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Description
Research indicates that over 7.7% of adults who seek medical care every year at a hospital report a delay in receiving care, having difficulty receiving care, or being unable to receive care due to long waiting times (Kennedy et al. 2004). This continue to stir the need for researchers to explore ways to extend healthcare services in minimal waiting times. This thesis research utilizes Arena, a discrete event simulation software, to analyze waiting times in a typical hospital setting. It goes on to explore the impact of cross training of hospital personnel in meeting the critical needs of patients while minimizing waiting times. Simulation output data were analyzed, and cross training was found to have significant impact on reducing waiting time when: intake of patients is higher than current (original) arrival rate, intake of appointment patients is highest, or intake of emergency patience is highest of the three patient categories.
ContributorsBusisi, Jeanbat (Author) / Theodore, Pavlic (Thesis director) / Feng, Ju (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Rapid advancements in Artificial Intelligence (AI), Machine Learning, and Deep Learning technologies are widening the playing field for automated decision assistants in healthcare. The field of radiology offers a unique platform for this technology due to its repetitive work structure, ability to leverage large data sets, and high position for clinical and social impact. Several technologies in cancer screening, such as Computer Aided Detection (CAD), have broken the barrier of research into reality through successful outcomes with patient data (Morton, Whaley, Brandt, & Amrami, 2006; Patel et al, 2018). Technologies, such as the IBM Medical Sieve, are growing excitement with the potential for increased impact through the addition of medical record information ("Medical Sieve Radiology Grand Challenge", 2018). As the capabilities of automation increase and become a part of expert-decision-making jobs, however, the careful consideration of its integration into human systems is often overlooked. This paper aims to identify how healthcare professionals and system engineers implementing and interacting with automated decision-making aids in Radiology should take bureaucratic, legal, professional, and political accountability concerns into consideration. This Accountability Framework is modeled after Romzek and Dubnick’s (1987) public administration framework and expanded on through an analysis of literature on accountability definitions and examples in military, healthcare, and research sectors. A cohesive understanding of this framework and the human concerns it raises helps drive the questions that, if fully addressed, create the potential for a successful integration and adoption of AI in radiology and ultimately the care environment.
ContributorsGilmore, Emily Anne (Author) / Chiou, Erin (Thesis director) / Wu, Teresa (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05