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- All Subjects: healthcare
- All Subjects: Machine Learning
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Member of: Theses and Dissertations
Description
The purpose of this study was to create a screening tool specifically for the identification of sex trafficking victims in the medical setting through the analysis of existing human trafficking screening tool studies geared towards use in the medical setting. Screening questions from these studies were compiled and modified into a survey that was distributed to healthcare professionals through the nationwide HEAL (Health Professional Education, Advocacy, Linkage) Trafficking listserv. Each screening tool study demonstrated benefits and disadvantages that were helpful in the sampling and selection of screening tool questions. The small sample size and a lack of data on the attitudes of medical professionals on sex trafficked victims were noted as limitations to this study. Further implications for this study would include validating the screening tool questions in a medical setting to determine the sensitivity of the survey in identifying patients as possible sex trafficking victims.
ContributorsCatano, Karen Samantha (Co-author) / Byun, Jiwon (Co-author) / Roe-Sepowitz, Dominique (Thesis director) / Lee, Maurice (Committee member) / School for the Science of Health Care Delivery (Contributor) / College of Integrative Sciences and Arts (Contributor) / W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft. Goals in these environments have recursive sub-goal dependencies which form a dependency tree. An agent operating within these environments have access to low amounts of data about the environment before interacting with it, so it is crucial that this agent is able to effectively utilize a tree of dependencies and its environmental surroundings to make judgements about which sub-goals are most efficient to pursue at any point in time. A successful agent aims to minimizes cost when completing a given goal. A deep neural network in combination with Q-learning techniques was employed to act as the agent in this environment. This agent consistently performed better than agents using alternate models (models that used dependency tree heuristics or human-like approaches to make sub-goal oriented choices), with an average performance advantage of 33.86% (with a standard deviation of 14.69%) over the best alternate agent. This shows that machine learning techniques can be consistently employed to make goal-oriented choices within an environment with recursive sub-goal dependencies and low amounts of pre-known information.
ContributorsKoleber, Derek (Author) / Acuna, Ruben (Thesis director) / Bansal, Ajay (Committee member) / W.P. Carey School of Business (Contributor) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The primary purpose of this paper is to analyze urgent care centers and explain their role within the U.S. healthcare system. The introduction of urgent care into the market for health care services has brought with it a new way for consumers to receive non-emergent healthcare outside of traditional hours. Urgent care is often cited as a plausible alternative to care received at an emergency department or primary care physician's office. One of the key questions the author attempts to answer is: "To what degree are urgent care centers an economic substitute to emergency departments or physician's offices?" This paper looks at both projected demand from currently operating urgent care centers and consumer preference surveys to estimate the willingness of consumers to use urgent care. The method used to accomplish this task has been compiling scholarly research and data on urgent care centers. After a thorough examination of relevant studies and datasets, urgent care centers have been found to be just as preferred as emergency departments when considering non-emergent cases, specifically among individuals aged 18-44. The clear majority of consumers still prefer visiting a primary care physician over an urgent care center when it comes to episodic care, however. When taking into account wait times, differences in cost, and ease of access, urgent care becomes much more preferred than an emergency department and weakly preferred to a physician's office. There are still some concerns with urgent care, however. Questions of capacity to meet demand, access for underserved communities, and susceptibility to adverse selection have yet to be fully explored.
ContributorsBullington, Robert Heyburn (Author) / Foster, William (Thesis director) / Hill, John (Committee member) / Sandra Day O'Connor College of Law (Contributor) / W.P. Carey School of Business (Contributor) / Department of Economics (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The purpose of this project is to develop a risk assessment tool for the University of California, Riverside (UCR). UCR is health enterprise that manages operations under an environment of innate and uncontrollable risks. Therefore, a risk assessment tool is highly advisable under California State Laws and federal laws. In the case of overlapping laws, federal law will always prevail unless State law explicitly states otherwise. California Health Information Privacy Manual states that California must follow numerous state guidelines and a federal set of guidelines called HIPAA (Health Insurance Portability and Accountability Act of 1996). HIPAA is put in place to protect and serve as an organizational tool to develop a stronger and more secure infrastructure of security measures within healthcare enterprises. Under HIPAA is a Security and Privacy Rule that was implemented by The U.S. Department of Health and Human Services (HHS) and will serve as the basis for the risk assessment tool I developed. The Security and Privacy Rule's main goal is to set a national standard of how electronic protected health information (ePHI) will be appropriately used and disclosed by organizations subject to this rule, also known covered entities. Covered entities include health plans, health care providers and health care clearinghouses unless specifically stated otherwise. Permitted uses and disclosures of PHI or ePHI are outlined in detail and covered entities are expected to follow all aspects of it that pertain to their role within a healthcare system. Under HHS, the Office of Civil Rights (OCR) strictly enforces the Security and Privacy Rules and can issue civil money penalties and/or other major consequences making this a sizable and critical issue in healthcare environments. Each risk and impact must be assessed to determine an overall risk score. This score will then determine what risks need to be immediately addressed and which risks are most critical to UCR. To do this, potential impacts were determined for each section. The impact score can be decided by using a chart that will be discussed in the development section. The likeliness of the risk can be determined by a UCR professional via the provided chart and an overall risk score can be assigned. From here, an action plan can be set and carried out to eliminate possible hazards and imminent risks. Once a Risk Assessment tool is developed, potential risks can be indentified and dealt with appropriately in regard to level of impact and the likelihood of the risk occurring. By reducing risk, a healthcare enterprise can gain greater financial stability, decrease loss and protect vital information that is critical to the success organization.
ContributorsAustin, Hannah N. (Author) / Riley, William (Thesis director) / Hackman, Paul (Committee member) / School of Molecular Sciences (Contributor) / W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Abstract: The Ultimate Fighting Championship or UFC as it is commonly known, was founded in 1993 and has quickly built itself into the world's foremost authority on all things MMA (mixed martial arts) related. With pay-per-view and cable television deals in hand, the UFC has become a huge competitor in the sports market, rivaling the popularity of boxing for almost a decade. As with most other sports, the UFC has seen an influx of various analytics and data science over the past five to seven years. We see this revolution in football with the broadcast first down markers, basketball with tracking player movement, and baseball with locating pitches for strikes and balls, and now the UFC has partnered with statistics company Fightmetric, to provide in-depth statistical analysis of its fights. ESPN has their win probability metrics, and statistical predictive modeling has begun to spread throughout sports. All these stats were made to showcase the information about a fighter that one wouldn't typically know, giving insight into how the fight might go. But, can these fights be predicted? Based off of the research of prior individuals and combining the thought processes of relevant research into other sports leagues, I sought to use the arsenal of statistical analyses done by Fightmetric, along with the official UFC fighter database to answer the question of whether UFC fights could be predicted. Specifically, by using only data that would be known about a fighter prior to stepping into the cage, could I predict with any degree of certainty who was going to win the fight?
ContributorsMoorman, Taylor D. (Author) / Simon, Alan (Thesis director) / Simon, Phil (Committee member) / W.P. Carey School of Business (Contributor) / Department of Information Systems (Contributor) / Department of Management and Entrepreneurship (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05