The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations

By Hardeep Singh, Ashley N. D. Meyer, Eric J. Thomas. 2014

595 citations recorded by the source corpus.

Background The frequency of outpatient diagnostic errors is challenging to determine due to varying error definitions and the need to review data across multiple providers and care settings over time. We estimated the frequency of diagnostic errors in the US adult population by synthesising data from three previous studies of clinic-based populations that used conceptually similar definitions of diagnostic error. Methods Data sources included two previous studies that used electronic triggers, or algorithms, to detect unusual patterns of return visits after an initial primary care visit or lack of follow-up of abnormal clinical findings related to colorectal cancer, both suggestive of diagnostic errors. A third study examined consecutive cases of lung cancer. In all three studies, diagnostic errors were confirmed through chart review and defined as missed opportunities to make a timely or correct diagnosis based on available evidence. We extrapolated the frequency of diagnostic error obtained from our studies to the US adult population, using the primary care study to estimate rates of diagnostic error for acute conditions (and exacerbations of existing conditions) and the two cancer studies to conservatively estimate rates of missed diagnosis of colorectal and lung cancer (as proxies for other serious chronic conditions). Results Combining estimates from the three studies yielded a rate of outpatient diagnostic errors of 5.08%, or approximately 12 million US adults every year. Based upon previous work, we estimate that about half of these errors could potentially be harmful. Conclusions Our population-based estimate suggests that diagnostic errors affect at least 1 in 20 US adults. This foundational evidence should encourage policymakers, healthcare organisations and researchers to start measuring and reducing diagnostic errors.

Subfields: Family Practice, General Health Professions, Pharmacy

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Citations in the corpus

• The Application of Medical Artificial Intelligence Technology in Rural Areas of Developing Countries (2018, background): “About half of these errors could potentially be harmful.(18) AI technology has been utilized to improve the quality of medical diagnosis, especially in radiology, due to the large volumes of medical image data.”
• Early recognition of multiple sclerosis using natural language processing of the electronic health record (2017, background): “In the outpatient setting, the overall missed diagnosis rate has been estimated to be 5% [5, 6] and has been reported to be as high as 20% for various cancers [7] and chronic kidney disease [8].”
• An overview of clinical decision support systems: benefits, risks, and strategies for success (2020, background): “52 We are now seeing diagnostic systems being developed with non-knowledge-based techniques like machine learning, which may pave the way for more accurate diagnosis.”