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Branch F, Santana I, Hegdé J. Diagnostics (Basel). 2022;12:105.
Anchoring bias is relying on initial diagnostic impression despite subsequent information to the contrary. In this study, radiologists were asked to read a mammogram and were told a random number which researchers claimed was the probability the mammogram was positive for breast cancer. Radiologists' estimation of breast cancer reflected the random number they were given prior to viewing the image; however, when they were not given a prior estimation, radiologists were highly accurate in diagnosing breast cancer.
Bulliard J‐L, Beau A‐B, Njor S, et al. Int J Cancer. 2021;149:846-853.
Overdiagnosis of breast cancer and the resulting overtreatment can cause physical, emotional, and financial harm to patients. Analysis of observational data and modelling indicates overdiagnosis accounts for less than 10% of invasive breast cancer in patients aged 50-69. Understanding rates of overdiagnosis can assist in ascertaining the net benefit of breast cancer screening.
Chang T-P, Bery AK, Wang Z, et al. Diagnosis (Berl). 2022;9:96-106.
A missed or delayed diagnosis of stroke increases the risk of permanent disability or death. This retrospective study compared rates of misdiagnosed stroke in patients presenting to general care or specialty care who were initially diagnosed with “benign dizziness”. Patients with dizziness who presented to general care were more likely to be misdiagnosed than those presenting to specialty care. Interventions to improve stroke diagnosis in emergency departments may also be successful in general care clinics.
Haimi M, Brammli-Greenberg S, Baron-Epel O, et al. BMC Med Inform Decis Mak. 2020;20.
This retrospective mixed-methods study explored patient safety within a pediatric telemedicine triage service by assessing the appropriateness and reasonableness of the diagnosis reached by the online physician. The researchers analyzed a random sample of telephone consultations and conducted qualitative interviews with physicians to obtain their perspectives about factors impacting their reaching diagnosis and deciding on reasonable and appropriate treatment. Analysis of telephone consultations found high levels of diagnosis appropriateness, decision reasonableness and accuracy. Physician interviews revealed six themes for appropriate diagnosis and decision-making: (1) use of intuition, (2) experience, (3) use of rules of thumb and protocols, (4) making shared decisions with parents, (5) considering non-medical factors, and (6) using additional tools such as video chat or digital photos when necessary.
Kim H-E, Kim HH, Han B-K, et al. The Lancet Digital Health. 2020.
There is increasing interest in the use of artificial intelligence (AI) to improve breast cancer detection. This study developed and validated an AI algorithm using mammography readings from five institutions in South Korea, the United States, and the United Kingdom. The AI algorithm alone showed better diagnostic performance in breast cancer detection compared to radiologists without AI assistance (area under the curve [AUC] of 0.94 vs. 0.81, p<0.0001) or radiologists with AI assistance (0.88; p<0.0001). AI improved performance of radiologists and was better at detecting mass cancers, distortion, asymmetry, or node-negative cancers compared with radiologist alone.
Abe T, Tokuda Y, Shiraishi A, et al. Crit Care. 2019;23:202.
This retrospective study sought to determine whether timely diagnosis of the site of infection affected in-hospital mortality for sepsis. Investigators found that patients whose infection site was misdiagnosed on admission had more than twofold greater odds of dying in the hospital compared to those with the correct infection site diagnosed on admission. These results reinforce the importance of correct and timely diagnosis for sepsis outcomes.
Tschandl P, Codella N, Akay BN, et al. Lancet Oncol. 2019;20:938-947.
Machine learning may have the potential to improve clinical decision-making and diagnosis. In this study, machine-learning algorithms generally performed better than human experts in accurately diagnosing 7 types of pigmented skin lesions and the top 3 algorithms performed better than the 27 physicians.
Woodham LA, Round J, Stenfors T, et al. PLoS One. 2019;14:e0215597.
Researchers assessed the impact of two different virtual patient models containing error-based scenarios on medical students at six different institutions across three countries. They found that the use of branched decision-making logic did not change students' motivation as compared to a linear virtual patient model without such logic.
Chew KS, van Merrienboer JJG, Durning SJ. BMC Med Educ. 2019;19:18.
