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Benbenek, Mary Mescher. "Diagnostic Error: An Overview." AACN Advanced Critical Care 36, no. 2 (2025): 123–30. https://doi.org/10.4037/aacnacc2025978.
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Diagnostic error is increasingly identified as a concern in health care. The purposes of this article are to provide an understanding of diagnostic error and its contributing factors and to briefly review strategies to reduce errors. A literature review provided a definition of diagnostic error, a synopsis of diagnostic error prevalence and settings, systemic and individual factors contributing to diagnostic error, and cognitive biases and errors in diagnostic reasoning. Strategies to address diagnostic error are discussed. Diagnostic errors are prevalent across clinical settings, may result in harm, and are preventable. Enhancing the education of health care professionals related to diagnostic reasoning and metacognition, using clinical decision-making tools, and advocating for strong communication practices may reduce diagnostic errors in practice settings.
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Berenson, Robert, and Hardeep Singh. "Payment Innovations To Improve Diagnostic Accuracy And Reduce Diagnostic Error." Health Affairs 37, no. 11 (2018): 1828–35. http://dx.doi.org/10.1377/hlthaff.2018.0714.
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Inayat, Grewal1* Prateek Madaan2 Kriti Soni3. "Curriculum for Radiology Residents to Reduce Errors in Diagnosis." International Clinical and Medical Case Reports Journal, no. 8 (August 29, 2024): 1–4. https://doi.org/10.5281/zenodo.13382081.
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Radiological errors occur with an estimated frequency of 3-5% in daily practice, resulting in severe consequences such as missed or delayed diagnoses.<sup>[1]</sup> This rate translates to approximately 40 million diagnostic errors involving imaging annually worldwide, highlighting the critical need for effective error reduction strategies.<sup>[2]</sup> The majority of these errors stem from human factors, predominantly perceptual errors (failure to see an abnormality) and cognitive errors (misinterpretation of findings).<sup>[3]</sup> Despite advances in technology and education, diagnostic errors remain persistently high, indicating a pressing need to revise the current curriculum to focus more on error reduction and quality improvement.<sup>[4]</sup> Research suggests that enhanced training in diagnostic processes, bias awareness, and feedback mechanisms can significantly improve radiologist performance and patient outcomes.<sup>[2,5]</sup> Therefore, addressing these educational gaps is essential for improving the accuracy and reliability of radiological diagnoses, ultimately leading to better patient care.
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Graber, Mark L., Stephanie Kissam, Velma L. Payne, et al. "Cognitive interventions to reduce diagnostic error: a narrative review." BMJ Quality & Safety 21, no. 7 (2012): 535–57. http://dx.doi.org/10.1136/bmjqs-2011-000149.
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Al-Khafaji, Jawad, Ryan F. Townsend, Whitney Townsend, Vineet Chopra, and Ashwin Gupta. "Checklists to reduce diagnostic error: a systematic review of the literature using a human factors framework." BMJ Open 12, no. 4 (2022): e058219. http://dx.doi.org/10.1136/bmjopen-2021-058219.
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ObjectivesTo apply a human factors framework to understand whether checklists reduce clinical diagnostic error have (1) gaps in composition; and (2) components that may be more likely to reduce errors.DesignSystematic review.Data sourcesPubMed, EMBASE, Scopus and Web of Science were searched through 15 February 2022.Eligibility criteriaAny article that included a clinical checklist aimed at improving the diagnostic process. Checklists were defined as any structured guide intended to elicit additional thinking regarding diagnosis.Data extraction and synthesisTwo authors independently reviewed and selected articles based on eligibility criteria. Each extracted unique checklist was independently characterised according to the well-established human factors framework: Systems Engineering Initiative for Patient Safety 2.0 (SEIPS 2.0). If reported, checklist efficacy in reducing diagnostic error (eg, diagnostic accuracy, number of errors or any patient-related outcomes) was outlined. Risk of study bias was independently evaluated using standardised quality assessment tools in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses.ResultsA total of 30 articles containing 25 unique checklists were included. Checklists were characterised within the SEIPS 2.0 framework as follows: Work Systems subcomponents of Tasks (n=13), Persons (n=2) and Internal Environment (n=3); Processes subcomponents of Cognitive (n=20) and Social and Behavioural (n=2); and Outcomes subcomponents of Professional (n=2). Other subcomponents, such as External Environment or Patient outcomes, were not addressed. Fourteen checklists examined effect on diagnostic outcomes: seven demonstrated improvement, six were without improvement and one demonstrated mixed results. Importantly, Tasks-oriented studies more often demonstrated error reduction (n=5/7) than those addressing the Cognitive process (n=4/10).ConclusionsMost diagnostic checklists incorporated few human factors components. Checklists addressing the SEIPS 2.0 Tasks subcomponent were more often associated with a reduction in diagnostic errors. Studies examining less explored subcomponents and emphasis on Tasks, rather than the Cognitive subcomponents, may be warranted to prevent diagnostic errors.
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Silverston, Paul. "SAFER PRACTICES in the COVID-19 pandemic." Practice Nursing 31, no. 5 (2020): 194–98. http://dx.doi.org/10.12968/pnur.2020.31.5.194.
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COVID-19 has created a wave of uncertainty for nurses and healthcare practitioners, with new information on the virus being released constantly. Paul Silverston discusses the assessment of patients with symptoms of COVID-19 and how to reduce the risk of misdiagnosis Errors in diagnosis are relatively common in primary care which often result in serious harm to patients. The majority of these errors are preventable. This article describes a diagnostic error checklist, SAFER PRACTICES, which can be used to help clinicians prepare themselves for consulting in patients with suspected or confirmed COVID-19 disease so that they come to the consultation with the correct medical knowledge, clinical assessment plan and diagnostic reasoning required to reduce the risk of diagnostic error. It can also be used during the consultation to deliver a systematic approach to the prevention and detection of diagnostic errors.
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Laposata, Michael. "The Definition and Scope of Diagnostic Error in the US and How Diagnostic Error is Enabled." Journal of Applied Laboratory Medicine 3, no. 1 (2018): 128–34. http://dx.doi.org/10.1373/jalm.2017.025882.
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Abstract Background The quality of healthcare in the US has been progressively addressed by 3 reports from the National Academy of Medicine, the latest of which, entitled “Improving Diagnosis in Health Care,” was issued in 2015 from a 21-member panel (the author of this report was a member). The report is a review of the longstanding problem of diagnostic error. The infrastructure of healthcare delivery in the US has inadvertently made diagnostic error a major contributor to the high cost of care and preventable poor patient outcomes. Content This review describes the failures in US healthcare delivery that have led to the overwhelming number of deaths attributable to diagnostic error. Each failure is associated with recommendations to eliminate it. The review begins with a description of the scope of the diagnostic error problem and then discusses each of the issues that need to be addressed to reduce the number of misdiagnoses. Summary The problem of diagnostic error in the US is a large one. Some the contributing factors to this large problem can be resolved at a small expense and with modest change; others require a major overhaul of aspects of medical practice. For the first time, Americans have a “to-do list” to reduce our diagnostic error problem and be on par with other developed countries that are recognized as providing less costly care with better patient outcomes.
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Silverston, Paul. "Right diagnosis, right treatment: SAFER PRACTICES." Journal of Prescribing Practice 1, no. 7 (2019): 356–60. http://dx.doi.org/10.12968/jprp.2019.1.7.356.
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Choosing the right treatment for the patient requires that the right diagnosis is made first. In primary and ambulatory care, however, diagnostic errors are both common and commonly preventable. The World Health Organization has recommended that all health professionals should receive formal training in the principles of diagnostic reasoning and the causes of diagnostic error, and that strategies and interventions to reduce the risk of diagnostic error should be used in clinical practice. This article describes a mnemonic checklist, SAFER PRACTICES, which can be used in an integrated approach to the prevention and detection of diagnostic errors that starts in the classroom and continues through to the consulting room.
