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

Thursday, 2 August 2012: 1:15 PM

Martha Sylvia, PhD, MBA, RN
Mary Terhaar, DNSc, RN
Department of Health Systems and Outcomes, Johns Hopkins University School of Nursing, Baltimore, MD

Learning Objective 1: Describe effective approaches for managing and analyzing data for evidence-based practice projects.

Learning Objective 2: Improve the quality of clinical data management for evidence-based practice projects.

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.