An Approach to Data Management and Evaluation for Evidence-Based Practice Projects

2.50
Hdl Handle:
http://hdl.handle.net/10755/243289
Category:
Full-text
Type:
Presentation
Title:
An Approach to Data Management and Evaluation for Evidence-Based Practice Projects
Author(s):
Sylvia, Martha; Terhaar, Mary
Lead Author STTI Affiliation:
Nu Beta member
Author Details:
Sylvia, Martha, PhD, MBA, RN, msylvia1@son.jhmi.edu; Terhaar, Mary, DNSc, RN;
Abstract:
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.

Keywords:
Clinical Data Management; EBP Methods; Practice Doctorate
Repository Posting Date:
12-Sep-2012
Date of Publication:
12-Sep-2012 ; 12-Sep-2012
Conference Date:
2012
Conference Name:
23rd International Nursing Research Congress
Conference Host:
Sigma Theta Tau International, the Honor Society of Nursing
Conference Location:
Brisbane, Australia

Full metadata record

DC FieldValue Language
dc.language.isoenen
dc.type.categoryFull-texten
dc.typePresentationen
dc.titleAn Approach to Data Management and Evaluation for Evidence-Based Practice Projectsen
dc.contributor.authorSylvia, Marthaen
dc.contributor.authorTerhaar, Maryen
dc.contributor.departmentNu Beta memberen
dc.author.detailsSylvia, Martha, PhD, MBA, RN, msylvia1@son.jhmi.edu; Terhaar, Mary, DNSc, RN;en
dc.identifier.urihttp://hdl.handle.net/10755/243289-
dc.description.abstractStrong data management skills are essential for effective evaluation of evidence-based practice project implementation. &nbsp;&nbsp;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.&nbsp; 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.&nbsp; 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). <p>Scholarly projects of students in the Johns Hopkins University School of Nursing Doctor of Nursing Practice Program use EBP frameworks. &nbsp;In response to students&rsquo; 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.&nbsp; The clinical data management process is laid out in 6 phases:&nbsp; data collection, data cleansing, data manipulation, exploratory analysis, outcomes analysis, and reporting and presentation.&nbsp; 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;&nbsp; measurement of statistical power;&nbsp; use of statistical software;&nbsp; identification and methods of managing sampling bias and confounding; identification and implementation of appropriate statistical testing; and meaningful presentation of results. <p>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.en
dc.subjectClinical Data Managementen
dc.subjectEBP Methodsen
dc.subjectPractice Doctorateen
dc.date.available2012-09-12T09:20:11Z-
dc.date.issued2012-09-12-
dc.date.issued2012-09-12en
dc.date.accessioned2012-09-12T09:20:11Z-
dc.conference.date2012en
dc.conference.name23rd International Nursing Research Congressen
dc.conference.hostSigma Theta Tau International, the Honor Society of Nursingen
dc.conference.locationBrisbane, Australiaen
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