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Zarbo, Richard J., Frederick A. Meier, and Stephen S. Raab. "Error Detection in Anatomic Pathology." Archives of Pathology & Laboratory Medicine 129, no. 10 (2005): 1237–45. http://dx.doi.org/10.5858/2005-129-1237-ediap.
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AbstractObjectives.—To define the magnitude of error occurring in anatomic pathology, to propose a scheme to classify such errors so their influence on clinical outcomes can be evaluated, and to identify quality assurance procedures able to reduce the frequency of errors.Design.—(a) Peer-reviewed literature search via PubMed for studies from single institutions and multi-institutional College of American Pathologists Q-Probes studies of anatomic pathology error detection and prevention practices; (b) structured evaluation of defects in surgical pathology reports uncovered in the Department of Pathology and Laboratory Medicine of the Henry Ford Health System in 2001–2003, using a newly validated error taxonomy scheme; and (c) comparative review of anatomic pathology quality assurance procedures proposed to reduce error.Results.—Marked differences in both definitions of error and pathology practice make comparison of error detection and prevention procedures among publications from individual institutions impossible. Q-Probes studies further suggest that observer redundancy reduces diagnostic variation and interpretive error, which ranges from 1.2 to 50 errors per 1000 cases; however, it is unclear which forms of such redundancy are the most efficient in uncovering diagnostic error. The proposed error taxonomy tested has shown a very good interobserver agreement of 91.4% (κ = 0.8780; 95% confidence limit, 0.8416–0.9144), when applied to amended reports, and suggests a distribution of errors among identification, specimen, interpretation, and reporting variables.Conclusions.—Presently, there are no standardized tools for defining error in anatomic pathology, so it cannot be reliably measured nor can its clinical impact be assessed. The authors propose a standardized error classification that would permit measurement of error frequencies, clinical impact of errors, and the effect of error reduction and prevention efforts. In particular, the value of double-reading, case conferences, and consultations (the traditional triad of error control in anatomic pathology) awaits objective assessment.
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Besa, Chola, G. Chongo, and N. Cooper. "Cognitive Autopsy of a Fatal Diagnostic Error." Medical Journal of Zambia 46, no. 4 (2019): 357–61. http://dx.doi.org/10.55320/mjz.46.4.609.
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Background: Diagnostic error is a significant cause of preventable harm worldwide and diagnostic errors have been identified as a high priority patient safety problem by the World Health Organization. Research shows thatdiagnostic error occurs mainly due to system failures and 'cognitive errors' – that is, failure to synthesise all the available information. There is a worldwide consensus that medical schools and postgraduate training programmes rarely teachthe diagnostic process and related decision making (clinical reasoning) in a way that is explicit, systematic and consistent with what is known from research.
 Materials and methods: This paper presents a short case report and analyses it from a clinical reasoning perspective – performing a 'cognitive autopsy' of a fatal diagnostic error.
 Results: Clinicians make cognitive shortcuts through pattern recognition and this is highly accurate most of the time. However, shortcuts sometimes go wrong and these are termed 'cognitive biases'. Cognitive biases are subconscious errors of judgement or perception and common examples include 'anchoring', 'the framing effect', 'search satisficing 'and' confirmation biases. These errors are more likely when clinicians are fatigued or cognitively overloaded, and when systems are not designed to mitigate human errors.
 Conclusions: There is a vast literature on clinical reasoning, 'human factors', and reflection during decision making that show us how we can reduce diagnostic error in our everyday practice. This paper attempts to highlight some of the key findings in the literature that will hopefully encourage readers to explore the patient safety and clinical reasoning literature for themselves and work together to improve outcomes for patients.
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Besa, Chola, G. Chongo, and N. Cooper. "Cognitive Autopsy of a Fatal Diagnostic Error." Medical Journal of Zambia 46, no. 4 (2020): 357–61. http://dx.doi.org/10.55320/mjz.46.4.248.
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Background: Diagnostic error is a significant cause of preventable harm worldwide and diagnostic errors have been identified as a high priority patient safety problem by the World Health Organization. Research shows thatdiagnostic error occurs mainly due to system failures and 'cognitive errors' – that is, failure to synthesise all the available information. There is a worldwide consensus that medical schools and postgraduate training programmes rarely teachthe diagnostic process and related decision making (clinical reasoning) in a way that is explicit, systematic and consistent with what is known from research.
 Materials and methods: This paper presents a short case report and analyses it from a clinical reasoning perspective – performing a 'cognitive autopsy' of a fatal diagnostic error.
 Results: Clinicians make cognitive shortcuts through pattern recognition and this is highly accurate most of the time. However, shortcuts sometimes go wrong and these are termed 'cognitive biases'. Cognitive biases are subconscious errors of judgement or perception and common examples include 'anchoring', 'the framing effect', 'search satisficing 'and' confirmation biases. These errors are more likely when clinicians are fatigued or cognitively overloaded, and when systems are not designed to mitigate human errors.
 Conclusions: There is a vast literature on clinical reasoning, 'human factors', and reflection during decision making that show us how we can reduce diagnostic error in our everyday practice. This paper attempts to highlight some of the key findings in the literature that will hopefully encourage readers to explore the patient safety and clinical reasoning literature for themselves and work together to improve outcomes for patients.
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Zwaan, Laura. "The critical step to reduce diagnostic errors in medicine: addressing the limitations of human information processing." Diagnosis 1, no. 1 (2014): 139–41. http://dx.doi.org/10.1515/dx-2013-0018.
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AbstractOver the last 50 years diagnostic testing has improved dramatically and we are now able to diagnose patients faster and more precisely than ever before. However, the incidence of diagnostic errors, particularly of common diseases, has remained relatively stable over time. In this paper, I argue that the intrinsic limitations of human information processing are crucial. The way people process information has not changed over the years and is the main cause of diagnostic error. To take a decisive step forward and substantially reduce the number of diagnostic errors in medicine, we need to create an environment which takes the intrinsic limitations of in human information processing into account.
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Liebovitz, David. "Next steps for electronic health records to improve the diagnostic process." Diagnosis 2, no. 2 (2015): 111–16. http://dx.doi.org/10.1515/dx-2014-0070.
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AbstractElectronic health record (EHR) usage is accelerating while preventable diagnostic error persists. EHRs may even contribute to diagnostic error through several pathways including poor usability and an over reliance on electronic chart based communication. The changing context of healthcare delivery offers potential financial incentives for organizations to leverage EHRs to reduce diagnostic error. The lack of standard quality metrics for reporting rates of diagnostic error, a lack of diagnostic feedback systems for physicians and organizations, and a lack of compelling evidence for specific interventions underscore the need for further research in preventing diagnostic error. Many potential strategies exist for EHRs to reduce the likelihood of diagnostic error. Practical next steps for leveraging EHR systems to assist in the diagnostic process are suggested. These include patient engagement strategies, closed loop result tracking, targeted next step reminder systems, and expansion of a list of actionable patient states based on diagnosis triggers.
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Bundy, David G., Hardeep Singh, Ruth EK Stein, et al. "The design and conduct of Project RedDE: A cluster-randomized trial to reduce diagnostic errors in pediatric primary care." Clinical Trials 16, no. 2 (2019): 154–64. http://dx.doi.org/10.1177/1740774518820522.
