Teams in Medicine

To effectively diagnose and treat patients, physicians must often cooperate, coordinate, and problem-solve with other healthcare workers. Successful teams in medicine define and work toward shared goals, employ the pertinent and appropriate qualifications of each member, and establish trust in other team members. Task-shifting passes certain roles onto other team members so that physicians can focus on their key areas of responsibility. A number of medical schools have adopted interprofessional education to train today’s physicians, but there are other strategies that are also being explored. Training modules such as Crew Resource Management and TeamSTEPPSTM are being adapted to the needs of the healthcare field. High-fidelity patient simulations are being used to replicate common situations encountered by healthcare workers. Also, many medical schools are using problem-based or team-based learning practices through which students work with one another to tackle and reason through clinical scenarios.

Using Online Patient Simulations to Identify the Clinical Reasoning Strategies of Medical Students: A Mixed Methods Study (Preprint)

BACKGROUND Improving clinical reasoning skills — the thought processes used by clinicians during consultations to formulate appropriate questions and diagnoses — is essential for reducing missed diagnostic opportunities. The electronic Clinical Reasoning Educational Simulation Tool (eCREST) was developed to improve future doctors’ clinical reasoning skills. A feasibility study demonstrated acceptability and potential impacts but the processes by which students developed their clinical reasoning is unknown. OBJECTIVE To identify and characterize final-year medical students’ clinical reasoning strategies while using eCREST; to explore how students interacted with eCREST. METHODS A sequential mixed methods design was used. Quantitative data captured in a feasibility trial across three UK medical schools (n=148) was used to identify typologies of reasoning, based on the proportion of essential information students identified and the proportion of relevant questions they asked a virtual patient. Strategies were compared between the intervention and control group. A qualitative think-aloud and semi-structured interview study was then undertaken with 16 final year medical students from one medical school to explore how students reasoned while using eCREST. Themes generated from qualitative data were used to expand the typologies of strategies. RESULTS Three types of clinical reasoning strategy were identified: ‘Focused’ (elicited most essential information and asked few irrelevant questions; n=78/148, 53%), ‘Thorough’ (elicited most essential information but asked many irrelevant questions; n=33/148, 22%) and ‘Succinct’ (elicited little essential information but asked few irrelevant questions; n=27/148, 18%). One group were ‘Non-strategic’ (did not elicit enough essential information and asked mostly irrelevant questions; n=10/148, 7%). In the feasibility trial, the intervention group, were significantly more likely to adopt a ‘Thorough’ strategy than controls (21/78, 27% vs 6/70, 9%) and less likely to adopt a ‘Succinct’ strategy (13/78, 17% vs 20/70, 29%); χ2 (3)=9.87, P=.02. Use of other strategies were similar across groups. Thematic analysis identified three dimensions underpinning reasoning: data gathering processes, generating diagnostic hypotheses, confidence and uncertainty. The mixed methods analysis indicated that those classified as ‘Thorough’ asked many questions to avoid missing key information and reported that eCREST helped them to manage uncertainty. The ‘Succinct’ group aimed to limit the number of questions asked and eCREST helped them to focus on asking pertinent questions. The ‘Focused’ group had clear rationales for asking questions but those who used a ‘Non-strategic’ approach did not and may have found eCREST less useful in developing their clinical reasoning. CONCLUSIONS Students apply a range of clinical reasoning strategies to online patient simulations like eCREST. eCREST led students to use more ‘Thorough’ strategies and students reported it helped them to manage uncertainty, which could help future doctors to identify missed diagnostic opportunities. eCREST could also be used by educators to support students to develop their clinical reasoning strategies.

The Utility of Virtual Patient Simulations for Clinical Reasoning Education

Virtual Patient Simulations (VPSs) have been cited as a novel learning strategy, but there is little evidence that VPSs yield improvements in clinical reasoning skills and medical knowledge. This study aimed to clarify the effectiveness of VPSs for improving clinical reasoning skills among medical students, and to compare improvements in knowledge or clinical reasoning skills relevant to specific clinical scenarios. We enrolled 210 fourth-year medical students in March 2017 and March 2018 to participate in a real-time pre-post experimental design conducted in a large lecture hall by using a clicker. A VPS program (®Body Interact, Portugal) was implemented for one two-hour class session using the same methodology during both years. A pre–post 20-item multiple-choice questionnaire (10 knowledge and 10 clinical reasoning items) was used to evaluate learning outcomes. A total of 169 students completed the program. Participants showed significant increases in average total post-test scores, both on knowledge items (pre-test: median = 5, mean = 4.78, 95% CI (4.55–5.01); post-test: median = 5, mean = 5.12, 95% CI (4.90–5.43); p-value = 0.003) and clinical reasoning items (pre-test: median = 5, mean = 5.3 95%, CI (4.98–5.58); post-test: median = 8, mean = 7.81, 95% CI (7.57–8.05); p-value < 0.001). Thus, VPS programs could help medical students improve their clinical decision-making skills without lecturer supervision.

Reliability and validity of scenario-specific versus generic simulation assessment rubrics

Background: This study assessed reliability and validity of scenario-specific and generic simulation assessment rubrics used in two different deteriorating patient simulations, and explored learner and instructor preferences.Methods: Learner performance was rated independently by three instructors using two rubrics.Results: A convenience sample of 29 nursing students was recruited. Inter-rater reliability was similar but slightly higher for the generic rubric than the scenario-specific learning outcomes assessment rubric (ICC = .759 vs .748 and IRR = .693 vs .641) for two different scenarios. Most students found the scenario-specific rubric more helpful to their learning (59%), and easier to use (52%). Instructors (3/3) found the scenario-specific rubric more helpful to guide debriefing.Conclusions: Scenario-specific rubrics may be more valuable for learners to help them identify their own knowledge and performance gaps and assist them in their preparation for simulation. Additionally, scenario-specific rubrics provide direction for both learners and instructors during debriefing sessions.