Literature DB >> 26548851

Adaptive simulation training using cumulative sum: a randomized prospective trial.

Yinin Hu1, Kendall D Brooks1, Helen Kim1, Adela Mahmutovic1, Joanna Choi1, Ivy A Le1, Bartholomew J Kane1, Eugene D McGahren1, Sara K Rasmussen2.   

Abstract

BACKGROUND: Cumulative sum (Cusum) is a novel tool that can facilitate adaptive, individualized training curricula. The purpose of this study was to use Cusum to streamline simulation-based training.
METHODS: Preclinical medical students were randomized to Cusum or control arms and practiced suturing, intubation, and central venous catheterization in simulation. Control participants practiced between 8 and 9 hours each. Cusum participants practiced until Cusum proficient in all tasks. Group comparisons of blinded post-test evaluations were performed using Wilcoxon rank sum.
RESULTS: Forty-eight participants completed the study. Average post-test composite score was 92.1% for Cusum and 93.5% for control (P = .71). Cusum participants practiced 19% fewer hours than control group participants (7.12 vs 8.75 hours, P < .001). Cusum detected proficiency relapses during practice among 7 (29%) participants for suturing and 10 (40%) for intubation.
CONCLUSIONS: In this comparison between adaptive and volume-based curricula in surgical training, Cusum promoted more efficient time utilization while maintaining excellent results.
Copyright © 2016 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Adaptive learning; Cumulative sum; Medical student education; Resident education; Surgical education; Surgical simulation

Mesh:

Year:  2015        PMID: 26548851     DOI: 10.1016/j.amjsurg.2015.08.030

Source DB:  PubMed          Journal:  Am J Surg        ISSN: 0002-9610            Impact factor:   2.565


  3 in total

1.  Long-Term Skills Retention Following a Randomized Prospective Trial on Adaptive Procedural Training.

Authors:  Adriana G Ramirez; Yinin Hu; Helen Kim; Sara K Rasmussen
Journal:  J Surg Educ       Date:  2018-05-24       Impact factor: 2.891

2.  A machine learning approach to predict surgical learning curves.

Authors:  Yuanyuan Gao; Uwe Kruger; Xavier Intes; Steven Schwaitzberg; Suvranu De
Journal:  Surgery       Date:  2019-11-18       Impact factor: 3.982

3.  A Qualitative Examination Detailing Medical Student Experiences of a Novel Competency-Based Neuroanatomy eLearning Intervention Designed to Bridge a Gap Within an Integrated Medical Curriculum.

Authors:  Elizabeth Paige Hart; Jennifer Brueckner-Collins; Jessica S Bergden
Journal:  J Med Educ Curric Dev       Date:  2021-07-24
  3 in total

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