BackgroundA 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?MethodsWe 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.ResultsWe 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.ConclusionsPatient 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.

BackgroundIn this article we outline Burden of Treatment Theory, a new model of the relationship between sick people, their social networks, and healthcare services. Health services face the challenge of growing populations with long-term and life-limiting conditions, they have responded to this by delegating to sick people and their networks routine work aimed at managing symptoms, and at retarding – and sometimes preventing – disease progression. This is the new proactive work of patient-hood for which patients are increasingly accountable: founded on ideas about self-care, self-empowerment, and self-actualization, and on new technologies and treatment modalities which can be shifted from the clinic into the community. These place new demands on sick people, which they may experience as burdens of treatment.DiscussionAs the burdens accumulate some patients are overwhelmed, and the consequences are likely to be poor healthcare outcomes for individual patients, increasing strain on caregivers, and rising demand and costs of healthcare services. In the face of these challenges we need to better understand the resources that patients draw upon as they respond to the demands of both burdens of illness and burdens of treatment, and the ways that resources interact with healthcare utilization.SummaryBurden of Treatment Theory is oriented to understanding how capacity for action interacts with the work that stems from healthcare. Burden of Treatment Theory is a structural model that focuses on the work that patients and their networks do. It thus helps us understand variations in healthcare utilization and adherence in different healthcare settings and clinical contexts.

With its components largely supported by existing literature, the model has implications for analytic design, clinical epidemiology, and clinical practice.

Background There is growing attention towards increasing patient and service user engagement (PSUE) in biomedical and health services research. Existing variations in language and design inhibit reporting and indexing, which are crucial to comparative effectiveness in determining best practices.