Defining Patient Engagement in Research: Results of a Systematic Review and Analysis: Report of the ISPOR Patient-Centered Special Interest Group

Content analysis is a widely used qualitative research technique. Rather than being a single method, current applications of content analysis show three distinct approaches: conventional, directed, or summative. All three approaches are used to interpret meaning from the content of text data and, hence, adhere to the naturalistic paradigm. The major differences among the approaches are coding schemes, origins of codes, and threats to trustworthiness. In conventional content analysis, coding categories are derived directly from the text data. With a directed approach, analysis starts with a theory or relevant research findings as guidance for initial codes. A summative content analysis involves counting and comparisons, usually of keywords or content, followed by the interpretation of the underlying context. The authors delineate analytic procedures specific to each approach and techniques addressing trustworthiness with hypothetical examples drawn from the area of end-of-life care.

To provide practical strategies for conducting and evaluating analyses of qualitative data applicable for health services researchers. DATA SOURCES AND DESIGN: We draw on extant qualitative methodological literature to describe practical approaches to qualitative data analysis. Approaches to data analysis vary by discipline and analytic tradition; however, we focus on qualitative data analysis that has as a goal the generation of taxonomy, themes, and theory germane to health services research. We describe an approach to qualitative data analysis that applies the principles of inductive reasoning while also employing predetermined code types to guide data analysis and interpretation. These code types (conceptual, relationship, perspective, participant characteristics, and setting codes) define a structure that is appropriate for generation of taxonomy, themes, and theory. Conceptual codes and subcodes facilitate the development of taxonomies. Relationship and perspective codes facilitate the development of themes and theory. Intersectional analyses with data coded for participant characteristics and setting codes can facilitate comparative analyses. Qualitative inquiry can improve the description and explanation of complex, real-world phenomena pertinent to health services research. Greater understanding of the processes of qualitative data analysis can be helpful for health services researchers as they use these methods themselves or collaborate with qualitative researchers from a wide range of disciplines.

Background A compelling ethical rationale supports patient engagement in healthcare research. It is also assumed that patient engagement will lead to research findings that are more pertinent to patients’ concerns and dilemmas. However; it is unclear how to best conduct this process. In this systematic review we aimed to answer 4 key questions: what are the best ways to identify patient representatives? How to engage them in designing and conducting research? What are the observed benefits of patient engagement? What are the harms and barriers of patient engagement? Methods We searched MEDLINE, EMBASE, PsycInfo, Cochrane, EBSCO, CINAHL, SCOPUS, Web of Science, Business Search Premier, Academic Search Premier and Google Scholar. Included studies were published in English, of any size or design that described engaging patients or their surrogates in research design. We conducted an environmental scan of the grey literature and consulted with experts and patients. Data were analyzed using a non-quantitative, meta-narrative approach. Results We included 142 studies that described a spectrum of engagement. In general, engagement was feasible in most settings and most commonly done in the beginning of research (agenda setting and protocol development) and less commonly during the execution and translation of research. We found no comparative analytic studies to recommend a particular method. Patient engagement increased study enrollment rates and aided researchers in securing funding, designing study protocols and choosing relevant outcomes. The most commonly cited challenges were related to logistics (extra time and funding needed for engagement) and to an overarching worry of a tokenistic engagement. Conclusions Patient engagement in healthcare research is likely feasible in many settings. However, this engagement comes at a cost and can become tokenistic. Research dedicated to identifying the best methods to achieve engagement is lacking and clearly needed.