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.
— DIL
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