Cognitive and visual diagnostic errors in dermatology: part 1

A Delphi-Style Evaluation of a Skin Assessment Simulation Using Clinical Vignettes and a Breast Health Training Tool

INTRODUCTION

Breast and skin assessment are both required clinical skills across health professional training programs. The breast is an ideal "canvas" for the development of a simulation exercise focused on the identification and diagnosis of skin conditions that could present anywhere on the body alongside unique conditions specific to the breast and nipple-areolar complex. A skin assessment simulation on a breast model has not been described.

METHODS

Investigators developed 20 clinical vignettes for breast and skin conditions (ie, features) depicted on a set of 10 single silicone breast models in 4 skin tones. A modified Delphi approach was used to evaluate the appropriateness of the clinical vignettes and realism of features. A convenience sample of 136 licensed physicians was recruited online. In round 1, participants rated the realism of features and suggested descriptors for each feature. In round 2, participants rated their agreement with descriptors from round 1, selected a diagnosis based on clinical vignettes, and assessed the utility of the Breast Health Training Tool for health professionals.

RESULTS

In round 1, participants (n = 38) agreed (5.1/6) that the features were realistic. In round 2, participants (n = 24) agreed (4.6/6) with the descriptors. Most (>50%) participants agreed on a diagnosis for each feature. Participants (n = 27) agreed (5.3/6) that this tool is useful for health professional education.

CONCLUSIONS

This is the first evaluation of a simulation tool for teaching breast examination and identification of skin conditions on the nipple-areolar complex and breast tissue in multiple skin tones, simultaneously filling the gap in women's breast, lactation, and skin health education.

Hiding in Plain Sight: A Retrospective Review of Unrecognized Tumors During Dermatologic Surgery

Background: Mohs micrographic surgery requires focused attention that may lead to tunnel vision bias, contributing to not recognizing skin cancer at nearby sites.

Objective: It is to determine if a subsequently diagnosed skin cancer was visible at the time of Mohs surgery.

Methods: A retrospective chart review was performed at a single academic center from 2008 to 2020. Patients who underwent at least two distinct MMS procedures, separated in time to capture subsequent tumors, were included.

Results: Four hundred and four individual patients were identified with at least two distinct Mohs procedures, which generated 1,110 Mohs sequences. Fifty-one (4.6%) clinically apparent tumors went unrecognized and 127 (11.4%) tumors were identified and biopsied during the visit. High-risk tumor histology was identified in 10 (20%) unrecognized tumors and 31 (24%) recognized tumors (p-value 0.491).

Conclusion: Our study suggests that Mohs surgeons may be overlooking adjacent skin cancers when focusing only on the tumor being surgically treated. Tunnel vision bias may account for part of this phenomenon.

The Importance of Incorporating Human Factors in the Design and Implementation of Artificial Intelligence for Skin Cancer Diagnosis in the Real World

Artificial intelligence (AI) algorithms have been shown to diagnose skin lesions with impressive accuracy in experimental settings. The majority of the literature to date has compared AI and dermatologists as opponents in skin cancer diagnosis. However, in the real-world clinical setting, the clinician will work in collaboration with AI. Existing evidence regarding the integration of such AI diagnostic tools into clinical practice is limited. Human factors, such as cognitive style, personality, experience, preferences, and attitudes may influence clinicians' use of AI. In this review, we consider these human factors and the potential cognitive errors, biases, and unintended consequences that could arise when using an AI skin cancer diagnostic tool in the real world. Integrating this knowledge in the design and implementation of AI technology will assist in ensuring that the end product can be used effectively. Dermatologist leadership in the development of these tools will further improve their clinical relevance and safety.

Effectiveness of blended learning versus lectures alone on ECG analysis and interpretation by medical students

BACKGROUND

Most medical students lack confidence and are unable to accurately interpret ECGs. Thus, better methods of ECG instruction are being sought. Current literature indicates that the use of e-learning for ECG analysis and interpretation skills (ECG competence) is not superior to lecture-based teaching. We aimed to assess whether blended learning (lectures supplemented with the use of a web application) resulted in better acquisition and retention of ECG competence in medical students, compared to conventional teaching (lectures alone).

METHODS

Two cohorts of fourth-year medical students were studied prospectively. The conventional teaching cohort (n = 67) attended 4 hours of interactive lectures, covering the basic principles of Electrocardiography, waveform abnormalities and arrhythmias. In addition to attending the same lectures, the blended learning cohort (n = 64) used a web application that facilitated deliberate practice of systematic ECG analysis and interpretation, with immediate feedback. All participants completed three tests: pre-intervention (assessing baseline ECG competence at start of clinical clerkship), immediate post-intervention (assessing acquisition of ECG competence at end of six-week clinical clerkship) and delayed post-intervention (assessing retention of ECG competence 6 months after clinical clerkship, without any further ECG training). Diagnostic accuracy and uncertainty were assessed in each test.

RESULTS

The pre-intervention test scores were similar for blended learning and conventional teaching cohorts (mean 31.02 ± 13.19% versus 31.23 ± 11.52% respectively, p = 0.917). While all students demonstrated meaningful improvement in ECG competence after teaching, blended learning was associated with significantly better scores, compared to conventional teaching, in immediate (75.27 ± 16.22% vs 50.27 ± 17.10%, p <  0.001; Cohen's d = 1.58), and delayed post-intervention tests (57.70 ± 18.54% vs 37.63 ± 16.35%, p <  0.001; Cohen's d = 1.25). Although diagnostic uncertainty decreased after ECG training in both cohorts, blended learning was associated with better confidence in ECG analysis and interpretation.

CONCLUSION

Blended learning achieved significantly better levels of ECG competence and confidence amongst medical students than conventional ECG teaching did. Although medical students underwent significant attrition of ECG competence without ongoing training, blended learning also resulted in better retention of ECG competence than conventional teaching. Web applications encouraging a stepwise approach to ECG analysis and enabling deliberate practice with feedback may, therefore, be a useful adjunct to lectures for teaching Electrocardiography.

Balancing confidence and humility in the diagnostic process

Humility in medicine can be difficult to achieve, yet arguably is one of the most important competencies to master. Overconfidence, on the contrary, is a natural tendency, having established its roots in evolution where quicker and more confident decisions likely conferred a selective advantage. Moreover, humility may evoke an image of weakness and vulnerability, antithetical to contemporary medicine, whose culture is dominated by overconfidence. Nevertheless, humility can be learned, and is important because overconfident behavior can be detrimental to our patients medically, psychosocially and legally, when it results in delayed or missed diagnoses. To achieve humility requires a great deal of metacognition, normalizing doubt and not being afraid to utilize tools that may feel beneath us. To practice humility requires strength and emotional resilience. In this paper we explore the definitions, roles and implications of humility in medicine, and we pose suggestions of how to accomplish this in the diagnostic process.

Diagnostic heuristics in dermatology, part 2: metacognition and other fixes

Diagnostic errors are the most common, costly and dangerous of medical mistakes. In part 1 of this series, we described how general and dermatology-specific cognitive and perceptual biases underlie most of our correct diagnoses, as well as being a source of diagnostic medical errors. In this second part of the series, we describe some tactics to combat diagnostic error. Metacognition, or thinking about how we think, is the central approach advocated to avoid errors of 'uncritical' diagnostic thinking. Current individual and medical cultural attitudes need to be modified in order to incorporate improvements in diagnosis. Algorithms, artificial intelligence and system changes are being developed to address error and improve diagnostic accuracy.