Deliberate practice of diagnostic clinical reasoning reveals low performance and improvement of diagnostic justification in pre-clerkship students.

PURPOSE: Diagnostic errors are a large burden on patient safety and improving clinical reasoning (CR) education could contribute to reducing these errors. To this end, calls have been made to implement CR training as early as the first year of medical school. However, much is still unknown about pre-clerkship students' reasoning processes. The current study aimed to observe how pre-clerkship students use clinical information during the diagnostic process. METHODS: In a prospective observational study, pre-clerkship medical students completed 10-11 self-directed online simulated CR diagnostic cases. CR skills assessed included: creation of the differential diagnosis (Ddx), diagnostic justification (DxJ), ordering investigations, and identifying the most probable diagnosis. Student performances were compared to expert-created scorecards and students received detailed individualized formative feedback for every case. RESULTS: 121 of 133 (91%) first- and second-year medical students consented to the research project. Students scored much lower for DxJ compared to scores obtained for creation of the Ddx, ordering tests, and identifying the correct diagnosis, (30-48% lower, p < 0.001). Specifically, students underutilized physical exam data (p < 0.001) and underutilized data that decreased the probability of incorrect diagnoses (p < 0.001). We observed that DxJ scores increased 40% after 10-11 practice cases (p < 0.001). CONCLUSIONS: We implemented deliberate practice with formative feedback for CR starting in the first year of medical school. Students underperformed in DxJ, particularly with analyzing the physical exam data and pertinent negative data. We observed significant improvement in DxJ performance with increased practice.

Impact of diagnostic checklists on the interpretation of normal and abnormal electrocardiograms.

OBJECTIVES: Checklists that aim to support clinicians' diagnostic reasoning processes are often recommended to prevent diagnostic errors. Evidence on checklist effectiveness is mixed and seems to depend on checklist type, case difficulty, and participants' expertise. Existing studies primarily use abnormal cases, leaving it unclear how the diagnosis of normal cases is affected by checklist use. We investigated how content-specific and debiasing checklists impacted performance for normal and abnormal cases in electrocardiogram (ECG) diagnosis. METHODS: In this randomized experiment, 42 first year general practice residents interpreted normal, simple abnormal, and complex abnormal ECGs without a checklist. One week later, they were randomly assigned to diagnose the ECGs again with either a debiasing or content-specific checklist. We measured residents' diagnostic accuracy, confidence, patient management, and time taken to diagnose. Additionally, confidence-accuracy calibration was assessed. RESULTS: Accuracy, confidence, and patient management were not significantly affected by checklist use. Time to diagnose decreased with a checklist (M=147s (77)) compared to without a checklist (M=189s (80), Z=-3.10, p=0.002). Additionally, residents' calibration improved when using a checklist (phase 1: R2=0.14, phase 2: R2=0.40). CONCLUSIONS: In both normal and abnormal cases, checklist use improved confidence-accuracy calibration, though accuracy and confidence were not significantly affected. Time to diagnose was reduced. Future research should evaluate this effect in more experienced GPs. Checklists appear promising for reducing overconfidence without negatively impacting normal or simple ECGs. Reducing overconfidence has the potential to improve diagnostic performance in the long term.

Effect on diagnostic accuracy of cognitive reasoning tools for the workplace setting: systematic review and meta-analysis.

BACKGROUND: Preventable diagnostic errors are a large burden on healthcare. Cognitive reasoning tools, that is, tools that aim to improve clinical reasoning, are commonly suggested interventions. However, quantitative estimates of tool effectiveness have been aggregated over both workplace-oriented and educational-oriented tools, leaving the impact of workplace-oriented cognitive reasoning tools alone unclear. This systematic review and meta-analysis aims to estimate the effect of cognitive reasoning tools on improving diagnostic performance among medical professionals and students, and to identify factors associated with larger improvements. METHODS: Controlled experimental studies that assessed whether cognitive reasoning tools improved the diagnostic accuracy of individual medical students or professionals in a workplace setting were included. Embase.com, Medline ALL via Ovid, Web of Science Core Collection, Cochrane Central Register of Controlled Trials and Google Scholar were searched from inception to 15 October 2021, supplemented with handsearching. Meta-analysis was performed using a random-effects model. RESULTS: The literature search resulted in 4546 articles of which 29 studies with data from 2732 participants were included for meta-analysis. The pooled estimate showed considerable heterogeneity (I2=70%). This was reduced to I2=38% by removing three studies that offered training with the tool before the intervention effect was measured. After removing these studies, the pooled estimate indicated that cognitive reasoning tools led to a small improvement in diagnostic accuracy (Hedges' g=0.20, 95% CI 0.10 to 0.29, p<0.001). There were no significant subgroup differences. CONCLUSION: Cognitive reasoning tools resulted in small but clinically important improvements in diagnostic accuracy in medical students and professionals, although no factors could be distinguished that resulted in larger improvements. Cognitive reasoning tools could be routinely implemented to improve diagnosis in practice, but going forward, more large-scale studies and evaluations of these tools in practice are needed to determine how these tools can be effectively implemented. PROSPERO REGISTRATION NUMBER: CRD42020186994.

