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Electronic health record reviews to measure diagnostic uncertainty in primary care.

Bhise V, Rajan SS, Sittig DF, Vaghani V, Morgan RO, Khanna A, Singh H. Electronic health record reviews to measure diagnostic uncertainty in primary care. Journal of evaluation in clinical practice. 2018 Jun 1; 24(3):545-551.

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RATIONALE, AIMS AND OBJECTIVES: Diagnostic uncertainty is common in primary care. Because it is challenging to measure, there is inadequate scientific understanding of diagnostic decision-making during uncertainty. Our objective was to understand how diagnostic uncertainty was documented in the electronic health record (EHR) and explore a strategy to retrospectively identify it using clinician documentation. METHODS: We reviewed the literature to identify documentation language that could identify both direct expression and indirect inference of diagnostic uncertainty and designed an instrument to facilitate record review. Direct expression included clinician's use of question marks, differential diagnoses, symptoms as diagnosis, or vocabulary such as "probably, maybe, likely, unclear or unknown," while describing the diagnosis. Indirect inference included absence of documented diagnosis at the end of the visit, ordering of multiple consultations or diagnostic tests to resolve diagnostic uncertainty, and use of suspended judgement, test of treatment, and risk-averse disposition. Two physician-reviewers independently reviewed notes on a sample of outpatient visits to identify diagnostic uncertainty at the end of the visit. Documented Ninth Revision of the International Classification of Diseases (ICD-9) diagnosis codes and note quality were assessed. RESULTS: Of 389 patient records reviewed, 218 had evidence of diagnostic activity and were included. In 156 visits (71.6%), reviewers identified clinicians who experienced diagnostic uncertainty with moderate inter-reviewer agreement (81.7%; Cohen's kappa: 0.609). Most cases (125, 80.1%) showed evidence of both direct expression and indirect inference. Uncertainty was directly expressed in 139 (89.1%) cases, most commonly by using symptoms as diagnosis (98, 62.8%), and inferred in 144 (92.3%). In more than 1/3 of visits (58, 37.2%), diagnostic uncertainty was recorded inappropriately using ICD-9 codes. CONCLUSIONS: While current diagnosis coding mechanisms (ICD-9 and ICD-10) are unable to capture uncertainty, our study finds that review of EHR documentation can help identify diagnostic uncertainty with moderate reliability. Better measurement and understanding of diagnostic uncertainty could help inform strategies to improve the safety and efficiency of diagnosis.

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