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Comparing Basic Life Support Serious Gaming Scores With Hands-on Training Platform Performance Scores: Pilot

Mehmet Emin Aksoy1

  • 1Acibadem Mehmet Ali Aydınlar University, Department Biomedical Device Technology, CASE (Center of Advanced Simulation and Education), Istanbul, Turkey.

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Summary

A new platform combines serious game and hands-on data for basic life support (BLS) training evaluation. This approach offers more objective performance assessment than traditional methods, enhancing healthcare education.

Keywords:
basic life supportmedical simulationserious gaming

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Area of Science:

  • Medical Education
  • Simulation-based Training
  • Health Informatics

Background:

  • Serious games enhance healthcare training by improving knowledge, skills, and confidence while providing entertainment.
  • A unified platform integrating serious game and hands-on performance data for basic life support (BLS) training is currently unavailable.

Purpose of the Study:

  • To develop a platform combining BLS serious game app performance data with hands-on simulator data.
  • To investigate if this platform provides more objective BLS training evaluations compared to conventional Objective Structured Clinical Examination (OSCE).

Main Methods:

  • Developed a BLS serious game module and a hands-on module with sensor data retrieval (compression depth, frequency, ventilation volume).
  • Collected data from 25 novice participants using the serious game and hands-on modules.
  • Compared hands-on module scores with conventional OSCE scores derived from video recordings.

Main Results:

  • Participants averaged 1.4 trials to achieve an 80/100 score in the game.
  • Mean scores: BLS serious game (88.3/100), hands-on (70.7/100), and OSCE (84.4/100).
  • No significant correlation found between game, hands-on, and OSCE scores.

Conclusions:

  • OSCE scores were significantly higher (17%) than hands-on scores despite identical criteria, attributed to differences in objective sensor data.
  • Integrating visual OSCE criteria with simulator sensor data offers a more objective evaluation modality for BLS training.
  • The developed platform has the potential to enhance the objectivity of performance evaluation in healthcare simulations.