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Automated nursing knowledge classification using indexing
Title
Automated nursing knowledge classification using indexing.
Creator
Chinchanikar, Sucharita Vijay., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Promoting healthcare and wellbeing requires the dedication of a multi-tiered health service delivery system, which is comprised of specialists, medical doctors and nurses. A holistic view to a patient care perspective involves emotional, mental and physical healthcare needs, in which caring is understood as the essence of nursing. Properly and efficiently capturing and managing nursing knowledge is essential to advocating health promotion and illness prevention. This thesis proposes a...
Show morePromoting healthcare and wellbeing requires the dedication of a multi-tiered health service delivery system, which is comprised of specialists, medical doctors and nurses. A holistic view to a patient care perspective involves emotional, mental and physical healthcare needs, in which caring is understood as the essence of nursing. Properly and efficiently capturing and managing nursing knowledge is essential to advocating health promotion and illness prevention. This thesis proposes a document-indexing framework for automating classification of nursing knowledge based on nursing theory and practice model. The documents defining the numerous categories in nursing care model are structured with the help of expert nurse practitioners and professionals. These documents are indexed and used as a benchmark for the process of automatic mapping of each expression in the assessment form of a patient to the corresponding category in the nursing theory model. As an illustration of the proposed methodology, a prototype application is developed using the Latent Semantic Indexing (LSI) technique. The prototype application is tested in a nursing practice environment to validate the accuracy of the proposed algorithm. The simulation results are also compared with an application using Lucene indexing technique that internally uses modified vector space model for indexing. The result comparison showed that the LSI strategy gives 87.5% accurate results compared to the Lucene indexing technique that gives 80% accuracy. Both indexing methods maintain 100% consistency in the results.
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Date Issued
2009
PURL
http://purl.flvc.org/FAU/186677
Subject Headings
Nursing, Computer-assisted instruction, Data transmission systems, Outcome assessment (Medical care), Nursing assessment, Digital techniques
Format
Document (PDF)
Knowledge based evaluation of nursing care practice model
Title
Knowledge based evaluation of nursing care practice model.
Creator
Tripathi, Shubhang., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Provision of complete and responsive solution to healthcare services requires a multi-tired health delivery system. One of the aspects of healthcare hierarchy is the need for nursing care of the patient. Nursing care and observation provide basis for nurses to communicate with other aspects of healthcare system. The ability of capturing and managing nursing practice is essential to the quality of human care. The thesis proposes knowledge based decision making and analyzing system for the...
Show moreProvision of complete and responsive solution to healthcare services requires a multi-tired health delivery system. One of the aspects of healthcare hierarchy is the need for nursing care of the patient. Nursing care and observation provide basis for nurses to communicate with other aspects of healthcare system. The ability of capturing and managing nursing practice is essential to the quality of human care. The thesis proposes knowledge based decision making and analyzing system for the nurses to capture and manage the nursing practice. Moreover it allows them to monitor nursing care quality, as well as to test an aspect of an electronic healthcare record for recording and reporting nursing practice. The framework used for this system is based on nursing theory and is coupled with the quantitative analysis of qualitative data. It allows us to quantify the qualitative raw natural nursing language data. The results are summarized in the graph that shows the relative importance of those attributes with respect to each other at different instances of nurse-patient encounter. Research has been conducted by the Department of Computer and Electrical Engineering and Computer Science for the College of Nursing.
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Date Issued
2010
PURL
http://purl.flvc.org/FAU/2683141
Subject Headings
Nursing assessment, Digital techniques, Nursing, Research, Methodology, Nursing, Technological innovations, Nursing, Practice, Nursing informatics
Format
Document (PDF)