Abstract
ObjectivesIdentification of diagnostic error is complex and mostly relies on expert ratings, a severely limited procedure. We developed a system that allows to automatically identify diagnostic labelling error from diagnoses coded according to the international classification of diseases (ICD), often available as routine health care data.MethodsThe system developed (index test) was validated against rater based classifications taken from three previous studies of diagnostic labeling error (reference standard). The system compares pairs of diagnoses through calculation of their distance within the ICD taxonomy. Calculation is based on four different algorithms. To assess the concordance between index test and reference standard, we calculated the area under the receiver operating characteristics curve (AUROC) and corresponding confidence intervals. Analysis were conducted overall and separately per algorithm and type of available dataset.ResultsDiagnoses of 1,127 cases were analyzed. Raters previously classified 24.58% of cases as diagnostic labelling errors (ranging from 12.3 to 87.2% in the three datasets). AUROC ranged between 0.821 and 0.837 overall, depending on the algorithm used to calculate the index test (95% CIs ranging from 0.8 to 0.86). Analyzed per type of dataset separately, the highest AUROC was 0.924 (95% CI 0.887–0.962).ConclusionsThe trigger system to automatically identify diagnostic labeling error from routine health care data performs excellent, and is unaffected by the reference standards’ limitations. It is however only applicable to cases with pairs of diagnoses, of which one must be more accurate or otherwise superior than the other, reflecting a prevalent definition of a diagnostic labeling error.
Highlights
Diagnostic error is a frequent health care problem [1,2,3,4] with major medical [4,5,6], legal [7,8,9] and economic consequences [10]
We developed a system that allows to automatically identify diagnostic labelling error from diagnoses coded according to the international classification of diseases (ICD), often available as routine health care data
Singh and colleagues, in a review of cases identified through an automated flagging of electronic health records, found that 43.7% of erroneous cases in primary care involved more than one type of process breakdown [15]
Summary
Diagnostic error is a frequent health care problem [1,2,3,4] with major medical [4,5,6], legal [7,8,9] and economic consequences [10]. Singh and colleagues, in a review of cases identified through an automated flagging of electronic health records (a technique called e-triggers [17]), found that 43.7% of erroneous cases in primary care involved more than one type of process breakdown [15]. Only some of these errors resulted in a wrong diagnostic label, and/or harm from delayed or wrong treatment. In his conceptual model of missed opportunities in diagnosis, Singh accounts for this phenomenon and distinguishes between four types of
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