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.

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Magnitude differences in scores on a measure of quality of life that correspond to differences in function or clinical course are called clinically important differences (CIDs). Anchor-based and distribution-based methods were used to provide ranges of CIDs for five targeted scale scores of the Functional Assessment of Cancer Therapy-Anemia (FACT-An) questionnaire. Three samples of cancer patients were used: Sample 1 included 50 patients participating in a validation study of the FACT-An; Sample 2 included 131 patients participating in a longitudinal study of chemotherapy-induced fatigue; sample 3 included 2,402 patients enrolled in a community-based clinical trial evaluating the effectiveness and safety of a treatment for anemia. Three clinical indicators (hemoglobin level; performance status; response to treatment) were used to determine anchor-based differences. One-half of the standard deviation and 1 standard error of measurement were used as distribution-based criteria. Analyses supported the following whole number estimates of a minimal CID for these five targeted scores: Fatigue Scale = 3.0; FACT-G total score = 4.0; FACT-An total score = 7.0; Trial Outcome Index-Fatigue = 5.0; and Trial Outcome Index-Anemia = 6.0. These estimates provide a basis for sample size estimation when planning for a clinical trial or other longitudinal study, when the purpose is to ensure detection of meaningful change over time. They can also be used in conjunction with more traditional clinical markers to assist investigators in determining treatment efficacy.

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.

A high degree of variability in the definition of biochemical recurrence exists following treatment for localized prostate cancer. Strict definitions for biochemical recurrence are necessary to identify men at risk for disease progression and to allow meaningful comparisons among patients treated similarly. The Panel acknowledges the American Society for Therapeutic Radiology and Oncology criteria and future modifications thereof for those receiving radiation therapy and recommends the newly developed American Urological Association criteria for those treated with radical prostatectomy. The purpose for the establishment of this standard is for data reporting purposes and for comparison of similarly treated patients. It is not intended to represent a threshold value for which to initiate treatment. The Panel acknowledges that the clinical decision to initiate treatment will be dependent on multiple factors including patient and physician interaction rather than a specific prostate specific antigen threshold value.

Objective We combined anchor- and distribution-based methods to establish minimally important differences (MIDs) for six PROMIS-Cancer scales in advanced-stage cancer patients. Study Design and Setting Participants completed six PROMIS-Cancer scales and 23 anchor measures at an initial (n=101) and follow-up (n=88) assessment 6 to 12 weeks later. Three a priori criteria were used to identify useable cross-sectional and longitudinal anchor-based MID estimates. The mean standard error of measurement was also computed for each scale. The focus of the analysis was on IRT-based MIDs estimated on a T-score scale. Raw score MIDs were estimated for comparison purposes. Results Many cross-sectional (64%) and longitudinal (73%) T-score anchor-based MID estimates were excluded because they did not meet a priori criteria. The following are recommended T-score MID ranges: 17-item Fatigue (2.5–4.5), 7-item Fatigue (3.0–5.0), 10-item Pain Interference (4.0–6.0), 10-item Physical Functioning (4.0–6.0), 9-item Emotional Distress-Anxiety (3.0–4.5), and 10-item Emotional Distress-Depression (3.0–4.5). Effect sizes corresponding to these MIDs averaged between 0.40 and 0.63. Conclusions This study is the first to address MIDs for PROMIS measures. Studies are currently being conducted to confirm these MIDs in other patient populations and to determine whether these MIDs vary by patients’ level of functioning.

Background:Burden of treatment refers to the workload of health care as well as its impact on patient functioning and well-being. We set out to build a conceptual framework of issues descriptive of burden of treatment from the perspective of the complex patient, as a first step in the development of a new patient-reported measure.Methods:We conducted semistructured interviews with patients seeking medication therapy management services at a large, academic medical center. All patients had a complex regimen of self-care (including polypharmacy), and were coping with one or more chronic health conditions. We used framework analysis to identify and code themes and subthemes. A conceptual framework of burden of treatment was outlined from emergent themes and subthemes.Results:Thirty-two patients (20 female, 12 male, age 26–85 years) were interviewed. Three broad themes of burden of treatment emerged including: the work patients must do to care for their health; problem-focused strategies and tools to facilitate the work of self-care; and factors that exacerbate the burden felt. The latter theme encompasses six subthemes including challenges with taking medication, emotional problems with others, role and activity limitations, financial challenges, confusion about medical information, and health care delivery obstacles.Conclusion:We identified several key domains and issues of burden of treatment amenable to future measurement and organized them into a conceptual framework. Further development work on this conceptual framework will inform the derivation of a patient-reported measure of burden of treatment.

Health-related quality of life (HRQOL) is an important endpoint in cancer clinical trials and in cancer treatment in general; however, the meaningfulness of HRQOL scores may not be apparent to clinicians or researchers. Minimally important differences (MIDs) can enhance the interpretability of HRQOL scores by identifying differences likely to be meaningful to patients and clinicians. This article's objective is to describe and provide examples of approaches we have used to identify MIDs for instruments in the Functional Assessment of Chronic Illness Therapy (FACIT) measurement system. Distribution- and anchor-based approaches are described and illustrated. We also discuss the importance of assessing the appropriateness of anchors, and we provide suggestions for combining results into a single range of plausible MIDs. MIDs for FACIT instruments established to date are summarized, and general guidelines that can be used to estimate MIDs for other FACIT instruments are provided. Applications of MIDs in research are illustrated.