MobiMed: Framework for Rapid Application Development of Medical Mobile Apps

In the medical field images obtained from high definition cameras and other medical imaging systems are an integral part of medical diagnosis. The analysis of these images are usually performed by the physicians who sometimes need to spend long hours reviewing the images before they are able to come up with a diagnosis and then decide on the course of action. In this dissertation we present a framework for a computer-aided analysis of medical imagery via the use of an expert system. While this problem has been discussed before, we will consider a system based on mobile devices. Since the release of the iPhone on April 2003, the popularity of mobile devices has increased rapidly and our lives have become more reliant on them. This popularity and the ease of development of mobile applications has now made it possible to perform on these devices many of the image analyses that previously required a personal computer. All of this has opened the door to a whole new set of possibilities and freed the physicians from their reliance on their desktop machines. The approach proposed in this dissertation aims to capitalize on these new found opportunities by providing a framework for analysis of medical images that physicians can utilize from their mobile devices thus remove their reliance on desktop computers. We also provide an expert system to aid in the analysis and advice on the selection of medical procedure. Finally, we also allow for other mobile applications to be developed by providing a generic mobile application development framework that allows for access of other applications into the mobile domain. In this dissertation we outline our work leading towards development of the proposed methodology and the remaining work needed to find a solution to the problem. In order to make this difficult problem tractable, we divide the problem into three parts: the development user interface modeling language and tooling, the creation of a game development modeling language and tooling, and the development of a generic mobile application framework. In order to make this problem more manageable, we will narrow down the initial scope to the hair transplant, and glaucoma domains.

Investigating a career choice for nursing based on social cognitive career theory

Nursing shortages still exist in the U.S. so it is important to determine factors that influence decisions to pursue nursing as a career. This comparative, correlational research study revealed factors that may contribute to, or deter students from choosing nursing as a career. The purpose of this study was to determine factors that contribute to a career choice for nursing based on the concepts of social cognitive career theory (SCCT), self efficacy, outcome expectations, and personal goals, among senior high school students, final year nursing students, and first year nursing students. Based on the results strategies may be developed to recruit a younger pool of students to the nursing profession and to boost retention efforts among those who already made a career choice in nursing. Data were collected using a three part questionnaire developed by the researcher to obtain demographic information and data about the respondents' self efficacy, outcome expectations, and personal goals with regards to nursing as a career. Point bi-serial correlations were used to determine relationships between the variables. ANOVAs and ANCOVAs were computed to determine differences in self efficacy and outcome expectations, among the three groups. Additional descriptive data determined reasons for and against a choice for nursing as a career. Self efficacy and outcome expectations were significantly correlated to career choice among all three groups. The nursing students had higher self efficacy perceptions than the high school students. There were no significant differences in outcome expectations between the three groups. The main categories identified as reasons for choosing nursing as a career were; (a) caring, (b) career and educational advancement, (c) personal accomplishment, (d) proficiency and love of the medical field. Common categories identified for not choosing nursing as a career were; (a) responsibility, (b) liability, (c) lack of respect, and (d) low salary. Other categories regarding not choosing nursing as a career included; (a) the nursing program and (b) professional (c) alternate career choice options and (d) fear of sickness and death. Findings from this study support the tenets of SCCT and may be used to recruit and retain nurses and develop curricula.

Predictors of success: Medical Laboratory Associate in Science Degree Program

Hospitals and healthcare facilities in the United States are facing serious shortages of medical laboratory personnel, which, if not addressed, stand to negatively impact patient care. The problem is compounded by a reduction in the numbers of academic programs and resulting decrease in the number of graduates to keep up with the increase in industry demands. Given these challenges, the purpose of this study was to identify predictors of success for students in a selected 2-year Medical Laboratory Technology Associate in Science Degree Program. ^ This study examined five academic factors (College Placement Test Math and Reading scores, Cumulative GPA, Science GPA, and Professional [first semester laboratory courses] GPA) and, demographic data to see if any of these factors could predict program completion. The researcher examined academic records for a 10-year period (N =158). Using a retrospective model, the correlational analysis between the variables and completion revealed a significant relationship (p < .05) for CGPA, SGPA, CPT Math, and PGPA indicating that students with higher CGPA, SGPA, CPT Math, and PGPA were more likely to complete their degree in 2 years. Binary logistic regression analysis with the same academic variables revealed PGPA was the best predictor of program completion (p < .001). ^ Additionally, the findings in this study are consistent with the academic part of the Bean and Metzner Conceptual Model of Nontraditional Student Attrition which points to academic outcome variables such as GPA as affecting attrition. Thus, the findings in this study are important to students and educators in the field of Medical Laboratory Technology since PGPA is a predictor that can be used to provide early in-program intervention to the at-risk student, thus increasing the chances of successful timely completion.^