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dc.contributor.authorSylvia, Marthaen
dc.contributor.authorTerhaar, Mary F.en
dc.date.accessioned2012-09-12T09:20:11Z
dc.date.available2012-09-12T09:20:11Z
dc.date.createdThursday, August 2, 2012en
dc.date.issued2012-9-12
dc.identifier.urihttp://hdl.handle.net/10755/243289
dc.description.abstract<p>Strong data management skills are essential for effective evaluation of evidence-based practice project implementation. Completion of a scholarly evidence-based project requires application of data management skills in order to understand and address a complex practice, process, or systems problem; develop, implement and monitor an innovative evidence-based intervention to address that problem; and evaluate the outcomes. In fact, EBP projects may require stronger data management skills than those required in traditional research because they often make use of data generated for direct care or administrative purposes that require sophisticated data cleansing and manipulation techniques. These projects commonly use observational study techniques that require complex statistical methods to eliminate the sampling biases that are removed when using controls in randomized clinical trials (Austin, 2011). Scholarly projects of students in the Johns Hopkins University School of Nursing Doctor of Nursing Practice Program use EBP frameworks. In response to students' lack of confidence, knowledge, and skills in data management, we developed a clinical data management (CDM) course focusing on strategies, procedures and knowledge application to promote quality data management for evidence-based projects. The clinical data management process is laid out in 6 phases: data collection, data cleansing, data manipulation, exploratory analysis, outcomes analysis, and reporting and presentation. Some specific components of the process include identification of and linkages between project aims, outcomes, measures, variables, and data sources; creation of data collection systems and processes; measurement of statistical power; use of statistical software; identification and methods of managing sampling bias and confounding; identification and implementation of appropriate statistical testing; and meaningful presentation of results. An example of this process will be reviewed using an evaluation of an evidence-based case management intervention for members of a health plan population who have chronic illness.</p>en
dc.formatText-based Documenten
dc.language.isoenen
dc.subjectClinical Data Managementen
dc.subjectEBP Methodsen
dc.subjectPractice Doctorateen
dc.titleAn approach to data management and evaluation for evidence-based practice projectsen
dc.typePresentationen
dc.rights.holder<p> All rights reserved by the author(s) and/or publisher(s) listed in this item record unless relinquished in whole or part by a rights notation or a Creative Commons License present in this item record. </p><p> All permission requests should be directed accordingly and not to the Sigma Repository. </p><p> All submitting authors or publishers have affirmed that when using material in their work where they do not own copyright, they have obtained permission of the copyright holder prior to submission and the rights holder has been acknowledged as necessary. </p>en
dc.description.note<p>Items submitted to a conference/event were evaluated/peer-reviewed at the time of abstract submission to the event. No other peer-review was provided prior to submission to the Henderson Repository.</p>en
dc.type.categoryFull-texten
dc.evidence.levelN/Aen
dc.research.approachN/Aen
dc.contributor.departmentNu Beta at-Largeen
dc.author.detailsSylvia, Martha, PhD, MBA, RN; Terhaar, Mary, DNSc, RNen
dc.conference.name23rd International Nursing Research Congressen
dc.conference.hostSigma Theta Tau Internationalen
dc.conference.locationBrisbane, Australiaen
dc.date.conferenceyear2012
dc.description.reviewtypeAbstract Review Only: Reviewed by Event Hosten
dc.description.acquisitionProxy-submissionen


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