To Explore the Predictive Power of Visuomotor Network Dysfunctions in Mild Cognitive Impairment and Alzheimer's Disease.

BACKGROUND: Research into Alzheimer's disease has shifted toward the identification of minimally invasive and less time-consuming modalities to define preclinical stages of Alzheimer's disease. METHOD: Here, we propose visuomotor network dysfunctions as a potential biomarker in AD and its prodromal stage, mild cognitive impairment with underlying the Alzheimer's disease pathology. The functionality of this network was tested in terms of timing, accuracy, and speed with goal-directed eye-hand tasks. The predictive power was determined by comparing the classification performance of a zero-rule algorithm (baseline), a decision tree, a support vector machine, and a neural network using functional parameters to classify controls without cognitive disorders, mild cognitive impaired patients, and Alzheimer's disease patients. RESULTS: Fair to good classification was achieved between controls and patients, controls and mild cognitive impaired patients, and between controls and Alzheimer's disease patients with the support vector machine (77-82% accuracy, 57-93% sensitivity, 63-90% specificity, 0.74-0.78 area under the curve). Classification between mild cognitive impaired patients and Alzheimer's disease patients was poor, as no algorithm outperformed the baseline (63% accuracy, 0% sensitivity, 100% specificity, 0.50 area under the curve). COMPARISON WITH EXISTING METHODS: The classification performance found in the present study is comparable to that of the existing CSF and MRI biomarkers. CONCLUSION: The data suggest that visuomotor network dysfunctions have potential in biomarker research and the proposed eye-hand tasks could add to existing tests to form a clear definition of the preclinical phenotype of AD.

Specific Disease Knowledge as Predictor of Susceptibility to Availability Bias in Diagnostic Reasoning: a Randomized Controlled Experiment.

BACKGROUND: Bias in reasoning rather than knowledge gaps has been identified as the origin of most diagnostic errors. However, the role of knowledge in counteracting bias is unclear. OBJECTIVE: To examine whether knowledge of discriminating features (findings that discriminate between look-alike diseases) predicts susceptibility to bias. DESIGN: Three-phase randomized experiment. Phase 1 (bias-inducing): Participants were exposed to a set of clinical cases (either hepatitis-IBD or AMI-encephalopathy). Phase 2 (diagnosis): All participants diagnosed the same cases; 4 resembled hepatitis-IBD, 4 AMI-encephalopathy (but all with different diagnoses). Availability bias was expected in the 4 cases similar to those encountered in phase 1. Phase 3 (knowledge evaluation): For each disease, participants decided (max. 2 s) which of 24 findings was associated with the disease. Accuracy of decisions on discriminating features, taken as a measure of knowledge, was expected to predict susceptibility to bias. PARTICIPANTS: Internal medicine residents at Erasmus MC, Netherlands. MAIN MEASURES: The frequency with which higher-knowledge and lower-knowledge physicians gave biased diagnoses based on phase 1 exposure (range 0-4). Time to diagnose was also measured. KEY RESULTS: Sixty-two physicians participated. Higher-knowledge physicians yielded to availability bias less often than lower-knowledge physicians (0.35 vs 0.97; p = 0.001; difference, 0.62 [95% CI, 0.28-0.95]). Whereas lower-knowledge physicians tended to make more of these errors on subjected-to-bias than on not-subjected-to-bias cases (p = 0.06; difference, 0.35 [CI, - 0.02-0.73]), higher-knowledge physicians resisted the bias (p = 0.28). Both groups spent more time to diagnose subjected-to-bias than not-subjected-to-bias cases (p = 0.04), without differences between groups. CONCLUSIONS: Knowledge of features that discriminate between look-alike diseases reduced susceptibility to bias in a simulated setting. Reflecting further may be required to overcome bias, but succeeding depends on having the appropriate knowledge. Future research should examine whether the findings apply to real practice and to more experienced physicians.