63 research outputs found
A Graduatte Level Immersive-Simulattion Program for Teaching and Assessing Fundamental Skills in Entry Level Clinical Perfusionists.
Background: The clinical perfusionist is a member of the open-heart-surgery team and responsible for operating the life support equipment that replaces the function of the patient\u27s heart and lungs and arrests and restarts the patient\u27s heart in the course of a Cardiopulmonary Bypass (CPB) procedure. In the perfusionists scope of practice, the consequence of unskilled actions, inaccurate understanding or delayed decision making may result in significant patient morbidity or even death. Historically, perfusion students have learned and practiced their skills within a clinical preceptorship program in which an experienced clinician allows the novice student to operate the life support equipment under their direct supervision and consultation. While there is clinical evidence from numerous surgical specialties which establishes that learning curve associated errors have a negative effect on patient outcomes, this has not been researched for clinical perfusionists. Despite this evidence gap, the professions leaders have been instrumental in driving educational innovation and the development of medical simulation models that may reduce the patient\u27s exposure to learning curve associated morbidity by developing competence with high-risk clinical skills prior to patient contact. The purpose of this research is to develop, validate and apply novel medical simulation techniques and technologies to the preparation of entry level clinical perfusionists and demonstrate pre-clinical competence with the fundamental perfusion skills.Methods and Results: To inform the development of a skills curriculum we conducted two national surveys using online survey tools. Through these surveys we validated a list of fundamental skills, and the deconstructed sub-elements involved in the conduct of these skills. Additionally, we identified the typical ranges of physiologic and technical parameters that clinicians maintain during clinical procedures. With this foundational benchmark data we validated the performance of a simulated patient to establish that the patient surrogate generates data that is substantially similar to the physiologic and technical data that a perfusionist would manage during a live clinical procedure. This validated simulation technology was then incorporated into a high-fidelity simulation suite and applied to an innovative immersive curriculum which included hands on repetitive practice, live and video supported self, peer and expert observation and feedback as well as a battery of high-stakes assessments. The validity and fidelity of the simulated experience was established through analysis of over 800 opinions generated over 10 years by novice and expert perfusionists after performing simulated cases. Finally, the efficacy of the simulation curriculum was assessed by comparing our simulation trained students to a national pool of their peers from other schools and expert clinicians. Through this process we generated the first measurements of the typical learning curve for the fundamental skills of CPB, the first estimates of error rates for students navigating the learning curve and the first benchmark measures of competent performance in a simulated environment. This data establishes that students learning in traditional clinical training programs conduct three-fold more errors than experts and will have approximately 99 high-risk patient encounters prior to developing competence with fundamental skills. By comparison, simulation trained students demonstrated competence with fundamental skills that was similar to the experts with almost no high-risk patient encounters. Discussion: The implications to patient safety are clearly implied. These studies establish that there is a high level of agreement among clinicians regarding the skills that are necessary to operate perfusion equipment and that realistic simulation environments can be designed and applied to the development of student\u27s fundamental perfusion skills without exposing patients to the threat of students learning curve associated errors. This data may catalyze a larger national dialog regarding Entrustable Professional Activities for perfusionists and influence national accreditation standards for educational programs
Catalog 1987-88
https://openspace.dmacc.edu/catalogs/1014/thumbnail.jp
Cognitive Foundations for Visual Analytics
In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions
Catalog 1985-86
https://openspace.dmacc.edu/catalogs/1012/thumbnail.jp
Catalog 1988-89
https://openspace.dmacc.edu/catalogs/1015/thumbnail.jp
Catalog 1990-92
https://openspace.dmacc.edu/catalogs/1016/thumbnail.jp
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AIRM: a new AI Recruiting Model for the Saudi Arabian labour market
One of the goals of Saudi Vision 2030 is to keep the unemployment rate at the lowest level to empower the economy. Prior research has shown that an increase in unemployment has a negative effect on a country’s Gross Domestic Product. This research aims to utilise cutting-edge technology such as Data Lake (DL), Machine Learning (ML) and Artificial Intelligence (AI) to assist the Saudi labour market bymatching job seekers with vacant positions. Currently, human experts carry out this process; however, this is time consuming and labour intensive. Moreover, in the Saudi labour market, this process does not use a cohesive data centre to monitor, integrate, or analyse labour market data, resulting in inefficiencies, such as bias and latency. These inefficiencies arise from a lack of technologies and, more importantly, from having an open labour market without a national labour market data centre. This research proposes a new AI Recruiting Model (AIRM) architecture that exploits DLs, ML and AI to rapidly and efficiently match job seekers to vacant positions in the Saudi labour market. A Minimum Viable Product (MVP) is employed to test the proposed AIRM architecture using a labour market dataset simulation corpus for training purposes; the architecture is further evaluated against three research-collaborative Human Resources (HR) professionals. As this research is data-driven in nature, it requires collaboration from domain experts. The first layer of the AIRM architecture uses balanced iterative reducing and clustering using hierarchies (BIRCH) as a clustering algorithm for the initial screening layer. The mapping layer uses sentence transformers with a robustly optimised BERTt pre-training approach (RoBERTa) as the base model, and ranking is carried out using the Facebook AI Similarity Search (FAISS). Finally, the preferences layer takes the user’s preferences as a list and sorts the results using the pre-trained cross-encoders model, considering the weight of the more important words. This new AIRM has yielded favourable outcomes: This research considered accepting an AIRM selection ratified by at least one HR expert to account for the subjective character of the selection process when exclusively handled by human HR experts. The research evaluated the AIRM using two metrics: accuracy and time. The AIRM had an overall matching accuracy of 84%, with at least one expert agreeing with the system’s output. Furthermore, it completed the task in 2.4 minutes, whereas human experts took more than six days on average. Overall, the AIRM outperforms humans in task execution, making it useful in pre-selecting a group of applicants and positions. The AIRM is not limited to government services. It can also help any commercial business that uses Big Data
Catalog 1992-93
https://openspace.dmacc.edu/catalogs/1017/thumbnail.jp
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