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Using smartphones for trainee performance assessment: A SIMPL case study.

Brian C George1, Jordan D Bohnen2, Mary C Schuller3

  • 1Center for Surgical Training and Research, Michigan Medicine, University of Michigan, Ann Arbor, MI.

Surgery
|November 1, 2019
PubMed
Summary
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Workplace-based assessments using smartphone technology can improve trainee performance evaluation. This study explores the benefits and challenges of implementing such mobile health tools in clinical education.

Area of Science:

  • Medical Education
  • Health Informatics
  • Competency-Based Education

Background:

  • Workplace-based assessments evaluate trainee performance in clinical settings.
  • There is increasing interest in these assessments due to the focus on observed performance in medical education.
  • Implementing workplace-based assessments presents significant challenges.

Purpose of the Study:

  • To explore the motivations and implications of using smartphone technology for workplace-based assessments.
  • To highlight the challenges and benefits of implementing mobile assessment systems.
  • To discuss the potential of technology in enhancing procedural learning and assessment.

Main Methods:

  • Review of motivations and implications for smartphone-based workplace assessments.

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  • Case study analysis of the SIMPL (System for Improving and Measuring Procedural Learning) app.
  • Discussion of implementation challenges and benefits.

    • Main Results:

    • Smartphone technology offers potential for efficient and effective workplace-based assessments.
    • The SIMPL app provides insights into practical application, challenges, and benefits.
    • Leveraging mobile technology can support competency-based medical education goals.

    Conclusions:

    • Smartphone-based workplace assessments are a promising avenue for medical education.
    • Careful planning and consideration of challenges are crucial for successful implementation.
    • Technology integration can significantly enhance the evaluation of clinical skills and procedural learning.