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Background: Diagnostic errors contribute to the large burden of healthcare-associated harm experienced by children. Primary care settings involve high diagnostic uncertainty and limited time and information, creating ideal conditions for diagnostic errors. We report on the design and conduct of Project RedDE, a stepped-wedge, cluster-randomized controlled trial of a virtual quality improvement collaborative aimed at reducing diagnostic errors in pediatric primary care. Methods: Project RedDE cluster-randomized pediatric primary care practices into one of three groups. Each group participated in a quality improvement collaborative targeting the same three diagnostic errors (missed diagnoses of elevated blood pressure and adolescent depression and delayed diagnoses of abnormal laboratory studies), but in a different sequence. During the quality improvement collaborative, practices worked both independently and collaboratively, leveraging general quality improvement strategies (e.g. process mapping) in addition to error-specific content (e.g. pocket guides for blood pressure norms) delivered during the intervention phase for each error. The quality improvement collaborative intervention included interactive learning sessions and webinars, quality improvement coaching at the team level, and repeated evaluation of failures via root cause analyses. Pragmatic data were collected monthly, submitted to a centralized data aggregator, and returned to the practices in the form of run charts comparing each practice’s progress over time to that of the group. The primary analysis used patients as the unit of analysis and compared diagnostic error proportions between the intervention and baseline periods, while secondary analyses evaluated the sustainability of observed reductions in diagnostic errors after the intervention period ended. Results: A total of 43 practices were recruited and randomized into Project RedDE. Eleven practices withdrew before submitting any data, and one practice merged with another participating practice, leaving 31 practices that began work on Project RedDE. All but one of the diverse, national pediatric primary care practices that participated ultimately submitted complete data. Quality improvement collaborative participation was robust, with an average of 63% of practices present on quality improvement collaborative webinars and 85% of practices present for quality improvement collaborative learning sessions. Complete data included 30 months of outcome data for the first diagnostic error worked on, 24 months of outcome data for the second, and 16 months of data for the third. Lessons learned and limitations: Contamination across study groups was a recurring concern; concerted efforts were made to mitigate this risk. Electronic health records played a large role in teams’ success. Conclusion: Project RedDE, a virtual quality improvement collaborative aimed at reducing diagnostic errors in pediatric primary care, successfully recruited and retained a diverse, national group of pediatric primary care practices. The stepped-wedge, cluster-randomized controlled trial design allowed for enhanced scientific efficiency.
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Raffel, Katie E., Molly A. Kantor, Peter Barish, et al. "Prevalence and characterisation of diagnostic error among 7-day all-cause hospital medicine readmissions: a retrospective cohort study." BMJ Quality & Safety 29, no. 12 (2020): 971–79. http://dx.doi.org/10.1136/bmjqs-2020-010896.
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BackgroundThe prevalence and aetiology of diagnostic error among hospitalised adults is unknown, though likely contributes to patient morbidity and mortality. We aim to identify and characterise the prevalence and types of diagnostic error among patients readmitted within 7 days of hospital discharge.MethodsRetrospective cohort study at a single urban academic hospital examining adult patients discharged from the medical service and readmitted to the same hospital within 7 days between January and December 2018. The primary outcome was diagnostic error presence, identified through two-physician adjudication using validated tools. Secondary outcomes included severity of error impact and characterisation of diagnostic process failures contributing to error.ResultsThere were 391 cases of unplanned 7-day readmission (5.2% of 7507 discharges), of which 376 (96.2%) were reviewed. Twenty-one (5.6%) admissions were found to contain at least one diagnostic error during the index admission. The most common problem areas in the diagnostic process included failure to order needed test(s) (n=11, 52.4%), erroneous clinician interpretation of test(s) (n=10, 47.6%) and failure to consider the correct diagnosis (n=8, 38.1%). Nineteen (90.5%) of the diagnostic errors resulted in moderate clinical impact, primarily due to short-term morbidity or contribution to the readmission.ConclusionThe prevalence of diagnostic error among 7-day medical readmissions was 5.6%. The most common drivers of diagnostic error were related to clinician diagnostic reasoning. Efforts to reduce diagnostic error should include strategies to augment diagnostic reasoning and improve clinician decision-making around diagnostic studies.
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Graber, Mark L. "Educational strategies to reduce diagnostic error: can you teach this stuff?" Advances in Health Sciences Education 14, S1 (2009): 63–69. http://dx.doi.org/10.1007/s10459-009-9178-y.
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Egri, Csilla, Kathryn E. Darras, Elena P. Scali, and Alison C. Harris. "Classification of Error in Abdominal Imaging: Pearls and Pitfalls for Radiologists." Canadian Association of Radiologists Journal 69, no. 4 (2018): 409–16. http://dx.doi.org/10.1016/j.carj.2018.06.006.
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Peer review for radiologists plays an important role in identifying contributing factors that can lead to diagnostic errors and patient harm. It is essential that all radiologists be aware of the multifactorial causes of diagnostic error in radiology and the methods available to reduce it. This pictorial review provides readers with an overview of common errors that occur in abdominal radiology and strategies to reduce them. This review aims to make readers more aware of pitfalls in abdominal imaging so that these errors can be avoided in the future. This essay also provides a systematic approach to classifying abdominal imaging errors that will be of value to all radiologists participating in peer review.
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Bruce, Beau B., Robert El-Kareh, John W. Ely, et al. "Methodologies for evaluating strategies to reduce diagnostic error: report from the research summit at the 7th International Diagnostic Error in Medicine Conference." Diagnosis 3, no. 1 (2016): 1–7. http://dx.doi.org/10.1515/dx-2016-0002.
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AbstractIn this article we review current evidence on strategies to evaluate diagnostic error solutions, discuss the methodological challenges that exist in investigating the value of these strategies in patient care, and provide recommendations for methods that can be applied in investigating potential solutions to diagnostic errors. These recommendations were developed iteratively by the authors based upon initial discussions held during the Research Summit of the 7th Annual Diagnostic Error in Medicine Conference in September 2014. The recommendations include the following elements for designing studies of diagnostic research solutions: (1) Select direct and indirect outcomes measures of importance to patients, while also practical for the particular solution; (2) Develop a clearly-stated logic model for the solution to be tested; (3) Use rapid, iterative prototyping in the early phases of solution testing; (4) Use cluster-randomized clinical trials where feasible; (5) Avoid simple pre-post designs, in favor of stepped wedge and interrupted time series; (6) Leverage best practices for patient safety research and engage experts from relevant domains; and (7) Consider sources of bias and design studies and their analyses to minimize selection and information bias and control for confounding. Areas of diagnostic error mitigation research identified for further attention include: role of competing diagnoses, understanding the impacts of organizational culture, timing of diagnosis, and sequencing of research studies. Future research will likely require novel clinical, health services, and qualitative research methods to address the age-old problem of arriving at an accurate diagnosis.
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Graber, Mark L., Asta V. Sorensen, Jon Biswas, et al. "Developing checklists to prevent diagnostic error in Emergency Room settings." Diagnosis 1, no. 3 (2014): 223–31. http://dx.doi.org/10.1515/dx-2014-0019.
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AbstractChecklists have been shown to improve performance of complex, error-prone processes. To develop a checklist with potential to reduce the likelihood of diagnostic error for patients presenting to the Emergency Room (ER) with undiagnosed conditions.Participants included 15 staff ER physicians working in two large academic centers. A rapid cycle design and evaluation process was used to develop a general checklist for high-risk situations vulnerable to diagnostic error. Physicians used the general checklists and a set of symptom-specific checklists for a period of 2 months. We conducted a mixed methods evaluation that included interviews regarding user perceptions and quantitative assessment of resource utilization before and after checklist use.A general checklist was developed iteratively by obtaining feedback from users and subject matter experts, and was trialed along with a set of specific checklists in the ER. Both the general and the symptom-specific checklists were judged to be helpful, with a slight preference for using symptom-specific lists. Checklist use commonly prompted consideration of additional diagnostic possibilities, changed the working diagnosis in approximately 10% of cases, and anecdotally was thought to be helpful in avoiding diagnostic errors. Checklist use was prompted by a variety of different factors, not just diagnostic uncertainty. None of the physicians used the checklists in collaboration with the patient, despite being encouraged to do so. Checklist use did not prompt large changes in test ordering or consultation.In the ER setting, checklists for diagnosis are helpful in considering additional diagnostic possibilities, thus having potential to prevent diagnostic errors. Inconsistent usage and using the checklists privately, instead of with the patient, are factors that may detract from obtaining maximum benefit. Further research is needed to optimize checklists for use in the ER, determine how to increase usage, to evaluate the impact of checklist utilization on error rates and patient outcomes, to determine how checklist usage affects test ordering and consultation, and to compare checklists generally with other approaches to reduce diagnostic error.
