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Objective. Person tradeoff (PTO) elicitations assess people's values for health states by asking them to compare the value of treatment programs. For example, people might be asked how many patients [...]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.socscimed.2006.08.045 Byline: Laura J. Damschroder (a)(b), Joy L. Pritts (c), Michael A. Neblo (d), Rosemarie J. Kalarickal (b), John W. Creswell (e), Rodney A. Hayward (a) Keywords: USA; Deliberative democracy; Trust; Privacy; Medical records; Health insurance portability and accountability act (hipaa); Health policy Abstract: The federal Privacy Rule, implemented in the United States in 2003, as part of the Health Insurance Portability and Accountability Act of 1996 (HIPAA), created new restrictions on the release of medical information for research. Many believe that its restrictions have fallen disproportionately on researchers prompting some to call for changes to the Rule. Here we ask what patients think about researchers' access to medical records, and what influences these opinions. A sample of 217 patients from 4 Veteran Affairs (VA) facilities deliberated in small groups at each location with the opportunity to question experts and inform themselves about privacy issues related to medical records research. After extensive deliberation, these patients were united in their inclination to share their medical records for research. Yet they were also united in their recommendations to institute procedures that would give them more control over whether and how their medical records are used for research. We integrated qualitative and quantitative results to derive a better understanding of this apparent paradox. Our findings can best be presented as answers to questions related to five dimensions of trust: (1) Are medical records kept confidential? (2) Does the research being conducted demonstrate high priority on patient welfare? (3) Are researchers held accountable and responsible for protecting privacy? (4) Are systems to protect medical records sufficiently secure? (5) Do researchers fully disclose the research being conducted and how medical records are used to conduct that research? Patients' trust in VA researchers was the most powerful determinant of the kind of control they want over their medical records. More specifically, those who had lower trust in VA researchers were more likely to recommend a more stringent process for obtaining individual consent. Insights on the critical role of trust suggest actions that researchers and others can take to more fully engage patients in research. Author Affiliation: (a) Healthcare System, VA Health Services Research & Development Center for Practice Management and Outcomes Research, VA Ann Arbor, USA (b) University of Michigan/Center for Behavioral and Decision Sciences in Medicine, Ann Arbor, MI, USA (c) Georgetown University, USA (d) The Ohio State University, USA (e) University of Nebraska, USA

Patients with chronic health conditions often rate their quality of life (QoL) significantly higher than non-patients. One explanation for this discrepancy is that non-patients focus on the negative aspects of the onset of a condition, especially the early difficulties people face when they first experience a debilitating condition, without considering that patients can adapt to it over time. To test this hypothesis, we had 359 people perform person tradeoff (PTO) elicitations in an online survey, varying whether the treatment programs under consideration saved the lives of patients (a) with pre-existing paraplegia; or (b) who would experience new onset of paraplegia. Half of each group completed an 'adaptation exercise' which encouraged them to consider their own ability to emotionally adapt to negative events in general and specifically to having paraplegia. The adaptation manipulation increased the value participants placed on pre-existing paraplegia (p = 0.03) and on new onset paraplegia (p = 0.05), relative to saving healthy lives. Moreover, the adaptation exercise dramatically reduced the differences between evaluations of pre-existing and new onset paraplegia to values within 2% of each other. Our findings suggest that asking non-patients to do an adaptation exercise before giving QoL ratings may help close the gap in ratings between patients and citizen non-patients. Keywords: Adaptation; Person tradeoff; Health state valuation; Cost-effectiveness analysis; Utility measure; Internet survey

