Using Electronic Records to Detect and Learn from Ambulatory Diagnostic Errors

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Project Details -
Completed

The increasing use of electronic health records (EHRs) facilitates creation of health data repositories that contain longitudinal patient information in a far more integrated fashion than was previously available. This project leveraged the EHR infrastructure of two large health care systems to test whether electronic triggers could be used for large-scale measurement and surveillance of diagnostic errors in primary care. The study sought to develop, refine, and test methods to detect diagnostic errors in primary care in several types of practice settings, including internal medicine and family practice in both academic and nonacademic settings. The goal was to describe the prevalence of these errors and the most common clinical conditions associated with them.

The main objectives of the project were to:

  1. Apply and improve computerized triggers based on visit patterns to detect, measure, and learn from diagnostic errors in diverse primary care settings.
  2. Test whether a method of computerized tracking for abnormal test results that are potentially lost to followup can be used as a trigger to identify diagnostic near-misses in primary care.

The findings of this study demonstrated that EHR-based trigger methods can enable more meaningful measurement and surveillance of diagnostic errors in primary care. Most errors involved conditions commonly seen in primary care. Prevalence of diagnostic error was estimated to be 2.8 percent; given the volume of primary care visits in the US, an estimated prevalence rate of about 2.8 percent entails a substantial patient safety risk. Primary care reform initiatives should redesign delivery systems and implement techniques for active error surveillance. Improved measurement and detection methods could provide useful feedback about errors to frontline providers and promote prevention-related learning.