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Franco, Joel, Alhasan N. Elghouche, Michael S. Harris, and Mimi S. Kokoska. "Diagnostic Delays and Errors in Head and Neck Cancer Patients: Opportunities for Improvement." American Journal of Medical Quality 32, no. 3 (2016): 330–35. http://dx.doi.org/10.1177/1062860616638413.
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A retrospective review of 100 sequential patients (2009-2012) with head and neck cancer was performed to determine the frequency of 5 types of diagnostic delays and errors outlined by the Institute of Medicine. There were a total of 105 diagnostic delays/errors. The most common was delay in being seen in the otolaryngology clinic after referral placement (28.6%), followed by diagnostic error by the referring physician (22%), delay in referral of a symptomatic patient to the otolaryngology clinic (16.2%), delay in employing an appropriate diagnostic test or procedure (15.2%), delay in action following reporting of pathology or imaging results for an incidental lesion (11.4%), diagnostic error by the otolaryngology clinic (2.8%), delay in action following reporting of pathology or imaging results for the symptomatic lesion (2.8%), and use of outmoded tests or therapy (1%). Increased awareness of these types of delays/errors will direct actions and processes to reduce or eliminate them.
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Silverston, Paul. "SAFER PRACTICES: reducing the risk of diagnostic errors." Practice Nursing 31, no. 2 (2020): 80–86. http://dx.doi.org/10.12968/pnur.2020.31.2.80.
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Diagnostic errors are relatively common in general practice. Paul Silverston describes a mnemonic-based system to prevent and detect these errors Diagnostic errors in primary care are relatively common and they have the potential to cause serious harm to patients. Up to 80% of these errors are believed to be preventable. This article describes a mnemonic-based system that practice nurses can use to prevent diagnostic errors from arising, as well as to detect these errors when they occur. The mnemonic is designed to be used pre-consultation to reduce the risk of errors arising through better preparation; during the consultation, as a diagnostic error checklist; and after the consultation to encourage reflective practice and critical thinking.
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Lukyanenko, N. Ya, Ya N. Shoikhet, A. F. Lazarev, V. A. Lubennikov, and I. V. Vikhlyanov. "Diagnostic errors in patients with diseases of the chest cavity and ways to reduce them." Russian Journal of Oncology 24, no. 3-6 (2020): 96–101. http://dx.doi.org/10.18821/1028-9984-2019-24-3-6-96-101.
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This paper presents an algorithm for reducing the risk of errors in the diagnosis of diseases of the chest cavity within 14 days after treatment of patients. The developed algorithm, based on multivariate analysis of the integrated assessment of clinical and radiological descriptors (signs) of diseases, determination of the probability coefficient of errors, software for comparing individual integral data with established typical characteristics for differentiable pathological processes, improved diagnostics, aimed the doctor at an adequate examination, and reduced the risk of error by 20.1%.
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Thomas, Dana B., and David E. Newman-Toker. "Diagnosis is a team sport – partnering with allied health professionals to reduce diagnostic errors." Diagnosis 3, no. 2 (2016): 49–59. http://dx.doi.org/10.1515/dx-2016-0009.
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Abstract: Diagnostic errors are the most common, most costly, and most catastrophic of medical errors. Interdisciplinary teamwork has been shown to reduce harm from therapeutic errors, but sociocultural barriers may impact the engagement of allied health professionals (AHPs) in the diagnostic process.: A qualitative case study of the experience at a single institution around involvement of an AHP in the diagnostic process for acute dizziness and vertigo. We detail five diagnostic error cases in which the input of a physical therapist was central to correct diagnosis. We further describe evolution of the sociocultural milieu at the institution as relates to AHP engagement in diagnosis.: Five patients with acute vestibular symptoms were initially misdiagnosed by physicians and then correctly diagnosed based on input from a vestibular physical therapist. These included missed labyrinthine concussion and post-traumatic benign paroxysmal positional vertigo (BPPV); BPPV called gastroenteritis; BPPV called stroke; stroke called BPPV; and multiple sclerosis called BPPV. As a consequence of surfacing these diagnostic errors, initial resistance to physical therapy input to aid medical diagnosis has gradually declined, creating a more collaborative environment for ‘team diagnosis’ of patients with dizziness and vertigo at the institution.: Barriers to AHP engagement in ‘team diagnosis’ include sociocultural norms that establish medical diagnosis as something reserved only for physicians. Drawing attention to the valuable diagnostic contributions of AHPs may help facilitate cultural change. Future studies should seek to measure diagnostic safety culture and then implement proven strategies to breakdown sociocultural barriers that inhibit effective teamwork and transdisciplinary diagnosis.
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Scott, Ian A. "Using information technology to reduce diagnostic error: still a bridge too far?" Internal Medicine Journal 52, no. 6 (2022): 908–11. http://dx.doi.org/10.1111/imj.15804.
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Sibbald, Matt, Jonathan Sherbino, Jonathan S. Ilgen, et al. "Debiasing versus knowledge retrieval checklists to reduce diagnostic error in ECG interpretation." Advances in Health Sciences Education 24, no. 3 (2019): 427–40. http://dx.doi.org/10.1007/s10459-019-09875-8.
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Safer Dakhilallah Saad Almalki. "Automation In Laboratories: How It’s Changing Diagnostic Techniques." Power System Technology 48, no. 4 (2024): 3102–20. https://doi.org/10.52783/pst.1182.
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The rapid advancement of automation in laboratories is revolutionizing diagnostic techniques across various healthcare and research fields. Automated systems in laboratories, ranging from sample handling to data analysis, are enhancing the efficiency, accuracy, and reliability of diagnostic processes. These systems help reduce human error, increase throughput, and enable real-time monitoring, making them essential in modern diagnostic labs. This paper explores the integration of automation in laboratory diagnostics, focusing on its impact on test accuracy, turnaround time, cost-effectiveness, and patient outcomes. The evolution of laboratory automation technologies, challenges faced, and future directions for automation in diagnostics are also discussed.
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Maude, Jason. "Differential diagnosis: the key to reducing diagnosis error, measuring diagnosis and a mechanism to reduce healthcare costs." Diagnosis 1, no. 1 (2014): 107–9. http://dx.doi.org/10.1515/dx-2013-0009.
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AbstractDifferential diagnosis has been taught in medical schools for over 100 years and yet it is not routinely carried out in practice; nor is it required to be documented within medical notes. I strongly believe that the routine use of a differential diagnosis would not only substantially reduce the level of diagnostic error but would also greatly reduce the cost of healthcare. This solution to the seemingly intractable problems of diagnostic error and rising healthcare costs is simple and has been with us for 100 years!
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Kataria, Vipin, Nitin Kumar, and Parth Patel. "Improving Malaria detection using enhanced-efficientnet deep neural network approach." International Journal of Innovative Research and Scientific Studies 8, no. 4 (2025): 1456–73. https://doi.org/10.53894/ijirss.v8i4.8098.
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Malaria detection traditionally relies on microscopic examination of blood smears, a process that is labor-intensive and prone to human error. This study aims to introduce a robust automated detection method using deep learning, designed to enhance diagnostic accuracy and reduce human effort. The research presents an innovative Enhanced-EfficientNet (EEN) deep neural network approach comprising three distinct phases: image preprocessing, feature extraction using the Enhanced-EfficientNet model, and classification using a Deep Neural Network (DNN). The proposed methodology was validated using a dataset containing 27,558 labeled blood cell images equally divided between "infected" and "uninfected" samples. The proposed EEN approach achieved superior diagnostic performance, with a maximum classification accuracy of 97.71%, precision of 97.71%, recall of 97.72%, and an F1 score of 97.71% on the test dataset. Comparative evaluation with established models, including VGG16, Xception, ResNet152, EfficientNetB3, and InceptionV3, confirmed significant performance improvements offered by the proposed method. The Enhanced-EfficientNet model effectively addresses the accuracy and reliability challenges associated with traditional malaria diagnostics, presenting a robust deep learning alternative with improved diagnostic outcomes. The study underscores deep learning's practical value as a supportive diagnostic tool, facilitating quicker, more reliable detection of malaria infections. Clinicians can leverage this technology to enhance patient care, significantly reduce diagnostic errors, and improve survival outcomes for patients.
