Electronic health records can help detect diagnostic errors in primary care
Diagnostic errors in primary care are harmful, but difficult to detect. A new study suggests that using certain types of queries ("triggers") of electronic health records (EHRs) can help identify potential diagnostic errors in primary care settings. Data from outpatient malpractice claims has consistently ranked missed, delayed, and wrong diagnoses as the most common identified errors. However, searching for diagnostic errors by existing methods (such as random chart reviews, voluntary reporting, or claims file reviews) has been found to be inefficient, biased, or unreliable.In this study, the researchers applied queries to 212,615 outpatient visits to identify primary care visits that might contain a diagnostic error. Their main criteria was based on whether a patient had an unplanned hospitalization within 14 days (Trigger 1) or had at least one unscheduled visit within 14 days (Trigger 2) of the original primary care visit. Diagnostic errors were found by physician reviewers of the patient's chart in 141 of 674 Trigger 1 records (positive predictive value or PPV of 20.9 percent) and in 36 of 669 Trigger 2 records (PPV of 5.4 percent). The PPV of 2.1 percent for a random sample of control visits was significantly lower than those for both Trigger 1 and 2.
The researchers suggest that the accuracy of the EHR diagnostic triggers was sufficiently better than existing methodologies that can be used to identify and analyze diagnostic error. The findings were based on EHR data on primary care outpatient visits from a large Veterans Affairs facility and a large private, integrated health care system. This study was funded in part by the Agency for Healthcare Research and Quality (HS17244).
More details are in "Electronic health record-based surveillance of diagnostic errors in primary care," by Hardeep Singh, M.D., Traber Davis Giardina, M.A., M.S.W., Samuel N. Forjuoh, M.B., Ch.B, Dr.P.H., M.P.H., and others in the British Medical Journal of Quality and Safety 21(2), pp. 93-100, 2012.
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