Effectiveness of hybrid simulation training on medical student performance in whole-task consultation of cardiac patients: The ASSIMILATE EXCELLENCE randomized waitlist-controlled trial.

Cardiology Clerkship Composite narrative Consultation Hybrid simulation Whole task

Journal

Advances in simulation (London, England)
ISSN: 2059-0628
Titre abrégé: Adv Simul (Lond)
Pays: England
ID NLM: 101700425

Informations de publication

Date de publication:
01 Oct 2024
Historique:
received: 26 03 2024
accepted: 20 09 2024
medline: 2 10 2024
pubmed: 2 10 2024
entrez: 1 10 2024
Statut: epublish

Résumé

Assessment of comprehensive consultations in medicine, i.e. a complete history, physical examination, and differential diagnosis, is regarded as authentic tests of clinical competence; however, they have been shown to have low reliability and validity due to variability in the real patients used and subjective examiner grading. In the ASSIMILATE EXCELLENCE study, our aim was to assess the effect(s) of expert tuition with hybrid simulation using a simulated patient wearing a novel auscultation vest, i.e. a hybrid simulated patient, and repeated peer grading using scoring checklists on student learning, performance, and acumen in comprehensive consultations of patients with valvular heart disease. ASSIMILATE EXCELLENCE was a randomized waitlist-controlled trial with blinded outcome assessment undertaken between February 2021 and November 2021. Students at the Royal College of Surgeons in Ireland in either the second or third year of the four-year graduate-entry medical degree programme were randomized to a hybrid simulation training or waitlist control group and undertook three consultation assessments of three different clinical presentations of valvular heart disease (cases: C1-C3) using hybrid simulation. Our primary outcome was the difference in total score between and within groups across time; a secondary outcome was any change in inter-rater reliability across time. Students self-reported their proficiency and confidence in comprehensive consultations using a pre- and post-study survey. Included were 68 students (age 27.6 ± 0.1 years; 74% women). Overall, total score was 39.6% (35.6, 44.9) in C1 and increased to 63.6% (56.7, 66.7) in C3 (P < .001). On intergroup analysis, a significant difference was observed between groups in C2 only (54.2 ± 7.1% vs. 45.6 ± 9.2%; P < .001), a finding that was mainly driven by a difference in physical examination score. On intragroup analysis, significant improvement in total score across time between cases was also observed. Intraclass correlation coefficients for each pair of assessors were excellent (0.885-0.996 [0.806, 0.998]) in all cases. Following participation, students' confidence in comprehensive consultation assessments improved, and they felt more prepared for their future careers. Hybrid simulation-based training improves competence and confidence in medical students undertaking comprehensive consultation assessment of cardiac patients. In addition, weighted scoring checklists improve grading consistency, learning through peer assessment, and feedback. Trial registration ClinicalTrials.gov Identifier: NCT05895799.

Sections du résumé

BACKGROUND BACKGROUND
Assessment of comprehensive consultations in medicine, i.e. a complete history, physical examination, and differential diagnosis, is regarded as authentic tests of clinical competence; however, they have been shown to have low reliability and validity due to variability in the real patients used and subjective examiner grading. In the ASSIMILATE EXCELLENCE study, our aim was to assess the effect(s) of expert tuition with hybrid simulation using a simulated patient wearing a novel auscultation vest, i.e. a hybrid simulated patient, and repeated peer grading using scoring checklists on student learning, performance, and acumen in comprehensive consultations of patients with valvular heart disease.
METHODS METHODS
ASSIMILATE EXCELLENCE was a randomized waitlist-controlled trial with blinded outcome assessment undertaken between February 2021 and November 2021. Students at the Royal College of Surgeons in Ireland in either the second or third year of the four-year graduate-entry medical degree programme were randomized to a hybrid simulation training or waitlist control group and undertook three consultation assessments of three different clinical presentations of valvular heart disease (cases: C1-C3) using hybrid simulation. Our primary outcome was the difference in total score between and within groups across time; a secondary outcome was any change in inter-rater reliability across time. Students self-reported their proficiency and confidence in comprehensive consultations using a pre- and post-study survey.
RESULTS RESULTS
Included were 68 students (age 27.6 ± 0.1 years; 74% women). Overall, total score was 39.6% (35.6, 44.9) in C1 and increased to 63.6% (56.7, 66.7) in C3 (P < .001). On intergroup analysis, a significant difference was observed between groups in C2 only (54.2 ± 7.1% vs. 45.6 ± 9.2%; P < .001), a finding that was mainly driven by a difference in physical examination score. On intragroup analysis, significant improvement in total score across time between cases was also observed. Intraclass correlation coefficients for each pair of assessors were excellent (0.885-0.996 [0.806, 0.998]) in all cases. Following participation, students' confidence in comprehensive consultation assessments improved, and they felt more prepared for their future careers.
CONCLUSIONS CONCLUSIONS
Hybrid simulation-based training improves competence and confidence in medical students undertaking comprehensive consultation assessment of cardiac patients. In addition, weighted scoring checklists improve grading consistency, learning through peer assessment, and feedback. Trial registration ClinicalTrials.gov Identifier: NCT05895799.