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Pimonov, Ihor, Andriy Yefimenkо, Denis Zhuk, and Volodymyr Prykhodko. "Determination of diagnostic errors in the system of diagnostic parameters of hydraulic drives of construction and road machinery." Bulletin of Kharkov National Automobile and Highway University, no. 99 (December 29, 2022): 62. http://dx.doi.org/10.30977/bul.2219-5548.2022.99.0.62.
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Problem. The article considers the issue of increasing the efficiency of operation of construction machines by improving systems of measures that provide an effective system for diagnosing hydraulic drive elements. The considered existing methods prove that a more effective determination of indicators of diagnostic parameters in a system with the error of the diagnostic devices themselves for diagnosing hydraulic drives of construction and road machines will be the equipment that gives the necessary result with lower overall costs for diagnostics with minimal costs, including the cost of diagnostic equipment, its operation and development, as well as the cost of preparing the hydraulic drive for diagnosis. Goal. The goal is determination of diagnostic errors in the system of diagnostic parameters of hydraulic drives of construction and road machinery. Methodology In technical diagnostics, test and functional diagnostics are used. During functional diagnosis, actions are used that are set by the working processes of the hydraulic drive. During test diagnostics, special test actions are applied to hydraulic units. The technical condition of hydraulic units is evaluated based on the reaction to these actions. Functional and test actions, between which there are no clear boundaries, are carried out only within the technical characteristics of the hydraulic unit. Results. A more effective determination of the indicators of diagnostic parameters in a system with the error of the diagnostic devices themselves for diagnosing hydraulic drives of construction and road machines will be the equipment that gives the required result with lower overall costs for diagnostics. These costs include the cost of diagnostic equipment, its operation and development, as well as the cost of preparing the hydraulic drive for diagnosis. Originality. Accuracy in this case is determined by the analytical dependence that connects the diagnostic parameters and other methods depending on the type of this dependence. Thus, it was established that the use of a lower rotation frequency of the pump compared to its passport data increases the share of leaks in the total flow of the working fluid and allows to reduce the diagnostic error by 40...60% without additional costs for the purchase and operation of measuring devices. Changing the parameters is possible within the technical characteristics of the hydraulic unit. Methods of ensuring accuracy are currently insufficiently used to improve diagnostics, which is an unused reserve for increasing its efficiency. Practical value. Efficiency of operation of construction machines is increased by improving the systems of measures that provide an effective system for diagnosing hydraulic drive elements.
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Cheng, Ze, Yong Xu, and Meng Nan Dong. "The Error Analysis of a New PV Fault Diagnosis System." Applied Mechanics and Materials 325-326 (June 2013): 725–29. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.725.
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The paper analyses the error factors of every part of the new pv fault diagnosis system which may affect the final diagnostic result and the fault positioning result. According to the different characteristics of each factor, we present the effective methods from the aspect of hardware or software to eliminate or reduce the errors, so the precision of the whole system can be improved.
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Sibbald, Matt, Jonathan Sherbino, Jonathan S. Ilgen, et al. "Correction to: Debiasing versus knowledge retrieval checklists to reduce diagnostic error in ECG interpretation." Advances in Health Sciences Education 24, no. 3 (2019): 441–42. http://dx.doi.org/10.1007/s10459-019-09884-7.
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Sangeeta, Scotton, Liczkowski Anthony, Mollan Susan P, and Sinclair Alexandra J. "WED 094 Diagnostic error rates in diagnosing idiopathic intracranial hypertension." Journal of Neurology, Neurosurgery & Psychiatry 89, no. 10 (2018): A10.1—A10. http://dx.doi.org/10.1136/jnnp-2018-abn.36.
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ObjectiveTo quantify the rate of diagnostic error amongst patients with IIH. Additionally to identify factors contributing to diagnostic error.MethodsSequential patients referred with a diagnosis of IIH to the Birmingham tertiary neuro-ophthalmology IIH clinic were prospectively included (October 2013- February 2017) A diagnostic error taxonomy tool was applied to cases referred as ‘definite’ or ‘possible’ IIH. Discrepancy between referred and final diagnosis were recorded. Results212 patients were referred, (96.2% female), 138/212 (65%) with definite IIH and 74/212 (35%) with possible IIH. Of those diagnosed with definite IIH 25% were not IIH and out of those diagnosed with possible IIH 57% were not IIH. Reasons for diagnostic error included incorrectly identifying papilloedema where in fact pseudopapilloedema existed and diagnosing IIH following an isolated lumbar puncture (LP) pressure >25 cmCSF (but in the absence of other diagnostic criteria for IIH). Misdiagnosis lead to 43% receiving unnecessary acetazolamide (or other diuretics) and 14% having multiple LPs.ConclusionsWe noted a high diagnostic error rate amongst IIH patients referred to a tertiary centre for ongoing management. Where there is doubt about the presence of true papilloedema early specialist review may reduce unnecessary treatment and LP’s.
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Rahman, Tahmina, Debashish Saha, and Rezina Ahmed. "Improving Diagnostic Safety by minimizing errors in Preanalytical Phase of Laboratory Testing Process." Pulse 16, no. 2 (2025): 19–26. https://doi.org/10.3329/pulse.v16i2.81675.
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The role of laboratory medicine is indispensable for the healthcare industry. Laboratory Services is a rapidly expanding field which contributes significantly 60–70% of clinical decisions regarding hospitalization, discharge, and medications of patients. Total laboratory testing is a cyclical process which is divided into three phases: preanalytical, analytical and postanalytical phase. The pre-analytical phase is a complex process and performed outside the laboratory. Available evidence demonstrates that the most common errors occur in the pre-analytical phase (46–68.2% of total errors). User’s feedback was collected proactively to find the gaps in the exiting process. Retrospective data was collected from February to July 2023, and it was evident that average sample rejection rate was significantly higher 2.56%, wrong investigation order error was 1.65% and sample transportation time was prolonged (average 160 minutes for inpatient and 79 min for outpatient department) in Evercare hospital Dhaka. Therefore, Laboratory stakeholders and senior leaders of Evercare hospital Dhaka decided to start hospital wide quality improvement project. The objective of this study is to identify the gaps in the preanalytical phase of diagnostic services. It was aimed to minimize the errors by developing and implementing effective quality initiatives and was targeted to decrease sample rejection rate below 2%, reduce wrong investigation input rate below 1% and to maintain IPD sample transportation TAT within 120 minutes and OPD sample transportation TAT within 60 minutes. After implementing multiple effective quality initiatives EHD successfully achieved the target and reduced sample rejection rate 1.5%, reduced investigation order error rate 0.8% and maintained sample transportation TAT for IPD 119.9 minutes and for OPD 59.2 minutes. Pulse Volume 16, Issue 2 2024, P: 19-26
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Chu, David, Jane Xiao, Payal Shah, and Brett Todd. "How common are cognitive errors in cases presented at emergency medicine resident morbidity and mortality conferences?" Diagnosis 5, no. 3 (2018): 143–50. http://dx.doi.org/10.1515/dx-2017-0046.