Embedding research and evaluation into organizations is one way to generate "practice‐based" evidence needed to accelerate implementation of evidence‐based innovations within learning health systems. Organizations and researchers/evaluators vary greatly in how they structure and operationalize these collaborations. One key aspect is the degree of embeddedness: from low embeddedness where researchers/evaluators are located outside organizations (eg, outside evaluation consultants) to high embeddedness where researchers/evaluators are employed by organizations and thus more deeply involved in program evolution and operations. Pros and cons related to the degree of embeddedness (low vs high) must be balanced when developing these relationships. We reflect on this process within the context of an embedded, mixed‐methods evaluation of the Veterans Health Administration (VHA) Diffusion of Excellence (DoE) program. Considerations that must be balanced include: (a) low vs high alignment of goals; (b) low vs high involvement in strategic planning; (c) observing what is happening vs being integrally involved with programmatic activities; (d) reporting findings at the project's end vs providing iterative findings and recommendations that contribute to program evolution; and (e) adhering to predetermined aims vs adapting aims in response to evolving partner needs. [ABSTRACT FROM AUTHOR]

Background: The challenges of implementing evidence-based innovations (EBIs) are widely recognized among practitioners and researchers. Context, broadly defined as everything outside the EBI, includes the dynamic and diverse array of forces working for or against implementation efforts. The Consolidated Framework for Implementation Research (CFIR) is one of the most widely used frameworks to guide assessment of contextual determinants of implementation. The original 2009 article invited critique in recognition for the need for the framework to evolve. As implementation science has matured, gaps in the CFIR have been identified and updates are needed. Our team is developing the CFIR 2.0 based on a literature review and follow-up survey with authors. We propose an Outcomes Addendum to the CFIR to address recommendations from these sources to include outcomes in the framework.Main Text: We conducted a literature review and surveyed corresponding authors of included articles to identify recommendations for the CFIR. There were recommendations to add both implementation and innovation outcomes from these sources. Based on these recommendations, we make conceptual distinctions between (1) anticipated implementation outcomes and actual implementation outcomes, (2) implementation outcomes and innovation outcomes, and (3) CFIR-based implementation determinants and innovation determinants.Conclusion: An Outcomes Addendum to the CFIR is proposed. Our goal is to offer clear conceptual distinctions between types of outcomes for use with the CFIR, and perhaps other determinant implementation frameworks as well. These distinctions can help bring clarity as researchers consider which outcomes are most appropriate to evaluate in their research. We hope that sharing this in advance will generate feedback and debate about the merits of our proposed addendum. [ABSTRACT FROM AUTHOR]

Methods for identifying heterogeneity of treatment effects in randomized trials have seen recent advances, yet applying these methods to health services intervention trials has not been well investigated. Our objective was to compare two approaches—predictive risk modeling and model-based recursive partitioning—for identifying subgroups of trial participants with potentially differential response to an intervention involving health risk assessment completion alone (n = 192) versus health risk assessment completion plus telephone-delivered health coaching (n = 173). Notably, these approaches have been developed by investigators from distinct disciplines and reported in separate literatures and have generally not been compared in prior work. Furthermore, these methods approach subgroup identification differently and answer related but slightly different questions. The primary outcome for both approaches was prevention health program enrollment by six months. The predictive risk model was developed in two steps, where, first, a single risk score was derived from a logistic regression model with 12 a priori chosen covariates by the scientific investigator team (c-statistic = 0.63). Then, the treatment effect was calculated within quartiles of risk via interaction in a logistic regression model (c-statistic = 0.69; c-for-benefit = 0.43). The greatest treatment effect was in the second quartile, in which 54% (22 of 41) of intervention patients and 10% (5 of 50) of control patients reported prevention program enrollment. In contrast, with the data-driven approach of model-based recursive partitioning, all 28 baseline covariates were considered, with the algorithm selecting covariates and optimal split points. Final model results had a c-statistic of 0.69 and a c-for-benefit of 0.55 (optimism-corrected c-statistic = 0.62 and c-for-benefit = 0.53) and identified 4 subgroups, with the greatest treatment effect among patients with lower mean numeracy, education less than a bachelor's degree, and diabetes, in which 54% (15 of 28) of intervention patients reported prevention program enrollment versus 7% (3 of 41) of control patients. While there is increasing interest in discovering heterogeneity of treatment effects, our analyses highlight the important differences between these approaches, both from questions answered, model development, and results obtained. Specifying goals of treatment heterogeneity analyses, choosing the appropriate method to best address the goals, and external validation of results are important steps when applying these methods. Clinicaltrials.gov identifier: NCT01828567 [ABSTRACT FROM AUTHOR]