Identifiants

pubmed: 39354536
doi: 10.1186/s41077-024-00314-2
pii: 10.1186/s41077-024-00314-2
doi:

Banques de données

ClinicalTrials.gov
['NCT05895799']

Types de publication

Journal Article

Langues

eng

Pagination

40

Informations de copyright

© 2024. The Author(s).

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Auteurs

Michael Daly (M)

RCSI SIM Centre for Simulation Education and Research, Royal College of Surgeons in Ireland (RCSI) University of Medicine and Health Sciences, 123 St. Stephen's Green, Dublin, Ireland. michaeljohndaly@rcsi.ie.
School of Medicine, RCSI University of Medicine and Health Sciences, 123 St. Stephen's Green, Dublin, Ireland. michaeljohndaly@rcsi.ie.
Department of Cardiology, Connolly Hospital, Mill Road, Blanchardstown, Dublin, Ireland. michaeljohndaly@rcsi.ie.

Claire Mulhall (C)

RCSI SIM Centre for Simulation Education and Research, Royal College of Surgeons in Ireland (RCSI) University of Medicine and Health Sciences, 123 St. Stephen's Green, Dublin, Ireland.

James O'Neill (J)

RCSI SIM Centre for Simulation Education and Research, Royal College of Surgeons in Ireland (RCSI) University of Medicine and Health Sciences, 123 St. Stephen's Green, Dublin, Ireland.
School of Medicine, RCSI University of Medicine and Health Sciences, 123 St. Stephen's Green, Dublin, Ireland.
Department of Cardiology, Connolly Hospital, Mill Road, Blanchardstown, Dublin, Ireland.

Walter Eppich (W)

RCSI SIM Centre for Simulation Education and Research, Royal College of Surgeons in Ireland (RCSI) University of Medicine and Health Sciences, 123 St. Stephen's Green, Dublin, Ireland.
Faculty of Medicine, Dentistry, and Health Sciences, Department of Medical Education and Collaborative Practice Centre, The University of Melbourne, Melbourne, Australia.

Jonathan Shpigelman (J)

School of Medicine, RCSI University of Medicine and Health Sciences, 123 St. Stephen's Green, Dublin, Ireland.

Caitriona Cahir (C)

Data Science Centre, School of Population Health, RCSI University of Medicine and Health Sciences, 123 St Stephen's Green, Dublin, Ireland.

Daniel Fraughen (D)

School of Medicine, RCSI University of Medicine and Health Sciences, 123 St. Stephen's Green, Dublin, Ireland.

Enda McElduff (E)

School of Medicine, RCSI University of Medicine and Health Sciences, 123 St. Stephen's Green, Dublin, Ireland.

Catherine Uhomoibhi (C)

School of Medicine, RCSI University of Medicine and Health Sciences, 123 St. Stephen's Green, Dublin, Ireland.

Claire Condron (C)

RCSI SIM Centre for Simulation Education and Research, Royal College of Surgeons in Ireland (RCSI) University of Medicine and Health Sciences, 123 St. Stephen's Green, Dublin, Ireland.

Classifications MeSH