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AbstractBackgroundCognitive errors are a major contributor to medical error. Traditionally, medical errors at teaching hospitals are analyzed in morbidity and mortality (M&M) conferences. We aimed to describe the frequency of cognitive errors in relation to the occurrence of diagnostic and other error types, in cases presented at an emergency medicine (EM) resident M&M conference.MethodsWe conducted a retrospective study of all cases presented at a suburban US EM residency monthly M&M conference from September 2011 to August 2016. Each case was reviewed using the electronic medical record (EMR) and notes from the M&M case by two EM physicians. Each case was categorized by type of primary medical error that occurred as described by Okafor et al. When a diagnostic error occurred, the case was reviewed for contributing cognitive and non-cognitive factors. Finally, when a cognitive error occurred, the case was classified into faulty knowledge, faulty data gathering or faulty synthesis, as described by Graber et al. Disagreements in error type were mediated by a third EM physician.ResultsA total of 87 M&M cases were reviewed; the two reviewers agreed on 73 cases, and 14 cases required mediation by a third reviewer. Forty-eight cases involved diagnostic errors, 47 of which were cognitive errors. Of these 47 cases, 38 involved faulty synthesis, 22 involved faulty data gathering and only 11 involved faulty knowledge. Twenty cases contained more than one type of cognitive error. Twenty-nine cases involved both a resident and an attending physician, while 17 cases involved only an attending physician. Twenty-one percent of the resident cases involved all three cognitive errors, while none of the attending cases involved all three. Forty-one percent of the resident cases and only 6% of the attending cases involved faulty knowledge. One hundred percent of the resident cases and 94% of the attending cases involved faulty synthesis.ConclusionsOur review of 87 EM M&M cases revealed that cognitive errors are commonly involved in cases presented, and that these errors are less likely due to deficient knowledge and more likely due to faulty synthesis. M&M conferences may therefore provide an excellent forum to discuss cognitive errors and how to reduce their occurrence.
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Gleason, Kelly T., Patricia M. Davidson, Elizabeth K. Tanner, et al. "Defining the critical role of nurses in diagnostic error prevention: a conceptual framework and a call to action." Diagnosis 4, no. 4 (2017): 201–10. http://dx.doi.org/10.1515/dx-2017-0015.
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AbstractNurses have always been involved in the diagnostic process, but there remains a pervasive view across physicians, nurses, and allied health professionals that medical diagnosis is solely a physician responsibility. There is an urgent need to adjust this view and for nurses to take part in leading efforts addressing diagnostic errors. The purpose of this article is to define a framework for nursing engagement in the diagnostic process that can serve as a catalyst for nurses to engage in eliminating preventable harms from diagnostic error. We offer a conceptual model to formalize and expand nurses’ engagement in the diagnostic process through education, maximize effectiveness of interprofessional teamwork and communication through culture change, and leverage the nursing mission to empower patients to become active members of the diagnostic team. We describe the primary barriers, including culture, education, operations, and regulations, to nurses participating as full, equal members of the diagnostic team, and illustrate our approach to addressing these barriers. Nurses already play a major role in diagnosis and increasingly take ownership of this role, removing barriers will strengthen nurses’ ability to be equal, integral diagnostic team members. This model should serve as a foundation for increasing the role of the nurse in the diagnostic process, and calling nurses to take action in leading efforts to reduce diagnostic error.
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Nosova, YA V., O. H. Avrunin, N. O. Shushlyapyna, Ibrahim Yunuss Abdelkhamid, and Alofy Bender Aly Salekh. "Diagnostic significance of methods for determining nasal breathing disorders." Optoelectronic Information-Power Technologies 41, no. 1 (2021): 47–58. http://dx.doi.org/10.31649/1681-7893-2021-41-1-47-58.
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In the diagnosis of nasal breathing disorders, the main instrumental diagnostic methods are optical endoscopy of the nose, X-ray computed spiral (or cone-beam) tomography of the nose and paranasal sinuses, as well as rhinomanometry. The statistics included 286 patients with nasal breathing disorders and a control group of 60 people. Patients were divided into two groups - with nasal breathing disorders of different nature and conditional norm (control group). The probability of error in detecting nasal breathing disorders is 0.27 (normalized Euclidean distance 1.82). Taking into account the addition of computed tomography data to the discrimination model, the diagnostic error decreases to 0.11 at a distance of 3.19. When rhinomanometry data are added to the model, the total normalized Euclidean distance increases to 3.96, and the probability of making a diagnostic decision, respectively, decreases to 0.05. Thus, rhinomanometric data make it possible to supplement the results of functional tests with information about changes in the architectonics of the nasal cavity by assessing the effect of anatomical structures on nasal aerodynamics and further reduce the likelihood of errors in diagnostic decisions when detecting disturbances in nasal breathing.
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Sherbino, Jonathan, Kulamakan Kulasegaram, Elizabeth Howey, and Geoffrey Norman. "Ineffectiveness of cognitive forcing strategies to reduce biases in diagnostic reasoning: a controlled trial." CJEM 16, no. 01 (2014): 34–40. http://dx.doi.org/10.2310/8000.2013.130860.
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ABSTRACT Objectives: Cognitive forcing strategies (CFS)may reduce error arising from cognitive biases. This is the first experimental test to determine the effect of CFS training in medical students. Methods: Students were allocated to CFS training or control during a 4-week emergency medicine rotation (n = 191). At the end of the rotation examination, students were tested using computer-based cases. Application of CFS could enable reduction of diagnostic error, as evidenced by identifying multiple correct diagnoses for the two cases prone to search satisficing bias (SSB) and uncommon diagnoses for the two cases prone to availability bias (AB). Two “false positive” cases were included to test for possible “oversearching.” Results: There were 145 students in the intervention and 46 in the control group. For the SSB cases, 52% of students with CFS training and 48% in the control group initiated a search for the second diagnosis (χ2 = 0.13, df = 1, p = 0.91). More than half (54%) correctly identified the second diagnosis in the CFS group, and 48% identified it in the control group. The difference was not significant (χ2 = 2.25, df = 1, p = 0.13). For the second diagnosis in the false positive cases, 64% of the CFS group and 77% of the control group incorrectly identified it. There were no significant differences between groups (χ2 = 2.38, df = 1, p = 0.12). In the AB cases, only 45% in each group identified the uncommon correct diagnosis (χ2 = 0.001, df = 1, p = 0.98). Conclusions: The educational interventions suggested by experts in clinical reasoning and employed in our study to teach CFS failed to show any reduction in diagnostic error by novices.
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Kirilochev, O. K. "Causes, frequency and avoidance of diagnostic errors in newborns and children of the first year of life." Rossiyskiy Vestnik Perinatologii i Pediatrii (Russian Bulletin of Perinatology and Pediatrics) 65, no. 3 (2020): 53–60. http://dx.doi.org/10.21508/1027-4065-2020-65-3-53-60.
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The article presents research methods to detect the frequency of diagnostic errors.Objective: to compare clinical and pathological diagnoses in order to determine the frequency, causes and ways of avoiding diagnostic errors in children with infectious pathology specific to the perinatal period. The authors studied 234 death cases in the intensive care unit for newborns in 2006–2018, and they found diagnostic errors in 18,3% of cases. 53,4% of the diagnostic errors were associated with unrecognized infectious diseases specific to the perinatal period. The authors found that the correct intravital diagnosis was impossible for objective reasons in 65% of cases. Those objective reasons were mainly caused by diagnostic difficulties due to the lack of characteristic clinical data or the atypical course. Almost in every third patient the diagnostic errors were caused by subjective reasons and were associated with the diagnosis of congenital cytomegalovirus infection and neonatal sepsis. The subjective errors were often caused by the so-called doctor’s bona fide delusion due to a lack of knowledge, skills, experience. As the judgment error was the most common reason for misdiagnosis we need to improve the clinicians’ cognitive condition. Based on the audit results, the authors proposed the additional diagnostic approaches for certain diseases. One way to reduce errors is to improve educational initiatives for doctors.
