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Development of Intelligent Systems to Optimize Training and Real-world Performance Amongst Health Care Professionals

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2019, Master of Science, University of Toledo, Engineering (Computer Science).
Training healthcare professionals and providing them with the skills and competencies required for their discipline is essential to reduce medical errors and enhance the efficiency of healthcare procedures. Simulation-Based Medical Education (SBME) is an attempt to provide means to train healthcare professionals. However, due to the lack of standard tools for delivery and assessment of medical simulation, performance during SBME is routinely evaluated by basic pass/fail metrics followed by instructor-led debriefing. Such assessments do not provide a detailed analysis of skills acquired by the learners or provide insight into future training requirements. To address the needs and limitations of current SBME procedures, this thesis proposes an open-access multi-tiered cloud-based learning management system (LMS) PREPARE, that supports PREdiction of Healthcare Provider Skill Acquisition and Future Training REquirements. PREPARE was built using evidence-based SBME guidelines and is an attempt to transform the current process of creating and administrating SBME into an intuitive data-driven platform. To evaluate the capabilities of PREPARE, we conducted a pilot study at the University of Toledo’s Interprofessional Immersive Simulation Center (IISC). With this study, PREPARE was able to establish its utility as an effective tool to standardize creation, assessment, and delivery of SBME. It gives a platform to the instructors to create SBME curriculum in a digital format, which includes curriculum goals/objectives, customizable preassessment and postassessment forms (to determine baseline and post-SBME knowledge and skills). Based on the designed curricula, instructors can define the simulation scenarios that consist of “learning events” focused on enhancing skills required to meet the objectives and the goals. PREPARE allows the instructors to give performance ratings and feedback across learners based on their participation. Additionally, PREPARE collects and process physiological data from learners during participation in SBME to provide potential objective performance measures related to knowledge and skill acquisition. Finally, it also helps the instructor analyze the average performance of learners across a SBME curricula to evaluate which of the curriculum’s goals and objectives were achieved.
Scott Pappada (Committee Chair)
Vijaya Devabhaktuni (Committee Co-Chair)
Thomas Papadimos (Committee Member)
Ahmad Javaid (Committee Member)
Mansoor Alam (Committee Member)
171 p.

Recommended Citations

Citations

  • Owais, M. H. (2019). Development of Intelligent Systems to Optimize Training and Real-world Performance Amongst Health Care Professionals [Master's thesis, University of Toledo]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1556914525013002

    APA Style (7th edition)

  • Owais, Mohammad Hamza. Development of Intelligent Systems to Optimize Training and Real-world Performance Amongst Health Care Professionals. 2019. University of Toledo, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=toledo1556914525013002.

    MLA Style (8th edition)

  • Owais, Mohammad Hamza. "Development of Intelligent Systems to Optimize Training and Real-world Performance Amongst Health Care Professionals." Master's thesis, University of Toledo, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1556914525013002

    Chicago Manual of Style (17th edition)