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Patricia Abbott

Publications and source records attributed to Patricia Abbott.

2 recordsLinked to original sources

Collaborating for a cause--Creating partnerships between IT and academia.

An international nursing shortage is driving the redesign of nursing education curriculum. Providing nursing students with an opportunity for hands on use of advanced healthcare technology is critical to the development of highly competent nurses who are prepared not only to fully interact in the healthcare domain, but to also participate in its re-engineering via IT. The academic institution with an integrated IT solution in clinical simulation labs can become a place where students can learn the skills, understand the potential, participate in the selection and evaluation of software systems and eventually help to craft IT solutions that benefit both the patient and the practice of nursing and medicine. As the healthcare environment continues to change - pushing curricular redesign, the expectation that educators will increase the integration of IT into coursework and clinical experiences is increasing. The challenge however, is not only to educate the students, but to also educate faculty who often possess limited amounts of IT knowledge. Faculties, like students, have limited time and learning outcomes are enhanced when hands-on and practical educational opportunities are available. This presentation therefore will not only focus on the student; instead, the gestalt of integrated teaching/learning from faculty and student perspectives will be addressed. The challenges, including identifying partnership opportunities, identifying financial resources to support such partnerships, gaining institutional support, determining criteria to evaluate student performance, and strategies for engendering faculty support will be discussed, along with solutions on how they can be overcome. The presentation will also highlight the benefits of the partnership for both the institution and the industry vendor. The institution can benefit from the access to the vendor's software, hardware, and expertise. By using advanced clinical solutions in the classroom and hospital setting, students and faculty can benefit from access to evidence based content and knowledge management. Opportunities for faculty and student research also become possible. In turn, the vendor has access to the institution's intellectual capital, participation in research studies co-authorship in academic publications, and enhancements of product design. The outcome is a nursing informatics educational partnership empowering nurses to utilize and drive the enhancements of advanced technology to improve the delivery of patient care.

Cooperative Behavior↗

Logistic regression and Bayesian networks to study outcomes using large data sets.

BACKGROUND: In nursing research, the interest in using large health care databases to predict nursing sensitive outcomes is growing rapidly. Traditionally, one of the most frequently used methods is logistic regression (LR), which, although powerful and familiar, has several limitations when used in the analysis of large databases. As a result, innovative approaches are required. APPROACH: To (a) introduce an innovative/alternative data analysis approach (Bayesian network), (b) discuss the constraints of LR and the complementary advantages of Bayesian networks (BNs) in working with large and multidimensional health care data, and (c) provide a fundamental understanding of the use of BNs in the nursing/health care domain. RESULTS: Studies have shown that BNs have several advantages over LR in analyzing complex and large data: (a) statistical assumptions, such as linearity and additivity, are relaxed; (b) handling of a larger number of predictors and identification of interactions among predictors is less complex; and (c) the discovery of structure, pattern, and knowledge, for example, of unknown, complex, and nonlinear relationships, in data is facilitated. CONCLUSION: Outcome studies, such as those undertaken by nurse researchers, may benefit from the examination and use of innovative approaches such as BNs to the analysis of very large and complex health care data sets.

Bayes Theorem↗