Electronic health record-based surveillance of diagnostic errors in primary care

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This is a review of Singh et. al's article "Electronic health record-based surveillance of diagnostic errors in primary care."[1]


Diagnostic errors in outpatient primary care settings are understudied, yet there are studies documenting the significance of these errors. These errors can be difficult to detect, especially using existing methods, which include random chart reviews, voluntary reporting, or malpractice claims review. This study proposed to use EHR-based triggers to determine potential diagnostic errors in two different outpatient settings. Triggers are "signals that can alert providers to review the record to determine whether a patient safety even occurred."[1]


Two trigger algorithms were developed:

  • First Trigger Algorithm: Primary care index visit followed by unplanned hospitalization within 14 days 
  • Second Trigger Algorithm: Primary care index visit followed by 1 unscheduled visit(s) within 14 days

These triggers ran on the EHR data for outpatients at two large health systems in Texas.

Following trigger application, charts were reviewed for select triggered and control patient visits. If the chart met initial criteria (as in, potentially had a diagnostic error), it was reviewed in detail by a physician reviewer blinded to the goals of the study. Reviewers were trained in detecting diagnostic errors according to an explicit definition from the literature. Reviewers filled out a standardized data collection form.


674 patient records were identified by Trigger 1, and of those, 141 were found to have previously unrecognized diagnostic errors (Positive Predictive value=20.9%).

Trigger 2 found 669 charts for review, of which there were 36 previously unidentified diagnostic errors (Positive Predictive Value=5.4%). 


Trigger 1 had a high positive predictive value (PPV) in comparison with other known computerized methods of diagnostic error detection. Quality improvement initiatives for primary care could use this methodology to improve error detection.


This methodology is novel and holds a lot of potential for the future of using technology for automated error detection. There are some concerns about subjectivity where these sorts of errors are concerned, but trigger methods should be developed to improve quality of care moving forward.

Health care facilities track patient's visits to ensure proper care was given to patients. It also tracks the reason for the subsequent visit. It is important to identify patients that have to return to a physician or facility for care for an existing concern. It is imperative that primary care practices are held to the same standard as acute and long term care facilities.

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  1. 1.0 1.1 Singh H, Giardina TD, Forjuoh SN, Reis MD, Kosmach S, Khan MM, and Thomas EJ. Electronic health record-based surveillance of diagnostic errors in primary care. BMJ Qual Saf. 2012 Feb;21(2):93-100. doi: 10.1136/bmjqs-2011-000304. Epub 2011 Oct 13 http://www.ncbi.nlm.nih.gov/pubmed/21997348