Background: While the Veterans Health Administration (VHA) MOVE! weight management program is effective in helping patients lose weight and is available at every VHA medical center across the United States, reaching patients to engage them in treatment remains a challenge. Facility-based MOVE! programs vary in structures, processes of programming, and levels of reach, with no single factor explaining variation in reach. Configurational analysis, based on Boolean algebra and set theory, represents a mathematical approach to data analysis well-suited for discerning how conditions interact and identifying multiple pathways leading to the same outcome. We applied configurational analysis to identify facility-level obesity treatment program arrangements that directly linked to higher reach.Methods: A national survey was fielded in March 2017 to elicit information about more than 75 different components of obesity treatment programming in all VHA medical centers. This survey data was linked to reach scores available through administrative data. Reach scores were calculated by dividing the total number of Veterans who are candidates for obesity treatment by the number of "new" MOVE! visits in 2017 for each program and then multiplied by 1000. Programs with the top 40 % highest reach scores (n = 51) were compared to those in the lowest 40 % (n = 51). Configurational analysis was applied to identify specific combinations of conditions linked to reach rates.Results: One hundred twenty-seven MOVE! program representatives responded to the survey and had complete reach data. The final solution consisted of 5 distinct pathways comprising combinations of program components related to pharmacotherapy, bariatric surgery, and comprehensive lifestyle intervention; 3 of the 5 pathways depended on the size/complexity of medical center. The 5 pathways explained 78 % (40/51) of the facilities in the higher-reach group with 85 % consistency (40/47).Conclusions: Specific combinations of facility-level conditions identified through configurational analysis uniquely distinguished facilities with higher reach from those with lower reach. Solutions demonstrated the importance of how local context plus specific program components linked together to account for a key implementation outcome. These findings will guide system recommendations about optimal program structures to maximize reach to patients who would benefit from obesity treatment such as the MOVE!Program: [ABSTRACT FROM AUTHOR]

Background: Although risk prediction has become an integral part of clinical practice guidelines for cardiovascular disease (CVD) prevention, multiple studies have shown that patients' risk still plays almost no role in clinical decision-making. Because little is known about why this is so, we sought to understand providers' views on the opportunities, barriers, and facilitators of incorporating risk prediction to guide their use of cardiovascular preventive medicines.Methods: We conducted semi-structured interviews with primary care providers (n = 33) at VA facilities in the Midwest. Facilities were chosen using a maximum variation approach according to their geography, size, proportion of MD to non-MD providers, and percentage of full-time providers. Providers included MD/DO physicians, physician assistants, nurse practitioners, and clinical pharmacists. Providers were asked about their reaction to a hypothetical situation in which the VA would introduce a risk prediction-based approach to CVD treatment. We conducted matrix and content analysis to identify providers' reactions to risk prediction, reasons for their reaction, and exemplar quotes.Results: Most providers were classified as Enthusiastic (n = 14) or Cautious Adopters (n = 15), with only a few Non-Adopters (n = 4). Providers described four key concerns toward adopting risk prediction. Their primary concern was that risk prediction is not always compatible with a "whole patient" approach to patient care. Other concerns included questions about the validity of the proposed risk prediction model, potential workflow burdens, and whether risk prediction adds value to existing clinical practice. Enthusiastic, Cautious, and Non-Adopters all expressed both doubts about and support for risk prediction categorizable in the above four key areas of concern.Conclusions: Providers were generally supportive of adopting risk prediction into CVD prevention, but many had misgivings, which included concerns about impact on workflow, validity of predictive models, the value of making this change, and possible negative effects on providers' ability to address the whole patient. These concerns have likely contributed to the slow introduction of risk prediction into clinical practice. These concerns will need to be addressed for risk prediction, and other approaches relying on "big data" including machine learning and artificial intelligence, to have a meaningful role in clinical practice. [ABSTRACT FROM AUTHOR]