Metacognition is an approach to enhance diagnostic thinking. This study used focus groups to assess physicians' and medical students' impressions of a metacognitive diagnostic checklist. Participants found the checklist to be applicable and usable, and the authors conclude that it should be tested in a clinical setting.
Liang H, Tsui BY, Ni H, et al. Nat Med. 2019;25:433-438.
Artificial intelligence may have the potential to improve patient safety by enhancing diagnostic capability. In this study, researchers applied machine learning techniques to a large amount of pediatric electronic health record data and found that their model was able to achieve diagnostic accuracy analogous to that of skilled pediatricians.
Shimizu T, Nemoto T, Tokuda Y. Int J Med Inform. 2018;109:1-4.
This retrospective study found that clinicians who had access to a commercial clinical decision support tool made fewer diagnostic errors than clinicians who did not have access to the tool. The authors conclude that online clinical decision support from this platform improved diagnosis.
Liu D, Gan R, Zhang W, et al. J Clin Pathol. 2018;71:67-71.
Autopsies are an underutilized tool for identifying diagnostic errors. Researchers evaluated 117 autopsies for patients in Shanghai whose cause of death was disputed or required third-party investigation. Diagnostic errors that would have altered treatment or survival were found in nearly 61%. This number is higher than estimates from a previous systematic review, likely because all patients in this sample had a disputed cause of death.
Bejnordi BE, Veta M, van Diest PJ, et al. JAMA. 2017;318:2199-2210.
Diagnostic error is a growing area of focus within patient safety. Artificial intelligence has the potential to improve the diagnostic process, both in terms of accuracy and efficiency. In this study, investigators compared the use of automated deep learning algorithms for detecting metastatic disease in stained tissue sections of lymph nodes of women with breast cancer to pathologists' diagnoses. The algorithms were developed by researchers as part of a competition and their performance was assessed on a test set of 129 slides, 49 with metastatic disease and 80 without. A panel of 11 pathologists evaluated the same slides with a 2-hour time limit and one pathologist evaluated the slides without any time constraints. The authors conclude that some of the algorithms demonstrated better diagnostic performance than the pathologists did, but they suggest that further testing in a clinical setting is warranted. An accompanying editorial discusses the potential of artificial intelligence in health care.
Lee J, Park SW, Kim YS, et al. Medicine (Baltimore). 2017;96:e7468.
Failure to detect abnormalities during testing can lead to missed or delayed diagnoses. In this retrospective observational study, investigators found that nearly 20% of colonoscopies that needed to be repeated within 6 months had an undetected abnormal finding—a polyp—that was not initially detected. The authors caution that there is significant risk of missing abnormal findings on colonoscopy.
Abujudeh H, Kaewlai R, Shaqdan K, et al. American Journal of Roentgenology. 2017;208.
This review summarizes key principles of high quality care and how they can be applied to augment radiology practice. Recommended safety improvement strategies included plan-do-study-act cycles, change management, and balanced scorecards.
ALQahtani DA, Rotgans JI, Mamede S, et al. Acad Med. 2016;91:710-716.
Diagnosis is a critical area of patient safety. Prior research demonstrates that physicians perceive time pressure as an impediment to diagnosis, but this has not been objectively documented. This educational simulation study examined the ability of internal medicine residents to correctly diagnose written cases with and without time pressure. Residents under time pressure had reduced diagnostic accuracy, and this decrement was more marked for difficult cases. These results demonstrate the benefit of allowing physicians more time for accurate diagnosis, consistent with recent Institute of Medicine recommendations to examine novel models of care and reimbursement to foster diagnostic safety. A recent PSNet interview discussed diagnostic errors and how to reduce them.
Lin Y-K, Lin C-J, Chan H-M, et al. Injury. 2014;45:83-7.
Full-time trauma surgeons had a lower incidence of diagnostic errors (defined as the incidence of missed injuries in severely injured patients) compared with surgeons who primarily practiced in other specialties, according to this retrospective analysis of patients admitted to a Taiwanese surgical intensive care unit.
Vashitz G, Pliskin JS, Parmet Y, et al. J Gen Intern Med. 2012;27.
This study found some evidence that the recommendations of physicians who are asked to provide second opinions are influenced by the initial clinician's opinion, particularly if the first clinician recommended interventional treatment.