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Ruedinger, Emily, Maren Olson, Justin Yee, Emily Borman-Shoap, and Andrew P. J. Olson. "Education for the Next Frontier in Patient Safety: A Longitudinal Resident Curriculum on Diagnostic Error." American Journal of Medical Quality 32, no. 6 (2016): 625–31. http://dx.doi.org/10.1177/1062860616681626.
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Diagnostic error is a common, serious problem that has received increased attention recently for its impact on both patients and providers. Presently, most graduate medical education programs do not formally address this topic. The authors developed and evaluated a longitudinal, multimodule resident curriculum about diagnostic error and medical decision making. Key components of the curriculum include demystifying the medical decision-making process, building skills in critical thinking, and providing strategies for diagnostic error mitigation. Special attention was paid to avoiding the second victim effect and to fostering a culture that supports constructive, productive feedback when an error does occur. The curriculum was rated by residents as helpful (96%), and residents were more likely to be aware of strategies to reduce cognitive error (27% pre vs 75% post, P < .0001) following its implementation. This article describes the development, implementation, and effectiveness of this curriculum and explores generalizability of the curriculum to other programs.
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Berlin, Leonard. "Radiologic errors, past, present and future." Diagnosis 1, no. 1 (2014): 79–84. http://dx.doi.org/10.1515/dx-2013-0012.
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AbstractDuring the 10-year period beginning in 1949 with publication of five articles in two radiology journals and UKs The Lancet, a California radiologist named L.H. Garland almost single-handedly shocked the entire medical and especially the radiologic community. He focused their attention on the fact now known and accepted by all, but at that time not previously recognized and acknowledged only with great reluctance, that a substantial degree of observer error was prevalent in radiologic interpretation. In the more than half-century that followed, Garland’s pioneering work has been affirmed and reaffirmed by numerous researchers. Retrospective studies disclosed then and still disclose today that diagnostic errors in radiologic interpretations of plain radiographic (as well as CT, MR, ultrasound, and radionuclide) images hover in the 30% range, not too dissimilar to the error rates in clinical medicine. Seventy percent of these errors are perceptual in nature, i.e., the radiologist does not “see” the abnormality on the imaging exam, perhaps due to poor conspicuity, satisfaction of search, or simply the “inexplicable psycho-visual phenomena of human perception.” The remainder are cognitive errors: the radiologist sees an abnormality but fails to render a correct diagnoses by attaching the wrong significance to what is seen, perhaps due to inadequate knowledge, or an alliterative or judgmental error. Computer-assisted detection (CAD), a technology that for the past two decades has been utilized primarily in mammographic interpretation, increases sensitivity but at the same time decreases specificity; whether it reduces errors is debatable. Efforts to reduce diagnostic radiological errors continue, but the degree to which they will be successful remains to be determined.
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Wright, Breanna, Nicholas Faulkner, Peter Bragge, and Mark Graber. "What interventions could reduce diagnostic error in emergency departments? A review of evidence, practice and consumer perspectives." Diagnosis 6, no. 4 (2019): 325–34. http://dx.doi.org/10.1515/dx-2018-0104.
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Abstract The purpose of this article is to synthesise review evidence, practice and patient perspectives on interventions to reduce diagnostic error in emergency departments (EDs). A rapid review methodology identified nine systematic reviews for inclusion. Six practice interviews were conducted to identify local contextual insights and implementation considerations. Finally, patient perspectives were explored through a citizen panel with 11 participants. The rapid review found evidence for the following interventions: second opinion, decision aids, guided reflection and education. Practitioners suggested three of the four interventions from the academic review: second opinion, decision aids and education. Practitioners suggested four additional interventions: improving teamwork, engaging patients, learning from mistakes and scheduled test follow-up. Patients most favoured interventions that improved communication through education and patient engagement, while also suggesting that implementation of state-wide standards to reduce variability in care and sufficient staffing are important to address diagnostic errors. Triangulating these three perspectives on the evidence allows for the intersections to be highlighted and demonstrates the usefulness of incorporating practitioner reflections and patient values in developing potential interventions.
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VEERAVARAPRASAD, PINDI. "AI-DRIVEN DIAGNOSTIC TOOLS: REVOLUTIONIZING EARLY DETECTION OF DISEASES IN HEALTHCARE." International Journal of Innovative Research and Creative Technology 1, no. 1 (2015): 1–8. https://doi.org/10.5281/zenodo.12805329.
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The advent of AI-driven diagnostic tools has significantly transformed the landscape of early disease detection in healthcare. These innovations leverage advanced algorithms and data analytics to enhance diagnostic accuracy, reduce human error, and streamline patient care. This paper delves into the various AI-driven diagnostic technologies currently employed in healthcare, their impact on early disease detection, and prospects. Key findings highlight the profound impact of AI tools in identifying diseases at earlier stages, leading to a significant improvement in patient outcomes and a potential reduction in healthcare costs. This study provides a comprehensive overview of the current state of AI in diagnostics and its revolutionary potential in the healthcare sector, instilling a sense of hope and optimism in the audience.
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Winkel, David J., Philipp Brantner, Jonas Lutz, Safak Korkut, Sebastian Linxen, and Tobias J. Heye. "Gamification of Electronic Learning in Radiology Education to Improve Diagnostic Confidence and Reduce Error Rates." American Journal of Roentgenology 214, no. 3 (2020): 618–23. http://dx.doi.org/10.2214/ajr.19.22087.
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Trotter, Martin J., and Andrea K. Bruecks. "Interpretation of Skin Biopsies by General Pathologists: Diagnostic Discrepancy Rate Measured by Blinded Review." Archives of Pathology & Laboratory Medicine 127, no. 11 (2003): 1489–92. http://dx.doi.org/10.5858/2003-127-1489-iosbbg.
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Abstract Context.—Slide review has been advocated as a means to reduce diagnostic error in surgical pathology and is considered an important component of a total quality assurance program. Blinded review is an unbiased method of error detection, and this approach may be used to determine the diagnostic discrepancy rates in surgical pathology. Objective.—To determine the diagnostic discrepancy rate for skin biopsies reported by general pathologists. Design.—Five hundred eighty-nine biopsies from 500 consecutive cases submitted by primary care physicians and reported by general pathologists were examined by rapid-screen, blinded review by 2 dermatopathologists, and the original diagnosis was compared with the review interpretation. Results.—Agreement was observed in 551 (93.5%) of 589 biopsies. Blinded review of these skin biopsies by experienced dermatopathologists had a sensitivity of 100% (all lesions originally reported were detected during review). False-negative errors were the most common discrepancy, but false positives, threshold discrepancies, and differences in type or grade were also observed. Only 1.4% of biopsies had discrepancies that were of potential clinical importance. Conclusions.—Blinded review demonstrates that general pathologists reporting skin biopsies submitted by primary care physicians have a low diagnostic error rate. The method detects both false-negative and false-positive cases and identifies problematic areas that may be targeted in continuing education activities. Blinded review is a useful component of a dermatopathology quality improvement program.
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Morant, Steven V., Frank H. Dodd, and Roger P. Natzke. "Consequences of diagnostic errors in mastitis therapy trials." Journal of Dairy Research 55, no. 3 (1988): 315–29. http://dx.doi.org/10.1017/s0022029900028570.
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SummaryThe effect of errors that occur in the diagnosis of intramammary infectious mastitis on the precision of experiments measuring the efficacy of mastitis therapy has been investigated. Diagnostic errors within the range found by experienced workers can create large biases in the apparent cure rate of therapy particularly at cure rates of less than 0·5. Using confirmed methods of diagnosis rather than single samples and reducing the probabilities of false positive and false negative diagnoses to 0·01 and 0·05 respectively, the biases in the apparent cure rates are reduced to acceptable levels. A method is given for calculating the rates of occurrence of false positive and false negative diagnoses from the results of trials using confirmed diagnoses. These errors cannot be calculated from therapy trial data when diagnosis is based on single milk samples.Because the bias in the measurements of the cure rate is greatest at the lowest levels of elimination, estimates of spontaneous recovery in untreated quarters have the greatest error. For this reason experiments incorporating an untreated control group of infected quarters usually reduce the precision of the therapy trials. An experiment in which the efficacy of a test product is measured relative to a reference product has advantages. It minimizes the difficulties arising from scale of measurement, diagnostic errors and herd differences in response rate, and makes possible comparisons between trials. Further investigations are required on the importance of spontaneous recovery, particularly for studies of Escherichia coli therapy and dry period therapy. The results of this investigation have relevance to all types of mastitis investigation that measure the change in mastitis status of udder quarters, i.e. new infection rates.
