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Medical Students' Clinical Reasoning During a Simulated Viral Pandemic: Evidence of Cognitive Integration and Insights on Novices' Approach to Diagnostic Reasoning.

ABSTRACT: Introduction:Cognitive integration from multiple disciplines is essential to clinical problem-solving. Because it is not directly observable, demonstrating evidence of learners' cognitive integration remains a challenge. In addition, little is known about preclinical medical students' approach to diagnostic reasoning despite widespread implementation of clinical reasoning curricula for these early learners. The objectives of this study were to characterize how first-year medical students integrated knowledge to problem-solve during a simulated viral pandemic and to characterize students' diagnostic reasoning approach to this clinical scenario. Materials and Methods:Student teams analyzed clinical data to formulate hypotheses for the pandemic's source and submitted reports justifying their hypotheses and treatment recommendations. A content analysis on students' reports identified codes and themes characterizing the learning content integrated and students' approaches to diagnostic reasoning tasks. Results:Sixteen problem-solving codes were identified, demonstrating integration of new and previously encountered content from multiple disciplines. A compare-contrast analytical approach was the most commonly employed diagnostic reasoning approach (100%), with a smaller subset of teams also using a causal approach (20%). Discussion:Content analysis of preclinical students' diagnostic justification tasks provided insights into their approach to diagnostic reasoning, which was most consistent with the search-inference framework rather than a causal approach, likely due to limited pathophysiological knowledge at that point in training. Conclusions:Evidence of cognitive integration can be made explicit through learners' narrative justification of diagnostic reasoning tasks. Preclinical students' diagnostic reasoning development has implications for curricular design and implementation for this learner group.

SUBMITTER: Jackson JM

PROVIDER: S-EPMC7202796 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Medical Students' Clinical Reasoning During a Simulated Viral Pandemic: Evidence of Cognitive Integration and Insights on Novices' Approach to Diagnostic Reasoning.

Jackson Jennifer M JM Skelton Joseph A JA Peters Timothy R TR

Medical science educator 20200506 2

<h4>Introduction</h4>Cognitive integration from multiple disciplines is essential to clinical problem-solving. Because it is not directly observable, demonstrating evidence of learners' cognitive integration remains a challenge. In addition, little is known about preclinical medical students' approach to diagnostic reasoning despite widespread implementation of clinical reasoning curricula for these early learners. The objectives of this study were to characterize how first-year medical students ...[more]

PMID: 32382451

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Project description:PurposeGraduating medical students need broad clinical diagnostic reasoning skills that integrate learning across clinical specialties to deal with undifferentiated patient problems. The opportunity to acquire these skills may be limited during clinical placements on increasingly specialized hospital wards. We developed an intervention of regular general practitioner (GP) facilitated teaching in hospital placements to enable students to develop broad clinical diagnostic reasoning. The intervention was piloted, refined and delivered to a whole cohort of medical students at the start of their third year. This paper examines whether students perceived opportunities to improve their broad diagnostic clinical reasoning through our intervention.MethodsGP-facilitated teaching sessions were delivered weekly in hospital placements to small groups of 6–8 students for 90 mins over 6 weeks. Students practiced clinical reasoning with real patient cases that they encountered on their placements. Evaluation of learning outcomes was conducted through a student questionnaire using Likert scales with free-text boxes for additional explanation. Focus groups were conducted to gain a more in-depth understanding of student perspectives.ResultsAs high as 87% of students agreed that their broad clinical diagnostic reasoning ability had improved. Thematic analysis of the qualitative data revealed four factors supporting this improvement: practicing the hypothetico-deductive method, using real patient cases, composing student groups from different speciality placements and the breadth of the facilitators’ knowledge. Students additionally reported enhanced person-centredness in terms of understanding the patient’s perspective and journey. Students perceived that the added value of general practitioner facilitators lay in their broad knowledge base and knowledge of patient needs in the community.ConclusionOur results suggest that medical students can develop broad clinical diagnostic reasoning skills in hospital settings through regular GP-facilitated teaching. Our approach has the advantage of working within the established curricular format of hospital placements and being deliverable at scale to whole student cohorts.

