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Biomedical subjects

Patricia A Abbott

Publications and source records attributed to Patricia A Abbott.

6 recordsLinked to original sources
PubMed2006

e-Learning in nursing education--Challenges and opportunities.

Quick changes on the field of informational communication technologies forces educational and other institutions to think about different ways of teaching and learning in both formal and informal environments. It addition it is well known that due to fast advancement of science and technology the knowledge gained in schools is getting out-of-date rapidly, so life long learning is becoming an essential alternative. As a consequence we are facing a rapid development and use of new educational approaches such as e-learning, simulations, virtual reality, etc. They brought a revolution to learning and instruction. But in general the empirical results of e-learning studies are somewhat disappointing. They cannot prove the superiority of e-learning processes over traditional learning in general, neither in specific areas like nursing. In our international study we proved that e-Learning can have many benefits and that it can enhance learning experience in nursing education, but it has to be provided in correct manner.

Education, Distance↗
PubMed2005

Promoting the usability of online AMIA Symposium Proceedings.

A semi-automatic procedure that extracts metadata from MEDLINE was used to develop a search tool that facilitates online location and (free) access to full-text electronic documents from the Proceedings of the American Medical Informatics Association (AMIA) Annual Symposia (1997-2003). Log file analysis for six months showed steady use of the tool, with most queries originating from hosts in the US (60%), Canada (15.3%), Argentina (10.2%) and Australia (9.6%) for common informatics topics.

Congresses as Topic↗
PubMed2003

Enabling technologies promise to revitalize the role of nursing in an era of patient safety.

The application of information technology (IT) in health care has the potential to transform the delivery of care, as well as the health care work environment, by streamlining processes, making procedures more accurate and efficient, and reducing the risk of human error. For nurses, a major aspect of this transformation is the refocusing of their work on direct patient care and away from being a conduit of information and communication among departments. Several of the technologies discussed, such as physician order entry and bar code technology, have existed for years as standalone systems. Many others are just being developed and are being integrated into complex clinical information systems (CISs) with clinical decision support at their core. While early evaluation of these systems shows positive outcome measurements, financial, technical, and organizational hurdles to widespread implementation still remain. One major issue is defining the role nurses, themselves, will play in the selection and implementation of these systems as they become more steeped in the knowledge of nursing informatics. Other challenges revolve around issues of job satisfaction and the attraction and retention of nursing staff in the midst of a serious nursing shortage. Despite these concerns, it is expected that, in the long run, the creation of an electronic work environment with systems that integrate all functions of the health care team will positively impact cost-effectiveness, productivity, and patient safety while helping to revitalize nursing practice.

Communication↗
PubMedDate not supplied

Bayesian networks for knowledge discovery in large datasets: basics for nurse researchers.

The growth of nursing databases necessitates new approaches to data analyses. These databases, which are known to be massive and multidimensional, easily exceed the capabilities of both human cognition and traditional analytical approaches. One innovative approach, knowledge discovery in large databases (KDD), allows investigators to analyze very large data sets more comprehensively in an automatic or a semi-automatic manner. Among KDD techniques, Bayesian networks, a state-of-the art representation of probabilistic knowledge by a graphical diagram, has emerged in recent years as essential for pattern recognition and classification in the healthcare field. Unlike some data mining techniques, Bayesian networks allow investigators to combine domain knowledge with statistical data, enabling nurse researchers to incorporate clinical and theoretical knowledge into the process of knowledge discovery in large datasets. This tailored discussion presents the basic concepts of Bayesian networks and their use as knowledge discovery tools for nurse researchers.

Artificial Intelligence↗