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Trevatt, Alexander EJ, Oliver J. Smith, Jacqueline Needleman, and Ashis Banerjee. "An analysis of the most common types of hand injury mistakes and their cost in the acute setting." Medico-Legal Journal 84, no. 4 (2016): 206–11. http://dx.doi.org/10.1177/0025817216664663.
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This study aimed to explore the most common hand injury errors occurring in Emergency Departments in England. A Freedom of Information request was made to the NHS Litigation Authority for claims data related to hand injuries in English Emergency Departments from 2004 to 2014. All successful hand injury claims against an individual DGH ED were also analysed. Two hundred and eighteen successful claims were made, costing a total of £6,273,688.22. Diagnosis error was the most common successful claim (97). Four successful claims were brought against the Emergency Department. Causes of error included the use of inappropriate views and failure to correlate imaging with clinical findings. Hand injury diagnostic error has been the most common cause of successful litigious claims against Emergency Departments over the past 10 years. This paper demonstrates that fracture recognition and clinical diagnosis of hand injuries are key areas to target to reduce error rates.
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Petersen, Lauren A., Stephanie Delkoski, and Sarah McCarthy. "Diagnostic Reasoning for APRN Learners: Overview of Teaching Strategies." AACN Advanced Critical Care 36, no. 2 (2025): 131–42. https://doi.org/10.4037/aacnacc2025341.
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Diagnostic error is a critical issue in health care. To reduce diagnostic error and enhance practice safety of new graduates, advanced practice registered nurse (APRN) learners need intentional preparation in diagnostic reasoning. It is imperative that APRN programs integrate diagnostic reasoning into all program curricula. This article provides an overview of teaching strategies aimed at promoting skill development in diagnostic reasoning, specifically related to knowledge development, differential diagnosis, and reflective practices. The article reviews foundational information related to dual-process thinking and teaching strategies for APRN primary and acute care curricula. Knowledge development is supported by illness scripting and problem representation activities. Skills in differential diagnosis and diagnosis prioritization are supported by the use of grids and lists. Cognitive debiasing and reflective practice are supported through self-explanation and structured reflection. Implementation of tailored teaching strategies can effectively prepare learners for clinical practice as diagnosticians.
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Lockhart, Joseph J., and Saty Satya-Murti. "Blinding or information control in diagnosis: could it reduce errors in clinical decision-making?" Diagnosis 5, no. 4 (2018): 179–89. http://dx.doi.org/10.1515/dx-2018-0030.
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Abstract Background Clinical medicine has long recognized the potential for cognitive bias in the development of new treatments, and in response developed a tradition of blinding both clinicians and patients to address this specific concern. Although cognitive biases have been shown to exist which impact the accuracy of clinical diagnosis, blinding the diagnostician to potentially misleading information has received little attention as a possible solution. Recently, within the forensic sciences, the control of contextual information (i.e. information apart from the objective test results) has been studied as a technique to reduce errors. We consider the applicability of this technique to clinical medicine. Content This article briefly describes the empirical research examining cognitive biases arising from context which impact clinical diagnosis. We then review the recent awakening of forensic sciences to the serious effects of misleading information. Comparing the approaches, we discuss whether blinding to contextual information might (and in what circumstances) reduce clinical errors. Summary and outlook Substantial research indicates contextual information plays a significant role in diagnostic error and conclusions across several medical specialties. The forensic sciences may provide a useful model for the control of potentially misleading information in diagnosis. A conceptual analog of the forensic blinding process (the “agnostic” first reading) may be applicable to diagnostic investigations such as imaging, microscopic tissue examinations and waveform recognition. An “agnostic” approach, where the first reading occurs with minimal clinical referral information, but is followed by incorporation of the clinical history and reinterpretation, has the potential to reduce errors.
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Volkov, Volodymyr, Volodymyr Kuzhel, Tetiana Volkova, Ganna Pliekhova, and Vyacheslav Narizhny. "Vehicle diagnostic technology." Journal of Mechanical Engineering and Transport 14, no. 2 (2022): 10–17. http://dx.doi.org/10.31649/2413-4503-2021-14-2-10-17.
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In the article, using the example of a mechatronic control system for the engine and transmission of vehicles (automobiles), the features of the technology of their diagnosis are shown. In an electronic transmission control system, the object of regulation is mainly an automatic transmission. Also, the laws of control (programs) of gear shifting in an automatic transmission ensure the optimal transfer of engine energy to the wheels of the vehicle (TC), taking into account the required traction and speed properties and fuel economy. At the same time, the programs for achieving optimal traction-speed properties and minimum fuel consumption differ from each other, since the simultaneous achievement of these goals is not always possible. Therefore, depending on the driving conditions and the desire of the driver, using a special switch, you can select the "economy" program to reduce fuel consumption, the "power" program - to improve traction and speed properties, or the "manual" program to switch gears by the driver. In turn, self-diagnostic capabilities include: system identification and electronic control units (ECU) (ECU); recognition, storage and reading of information about static and single malfunctions; reading current real data, including environmental conditions and specifications; modeling of system functions; programming of system parameters. The individual programs for the test block are stored in the plug-in modules, while the correction and data transfer in the system is carried out via the data interface. Note also that the diagnostic process begins with the initialization of the systems - their detection in the electrical equipment of the vehicle. Upon successful initialization, it is possible to: read the error memory; erase the error memory; view the data of the next detected system or exit to the main menu; change the readings of the selected category; correct the current time; correct the current date and perform a number of additional functions.
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Gureev, Ivan I. "Instrumental and Methodological Support for the Diagnostics of Nutritional Requirements of Plants." Engineering Technologies and Systems 32, no. 4 (2022): 504–19. http://dx.doi.org/10.15507/2658-4123.032.202204.504-519.
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Introduction. Mineral fertilizers essential for intensive crop production technologies are an expensive and environmentally unsafe resource polluting the soil and agricultural products when applied in excess. The purpose of the research is instrumental and methodological support for modern functional diagnostics of nutritional requirements of plants, which is aimed at activating the photosynthesis process. Materials and Methods. It is proposed, for identifying nutritional requirements of plants to replace numerous intermediate plastic test tubes with a mixture of permanent components (sodium chloride, chloroplast suspension and Tillmans’ paint) for the diagnostic solution variants by a separate elastic light-protective container. A homogeneous mixture in a separate container eliminates the error in the concentration of solution components, which accompanies the repeated formation of mixtures in intermediate test tubes. This made it possible to reduce a number of repeated operations of filling intermediate test tubes with pipette dispensers for each tested mixture of elements. The studies were carried out in 2021–2022 using mechanical pipette dispensers Lenpipet Thermo Fisher Scientific (Finland) – 10 ml, Lenpipet Color – 100 μl and Lenpipet Color – 200 μl. Their error was determined on a VK-600 electronic balance. Results. The use of innovation increased the reliability of diagnostic data due to a 8.6% average reduction of error in the concentration of components in the mixture solution. In addition, the time spent on performing diagnostics decreased by 1.7 times that, under the conditions of a limited lifetime of chloroplasts, had a favorable effect on obtaining reliable data. Discussion and Conclusion. Reliable diagnostic data on nutritional requirements of plants will save fertilizer resources and improve the quality of agricultural production free from excessive nutrients.