or Delays with EHR Data – a Mixture Survival Model Approach

Music City Center 

Studying adverse healthcare events caused by diagnostic errors or delays, which are both common and costly, presents a significant opportunity to reduce preventable harm, and therefore vital for healthcare improvement. Traditional effort like chart reviews are labor-intensive and do not scale well. To enhance diagnostic performance monitoring and identify improvement areas more efficiently, researchers suggest using electronic health records or claim data to analyze the relationship between symptoms and diseases. Specifically, tracking elevated disease risk after a false-negative diagnosis can help signal potential harm. We introduced a mixture regression model along with harm measures and profiling analysis procedures designed to quantify, evaluate, and compare misdiagnosis-related harm across medical institutes with varying patient population compositions.

EHR

diagnostic errors

mixture models