2.50
Hdl Handle:
http://hdl.handle.net/10755/157237
Type:
Presentation
Title:
Symposium Overview Abstract: Session 1075
Abstract:
Symposium Overview Abstract: Session 1075
Conference Sponsor:Western Institute of Nursing
Conference Year:2009
Author:Effken, Judith A., PhD, RN, FACMI, FAAN
P.I. Institution Name:The University of Arizona, College of Nursing
Title:Professor
Contact Address:13754 N. Keystone Springs Dr, Oro Valley, AZ, 85755, USA
Contact Telephone:520-626-6307
Researchers engaged in nursing outcomes research typically collect a vast amount of data because of prior evidence of the many individual, organizational, and unit factors that affect patient outcomes. For example, the purpose of our DyNADS research project is to develop a simple prototype virtual environment that allows nurse managers to assess, through a dynamic network analysis (DNA) decision support tool (DyNADS), the organizational health of their nursing units and then engage in strategic planning by using automated "what-if" analysis techniques to test, on the virtual nursing units, potential organizational innovations to improve their actual nursing units? safety and quality outcomes. However, identifying the particular data elements that are needed to accurately model nursing units requires first collecting a huge amount of data and then identifying the subset of data that most effectively describes the unit and can be reasonably collected by nurse managers using the future DyNADS decision support tool. In this symposium, we describe three approaches we are currently using to identify a streamlined set of data that nurse managers either are already collecting or can easily collect and input directly into DyNADS so that the tool fits easily into their workflow without undue additional data collection. The first presentation (Verran and Hsu) explores the relationship of response rate to unit variables, such as culture, and to patient outcomes, such as symptom management, and suggests that response rate might be a useful proxy for some of these measures. The second presentation (Verran and Lim) describes a novel, multi-step process for data reduction of both scales and items and applies the process to the Control Over Nursing Practice scale. The final presentation (Effken and Logue) applies our use of a cognitive work analysis methodology to describe the current work domain of nurse managers. The results are being used to (a) inform our efforts to fit the DyNADS tool into the nurse manager's workflow and (b) to identify data currently available to nurse managers that are the same as or could be used in place of the data we identify as needed for DyNADS so that most data needed by the decision support tool can be obtained electronically and that any additional data collection can be minimized.
Repository Posting Date:
26-Oct-2011
Date of Publication:
17-Oct-2011
Sponsors:
Western Institute of Nursing

Full metadata record

DC FieldValue Language
dc.typePresentationen_GB
dc.titleSymposium Overview Abstract: Session 1075en_GB
dc.identifier.urihttp://hdl.handle.net/10755/157237-
dc.description.abstract<table><tr><td colspan="2" class="item-title">Symposium Overview Abstract: Session 1075</td></tr><tr class="item-sponsor"><td class="label">Conference Sponsor:</td><td class="value">Western Institute of Nursing</td></tr><tr class="item-year"><td class="label">Conference Year:</td><td class="value">2009</td></tr><tr class="item-author"><td class="label">Author:</td><td class="value">Effken, Judith A., PhD, RN, FACMI, FAAN</td></tr><tr class="item-institute"><td class="label">P.I. Institution Name:</td><td class="value">The University of Arizona, College of Nursing</td></tr><tr class="item-author-title"><td class="label">Title:</td><td class="value">Professor</td></tr><tr class="item-address"><td class="label">Contact Address:</td><td class="value">13754 N. Keystone Springs Dr, Oro Valley, AZ, 85755, USA</td></tr><tr class="item-phone"><td class="label">Contact Telephone:</td><td class="value">520-626-6307</td></tr><tr class="item-email"><td class="label">Email:</td><td class="value">jeffken@nursing.arizona.edu, jaeffken@comcast.net</td></tr><tr><td colspan="2" class="item-abstract">Researchers engaged in nursing outcomes research typically collect a vast amount of data because of prior evidence of the many individual, organizational, and unit factors that affect patient outcomes. For example, the purpose of our DyNADS research project is to develop a simple prototype virtual environment that allows nurse managers to assess, through a dynamic network analysis (DNA) decision support tool (DyNADS), the organizational health of their nursing units and then engage in strategic planning by using automated &quot;what-if&quot; analysis techniques to test, on the virtual nursing units, potential organizational innovations to improve their actual nursing units? safety and quality outcomes. However, identifying the particular data elements that are needed to accurately model nursing units requires first collecting a huge amount of data and then identifying the subset of data that most effectively describes the unit and can be reasonably collected by nurse managers using the future DyNADS decision support tool. In this symposium, we describe three approaches we are currently using to identify a streamlined set of data that nurse managers either are already collecting or can easily collect and input directly into DyNADS so that the tool fits easily into their workflow without undue additional data collection. The first presentation (Verran and Hsu) explores the relationship of response rate to unit variables, such as culture, and to patient outcomes, such as symptom management, and suggests that response rate might be a useful proxy for some of these measures. The second presentation (Verran and Lim) describes a novel, multi-step process for data reduction of both scales and items and applies the process to the Control Over Nursing Practice scale. The final presentation (Effken and Logue) applies our use of a cognitive work analysis methodology to describe the current work domain of nurse managers. The results are being used to (a) inform our efforts to fit the DyNADS tool into the nurse manager's workflow and (b) to identify data currently available to nurse managers that are the same as or could be used in place of the data we identify as needed for DyNADS so that most data needed by the decision support tool can be obtained electronically and that any additional data collection can be minimized.</td></tr></table>en_GB
dc.date.available2011-10-26T19:41:24Z-
dc.date.issued2011-10-17en_GB
dc.date.accessioned2011-10-26T19:41:24Z-
dc.description.sponsorshipWestern Institute of Nursingen_GB
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