Presentation Title

New Assessment Approaches Using Population Analysis for Simulation-Based Medical Training: A Pilot Study with a Focus on Complex Surgical Skills

Presenter Information

Saiteja MalisettyFollow

Advisor Information

Hesham Ali

Location

MBSC Ballroom - Poster #202 - G

Presentation Type

Poster

Start Date

4-3-2022 10:45 AM

End Date

4-3-2022 12:00 PM

Abstract

The United States is facing a crisis in staffing both doctors and nurses. There is a need to make training faster, less expensive, and more accessible, yet clinical education remains largely the same as it has been in the past. Simulation is an excellent solution. Although simulation is often an available tool for trainees interested in perfecting medical skills, it has yet to be transformative in experiential learning often desired by clinical educators. One critical barrier to more meaningful simulation is evaluation. Developments in network models can dramatically improve the value of simulation by allowing the simulation itself to evaluate the trainee's proficiency. Laparoscopic surgery relies on simulation to teach fundamental surgical skills and requires mastery through simulation of this difficult technique. In this study, we used a similarity network model to evaluate and assess trainees’ proficiency. Our results demonstrated that network-based evaluation can precisely capture the progress of learning in trainees, identify individuals who need additional training, and promote skilled trainees to accelerate their learning experience. Such precision and personalized training will be cost-effective and provide an optimal learning environment for trainees to efficiently accelerate the rate of skills acquisition.

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Mar 4th, 10:45 AM Mar 4th, 12:00 PM

New Assessment Approaches Using Population Analysis for Simulation-Based Medical Training: A Pilot Study with a Focus on Complex Surgical Skills

MBSC Ballroom - Poster #202 - G

The United States is facing a crisis in staffing both doctors and nurses. There is a need to make training faster, less expensive, and more accessible, yet clinical education remains largely the same as it has been in the past. Simulation is an excellent solution. Although simulation is often an available tool for trainees interested in perfecting medical skills, it has yet to be transformative in experiential learning often desired by clinical educators. One critical barrier to more meaningful simulation is evaluation. Developments in network models can dramatically improve the value of simulation by allowing the simulation itself to evaluate the trainee's proficiency. Laparoscopic surgery relies on simulation to teach fundamental surgical skills and requires mastery through simulation of this difficult technique. In this study, we used a similarity network model to evaluate and assess trainees’ proficiency. Our results demonstrated that network-based evaluation can precisely capture the progress of learning in trainees, identify individuals who need additional training, and promote skilled trainees to accelerate their learning experience. Such precision and personalized training will be cost-effective and provide an optimal learning environment for trainees to efficiently accelerate the rate of skills acquisition.