An Approach to Analyze Diagnosis Errors in Advanced Main Control Room Operations Using the Cause-Based Decision Tree Method

Advancements in the nuclear industry have led to the development of fully digitized main control rooms (MCRs)—often termed advanced MCRs—for newly built nuclear power plants (NPPs). Diagnosis is a major part of the cognitive activity in NPP MCRs. Advanced MCRs are expected to improve the working environment and reduce human error, especially during the diagnosis of unexpected scenarios. However, with the introduction of new types of tasks and errors by digital MCRs, a new method to analyze the diagnosis errors in these new types of MCRs is required. Task analysis for operator diagnosis in an advanced MCR based on emergency operation was performed to determine the error modes. The cause-based decision tree (CBDT) method—originally developed for analog control rooms—was then revised to a modified CBDT (MCBDT) based on the error mode categorizations. This work examines the possible adoption of the MCBDT method for the evaluation of diagnosis errors in advanced MCRs. We have also provided examples of the application of the proposed method to some common human failure events in emergency operations. The results show that with some modifications of the CBDT method, the human reliability in advanced MCRs can be reasonably estimated.

Approach to the Diagnosis of Skin Disease

The diagnosis of skin disease is not something that changes radically year to year. In fact, for hundreds of years physicians have been assessing the skin to diagnose and treat skin diseases and to “view” internal diseases. The latest edition of this review provides several updates that enhance our approach to the diagnosis of skin disease with active links to updated digital references and atlases. These will be valuable to students, residents, and physicians interested in improving their dermatologic diagnostic skills. A new algorithm highlights our suggested approach to cutaneous diagnoses. It is our hope that readers will begin to “think like dermatologists” as they digest the contents of this review. Key words: Macule, papule, vesicle, bulla, plaque, excoriation, scale, ulceration, diagnosis, errors This review contains 13 figures, 5 tables, 17 references, and 7 additional readings.

Identifying error types in visual diagnostic skill assessment

Background:Misinterpretation of medical images is an important source of diagnostic error. Errors can occur in different phases of the diagnostic process. Insight in the error types made by learners is crucial for training and giving effective feedback. Most diagnostic skill tests however penalize diagnostic mistakes without an eye for the diagnostic process and the type of error. A radiology test with stepwise reasoning questions was used to distinguish error types in the visual diagnostic process. We evaluated the additional value of a stepwise question-format, in comparison with only diagnostic questions in radiology tests.Methods:Medical students in a radiology elective (n=109) took a radiology test including 11–13 cases in stepwise question-format: marking an abnormality, describing the abnormality and giving a diagnosis. Errors were coded by two independent researchers as perception, analysis, diagnosis, or undefined. Erroneous cases were further evaluated for the presence of latent errors or partial knowledge. Inter-rater reliabilities and percentages of cases with latent errors and partial knowledge were calculated.Results:The stepwise question-format procedure applied to 1351 cases completed by 109 medical students revealed 828 errors. Mean inter-rater reliability of error type coding was Cohen’s κ=0.79. Six hundred and fifty errors (79%) could be coded as perception, analysis or diagnosis errors. The stepwise question-format revealed latent errors in 9% and partial knowledge in 18% of cases.Conclusions:A stepwise question-format can reliably distinguish error types in the visual diagnostic process, and reveals latent errors and partial knowledge.

Approach to the Diagnosis of Skin Disease

The diagnosis of skin disease is not something that changes radically year to year. In fact, for hundreds of years physicians have been assessing the skin to diagnose and treat skin diseases and to “view” internal diseases. The latest edition of this review provides several updates that enhance our approach to the diagnosis of skin disease with active links to updated digital references and atlases. These will be valuable to students, residents, and physicians interested in improving their dermatologic diagnostic skills. A new algorithm highlights our suggested approach to cutaneous diagnoses. It is our hope that readers will begin to “think like dermatologists” as they digest the contents of this review. Key words: Macule, papule, vesicle, bulla, plaque, excoriation, scale, ulceration, diagnosis, errors This review contains 13 figures, 5 tables, 17 references, and 7 additional readings.

Approach to the Diagnosis of Skin Disease

The diagnosis of skin disease is not something that changes radically year to year. In fact, for hundreds of years physicians have been assessing the skin to diagnose and treat skin diseases and to “view” internal diseases. The latest edition of this review provides several updates that enhance our approach to the diagnosis of skin disease with active links to updated digital references and atlases. These will be valuable to students, residents, and physicians interested in improving their dermatologic diagnostic skills. A new algorithm highlights our suggested approach to cutaneous diagnoses. It is our hope that readers will begin to “think like dermatologists” as they digest the contents of this review. Key words: Macule, papule, vesicle, bulla, plaque, excoriation, scale, ulceration, diagnosis, errors This review contains 13 figures, 5 tables, 17 references, and 7 additional readings.