Translating Evidence into Practice to Improve Outcomes using Intelligent Informatics

2.50
Hdl Handle:
http://hdl.handle.net/10755/161064
Type:
Presentation
Title:
Translating Evidence into Practice to Improve Outcomes using Intelligent Informatics
Abstract:
Translating Evidence into Practice to Improve Outcomes using Intelligent Informatics
Conference Sponsor:Midwest Nursing Research Society
Conference Year:2006
Author:Akre, Mari, PhD, RN
P.I. Institution Name:University of Wisconsin - Milwaukee
Title:Assistant Professor
Contact Address:College of Nursing, Cunningham Hall, Rm 673, Milwaukee, WI, 53201, USA
Contact Telephone:414-229-5469
Co-Authors:Mary Hook, MS, RN; Norma M. Lang, PhD, RN, FAAN; Tae Youn Kim, PhD, RN; Amy Coenen, PhD, RN, FAAN; and Mary Hagle, PhD, RN, AOCN
With the extensive lag time between knowledge discovery and translation into nursing practice, it is imperative to make a change. Additionally, with the advent of computerized health records and decision support, best evidence may be brought directly to practicing nurses. A unique partnership of nurses in academia, service and the informatics industry is addressing this need. The "Knowledge Based Nursing" Initiative (KBNI) substantially improves patient outcomes by identifying and synthesizing best evidence and translating useful knowledge into practice with the use of computerized decision support and clinical information systems. The KBNI defines and measures nurses' contributions to patient outcomes through retrievable clinical data. The KBNI has tested processes for searching, analyzing, and ranking referential, or published research, evidence. Using a conceptual framework for the translation of knowledge into practice by categorizing best evidence according to nursing assessment, diagnosis, and intervention, the synthesized best evidence is embedded in an electronic information system to provide decision support to nurses in practice. The referential and synthesized evidence, as well as the original publications, are retrievable as content. Medication adherence and physical activity were the first phenomenon of concern addressed. The knowledge for nursing assessment, diagnosis and intervention within the phenomenon were coded in standardized terminologies using a combination of SNOMED CT and International Classification of Nursing Practice (ICNP«) to create a clinical data repository. Additionally, outcomes were identified and coded. The data are stored and retrievable. Analyses such as data mining techniques will facilitate the description and evaluation of nursing practice across multiple types of patients, care settings, and caregiver variables. The KBNI process also may contribute to the generation of new knowledge. The collaboration among the KBNI partners is vital to create robust, accelerated processes that move evidence into practice and collect and analyze clinical data to generate new knowledge. [Poster Presentation]
Repository Posting Date:
26-Oct-2011
Date of Publication:
17-Oct-2011
Sponsors:
Midwest Nursing Research Society

Full metadata record

DC FieldValue Language
dc.typePresentationen_GB
dc.titleTranslating Evidence into Practice to Improve Outcomes using Intelligent Informaticsen_GB
dc.identifier.urihttp://hdl.handle.net/10755/161064-
dc.description.abstract<table><tr><td colspan="2" class="item-title">Translating Evidence into Practice to Improve Outcomes using Intelligent Informatics</td></tr><tr class="item-sponsor"><td class="label">Conference Sponsor:</td><td class="value">Midwest Nursing Research Society</td></tr><tr class="item-year"><td class="label">Conference Year:</td><td class="value">2006</td></tr><tr class="item-author"><td class="label">Author:</td><td class="value">Akre, Mari, PhD, RN</td></tr><tr class="item-institute"><td class="label">P.I. Institution Name:</td><td class="value">University of Wisconsin - Milwaukee</td></tr><tr class="item-author-title"><td class="label">Title:</td><td class="value">Assistant Professor</td></tr><tr class="item-address"><td class="label">Contact Address:</td><td class="value">College of Nursing, Cunningham Hall, Rm 673, Milwaukee, WI, 53201, USA</td></tr><tr class="item-phone"><td class="label">Contact Telephone:</td><td class="value">414-229-5469</td></tr><tr class="item-email"><td class="label">Email:</td><td class="value">akre@uwm.edu</td></tr><tr class="item-co-authors"><td class="label">Co-Authors:</td><td class="value">Mary Hook, MS, RN; Norma M. Lang, PhD, RN, FAAN; Tae Youn Kim, PhD, RN; Amy Coenen, PhD, RN, FAAN; and Mary Hagle, PhD, RN, AOCN</td></tr><tr><td colspan="2" class="item-abstract">With the extensive lag time between knowledge discovery and translation into nursing practice, it is imperative to make a change. Additionally, with the advent of computerized health records and decision support, best evidence may be brought directly to practicing nurses. A unique partnership of nurses in academia, service and the informatics industry is addressing this need. The &quot;Knowledge Based Nursing&quot; Initiative (KBNI) substantially improves patient outcomes by identifying and synthesizing best evidence and translating useful knowledge into practice with the use of computerized decision support and clinical information systems. The KBNI defines and measures nurses' contributions to patient outcomes through retrievable clinical data. The KBNI has tested processes for searching, analyzing, and ranking referential, or published research, evidence. Using a conceptual framework for the translation of knowledge into practice by categorizing best evidence according to nursing assessment, diagnosis, and intervention, the synthesized best evidence is embedded in an electronic information system to provide decision support to nurses in practice. The referential and synthesized evidence, as well as the original publications, are retrievable as content. Medication adherence and physical activity were the first phenomenon of concern addressed. The knowledge for nursing assessment, diagnosis and intervention within the phenomenon were coded in standardized terminologies using a combination of SNOMED CT and International Classification of Nursing Practice (ICNP&laquo;) to create a clinical data repository. Additionally, outcomes were identified and coded. The data are stored and retrievable. Analyses such as data mining techniques will facilitate the description and evaluation of nursing practice across multiple types of patients, care settings, and caregiver variables. The KBNI process also may contribute to the generation of new knowledge. The collaboration among the KBNI partners is vital to create robust, accelerated processes that move evidence into practice and collect and analyze clinical data to generate new knowledge. [Poster Presentation]</td></tr></table>en_GB
dc.date.available2011-10-26T23:15:19Z-
dc.date.issued2011-10-17en_GB
dc.date.accessioned2011-10-26T23:15:19Z-
dc.description.sponsorshipMidwest Nursing Research Societyen_GB
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