gms | German Medical Science

G-I-N Conference 2012

Guidelines International Network

22.08 - 25.08.2012, Berlin

Making sense of complex data: Development of a mapping process to analyse results of a realist review on guideline implementability

Meeting Abstract

  • M. Kastner - St. Michael's Hospital - Li Ka Shing Knowledge Institute, Toronto, Canada
  • J. Makarski - Department of Oncology, Hamilton, ON, Canada
  • L. Hayden - St. Michael's Hospital - Li Ka Shing Knowledge Institute, Toronto, Canada
  • L. Durocher - Department of Oncology, Hamilton, ON, Canada
  • A. Chatterjee - St. Michael's Hospital - Li Ka Shing Knowledge Institute, Toronto, Canada
  • M. Brouwers - Department of Oncology, Hamilton, ON, Canada
  • O. Bhattacharyya - St. Michael's Hospital - Li Ka Shing Knowledge Institute, Toronto, Canada

Guidelines International Network. G-I-N Conference 2012. Berlin, 22.-25.08.2012. Düsseldorf: German Medical Science GMS Publishing House; 2012. DocO18

DOI: 10.3205/12gin050, URN: urn:nbn:de:0183-12gin0507

Published: July 10, 2012

© 2012 Kastner et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.en). You are free: to Share – to copy, distribute and transmit the work, provided the original author and source are credited.


Outline

Text

Background: Existing techniques for organizing and synthesizing disparate information on a complex topic are not well operationalized.

Objectives: To develop an innovative analysis method for organizing complex evidence from realist review findings.

Methods: Development and testing of a mapping process to organize and synthesize findings from a realist review investigating guideline implementability. Two teams of investigators extracted data on guideline attributes, their definition and operationalization, and their relationship with uptake and context. Analysis involved clustering like attributes and classifying them into discrete categories, and creating a conceptual framework of implementability using a web-based mapping tool (i.e., MindMeister). Validity measures included an auditing process whereby primary data extractions were checked against a codebook of definitions, and an expert survey to verify the sense and fit of attributes and categories within the framework.

Results: This unique mapping strategy informed a guideline implementability map through the classification of 1045 attributes from 250 articles into a consensus-based set of categories, which were collapsed into 5 core conceptual domains: language, format, evidence, feasibility, decision-making.

Discussion: Our strategy helped make sense of our complex data on guideline implementability. Our approach can be used to determine the purpose and scope of poorly understood concepts; and to organize, synthesize, validate, and represent results in a relevant and meaningful way.

Implications for guideline developers/users: This study represents a novel contribution to advancing the mapping and synthesis literature by offering a method for analyzing any large and complex data set where the goals are to condense, organize and identify relationships.