Project description:BACKGROUND:Over-testing of patients is a significant problem in clinical medicine that can be tackled by education. Clinical reasoning learning (CRL) is a potentially relevant method for teaching test ordering and interpretation. The feasibility might be improved by using an interactive whiteboard (IWB) during the CRL sessions to enhance student perceptions and behaviours around diagnostic tests. Overall, IWB/CRL could improve their skills. METHODS:Third-year undergraduate medical students enrolled in a vertically integrated curriculum were randomized into two groups before clinical placement in either a respiratory disease or respiratory physiology unit: IWB-based CRL plus clinical mentoring (IWB/CRL?+?CM: n?=?40) or clinical mentoring only (CM-only: n?=?40). Feasibility and learning outcomes were assessed. In addition, feedback via questionnaire of the IWB students and their classmates (n?=?233) was compared. RESULTS:Analyses of the IWB/CRL sessions (n?=?40, 27 paperboards) revealed that they met validated learning objectives. Students perceived IWB as useful and easy to use. After the IWB/CRL?+?CM sessions, students mentioned more hypothesis-based indications in a test ordering file (p?<? 0.001) and looked for more nonclinical signs directly on raw data tests (p?<? 0.01) compared with students in the CM-only group. Last, among students who attended pre- and post-assessments (n?=?23), the number of diagnostic tests ordered did not change in the IWB/CRL?+?CM group (+?7%; p?=?N.S), whereas it increased among CM-only students (+?30%; p?<? 0.001). Test interpretability increased significantly in the IWB/CRL?+?CM group (from 4.7 to 37.2%; p?<? 0.01) but not significantly in the CM-only group (from 2.4 to 9.8%; p?=?0.36). CONCLUSIONS:Integrating IWB into CRL sessions is feasible to teach test ordering and interpretation to undergraduate students. Moreover, student feedback and prospective assessment suggested a positive impact of IWB/CRL sessions on students' learning.

Project description:PurposeDeveloping medical students' clinical reasoning requires a structured longitudinal curriculum with frequent targeted assessment and feedback. Performance-based assessments, which have the strongest validity evidence, are currently not feasible for this purpose because they are time-intensive to score. This study explored the potential of using machine learning technologies to score one such assessment-the diagnostic justification essay.MethodFrom May to September 2018, machine scoring algorithms were trained to score a sample of 700 diagnostic justification essays written by 414 third-year medical students from the Southern Illinois University School of Medicine classes of 2012-2017. The algorithms applied semantically based natural language processing metrics (e.g., coherence, readability) to assess essay quality on 4 criteria (differential diagnosis, recognition and use of findings, workup, and thought process); the scores for these criteria were summed to create overall scores. Three sources of validity evidence (response process, internal structure, and association with other variables) were examined.ResultsMachine scores correlated more strongly with faculty ratings than faculty ratings did with each other (machine: .28-.53, faculty: .13-.33) and were less case-specific. Machine scores and faculty ratings were similarly correlated with medical knowledge, clinical cognition, and prior diagnostic justification. Machine scores were more strongly associated with clinical communication than were faculty ratings (.43 vs .31).ConclusionsMachine learning technologies may be useful for assessing medical students' long-form written clinical reasoning. Semantically based machine scoring may capture the communicative aspects of clinical reasoning better than faculty ratings, offering the potential for automated assessment that generalizes to the workplace. These results underscore the potential of machine scoring to capture an aspect of clinical reasoning performance that is difficult to assess with traditional analytic scoring methods. Additional research should investigate machine scoring generalizability and examine its acceptability to trainees and educators.

Dataset Information

Medical Students' Clinical Reasoning During a Simulated Viral Pandemic: Evidence of Cognitive Integration and Insights on Novices' Approach to Diagnostic Reasoning.

Publications

Medical Students' Clinical Reasoning During a Simulated Viral Pandemic: Evidence of Cognitive Integration and Insights on Novices' Approach to Diagnostic Reasoning.

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