Project description:Poor adherence to asthma controller medications results in poor treatment outcomes.To compare controller medication adherence and clinical outcomes in 612 adults with poorly controlled asthma randomized to one of two different treatment decision-making models or to usual care.In shared decision making (SDM), nonphysician clinicians and patients negotiated a treatment regimen that accommodated patient goals and preferences. In clinician decision making, treatment was prescribed without specifically eliciting patient goals/preferences. The otherwise identical intervention protocols both provided asthma education and involved two in-person and three brief phone encounters.Refill adherence was measured using continuous medication acquisition (CMA) indices-the total days' supply acquired per year divided by 365 days. Cumulative controller medication dose was measured in beclomethasone canister equivalents. In follow-up Year 1, compared with usual care, SDM resulted in: significantly better controller adherence (CMA, 0.67 vs. 0.46; P < 0.0001) and long-acting beta-agonist adherence (CMA, 0.51 vs. 0.40; P = 0.0225); higher cumulative controller medication dose (canister equivalent, 10.9 vs. 5.2; P < 0.0001); significantly better clinical outcomes (asthma-related quality of life, health care use, rescue medication use, asthma control, and lung function). In Year 2, compared with usual care, SDM resulted in significantly lower rescue medication use, the sole clinical outcome available for that year. Compared with clinician decision making, SDM resulted in: significantly better controller adherence (CMA, 0.67 vs. 0.59; P = 0.03) and long-acting beta-agonist adherence (CMA, 0.51 vs. 0.41; P = 0.0143); higher cumulative controller dose (CMA, 10.9 vs. 9.1; P = 0.005); and quantitatively, but not significantly, better outcomes on all clinical measures.Negotiating patients' treatment decisions significantly improves adherence to asthma pharmacotherapy and clinical outcomes. Clinical trials registered with www.clinicaltrials.gov (NCT00217945 and NCT00149526).

Project description:Background:Practice facilitation is a method of introducing and sustaining organizational change. It involves the use of skilled healthcare professionals called practice facilitators (PFs) to help address the challenges associated with implementing evidence-based guidelines and complex interventions into practice. PFs provide a framework for translating research into practice by building relationships, improving communication, fostering change, and sharing resources. Nurses are well positioned to serve as PFs for the implementation of complex interventions, however, there is little evidence currently available to describe nurses in this role. Additionally, the best strategies to implement complex interventions into practices are still not fully understood. Combining practice facilitation with the train-the-trainer model has the potential to spread knowledge and skills. Shared decision making (SDM), which involves patients and providers jointly engaging in decisions around treatment options, has been shown to improve outcomes for patients with asthma. The goal of this manuscript is to describe and evaluate the practice facilitation process from the ADAPT-NC Study which successfully utilized research nurses to implement a complex asthma SDM toolkit intervention into primary care practices. Methods:As part of a larger study, 10 primary care practices were recruited for a facilitator-led dissemination intervention involving a 12-week rollout of an asthma SDM toolkit (trial registration: 1.28.2014, #NCT02047929). An experienced lead PF trained research nurses as PFs from each of the 4 participating practice-based research networks (PBRNs) in a train-the-trainer model utilizing a one-day training event and subsequent remote meetings. Evaluation of PF engagement was measured through process improvement surveys. Results:Overall, the asthma SDM intervention was successfully implemented within the 4 PBRNs. All 10 facilitator-led practices remained engaged with their PFs, with 8 out of the 10 practices able to incorporate and sustain SDM visits or clinics. Responses from the surveys for process improvement yielded improved PF communication and team dynamics over time. Conclusions:This study demonstrated effective use of research nurses as practice facilitators during the dissemination of an asthma SDM intervention into primary care practices, adding to the knowledge of best practices by describing a model of large-scale implementation of a complex intervention through practice facilitation with nurses. Trial registration:"Comparing Traditional and Participatory Dissemination of a Shared Decision Making Intervention" was retrospectively registered at https://clinicaltrials.gov/ on January 28th, 2014 (NCT02047929).

Project description:Introduction: While the majority of young people who meet the criteria for being considered at increased risk of psychosis do not go on to develop a psychotic disorder, young people are currently being identified and treated in early intervention services. Ethical concerns have been raised concerning the decision about whether or not to provide treatment, and if so, what type of treatment. This study sought to support young people themselves to make these decisions with support from their clinician through a shared decision-making approach, facilitated by an online decision aid. Methods: This project used the International Patient Decision Aid Standards (IPDAS) to guide the development and piloting of an online decision aid across two phases: (1) qualitative, semi-structured focus groups with young people who were past clients and clinicians from an early psychosis service; and (2) pilot testing of the decision aid with clinicians and young people who were current clients to finalize the development. Results: Issues discussed by clinicians in the focus group were grouped into three main areas: (1) engagement phase; (2) assessment and priorities for treatment; and (3) initial and ongoing decision making. Clients focused on the context in which the decisions were made, including as they experienced initial feelings of resistance, and then acceptance of efforts made to describe and treat their mental health challenges. Clients highlighted the need for collaboration between themselves and their clinician, and the need to be equipped with the knowledge and tools to take care of themselves. These focus group data were used to refine the online decision aid. Pilot testing revealed that while it was overall useful and relevant, important limitations were noted by both clients and clinicians. Discussion: The use of a decision aid to facilitate shared decision making (SDM) in this area is feasible and has utility for both clients and clinicians. Use of such a tool can help to address the need to uphold the rights of young people as decision makers about their own care. Future efforts should embed decision aids within complex SDM interventions, and research to understand issues relating to implementation of these interventions.

Project description:BackgroundShared decision-making (SDM) is widely recommended and required by the Centers for Medicare and Medicaid for patients considering lung cancer screening (LCS).ObjectiveWe examined clinicians' communication practices and perceived barriers of SDM for LCS at three medical centers with established screening programs.DesignMulticenter qualitative study of clinicians participating in LCS.ApproachWe performed semi-structured interviews, which were transcribed and analyzed using directed content analysis, guided by a theoretical model of patient-clinician communication.ParticipantsWe interviewed 24 clinicians including LCS coordinators (2), pulmonologists (3), and primary care providers (17), 4 of whom worked for the LCS program, a thoracic surgeon, and a radiologist.ResultsAll clinicians agreed with the goal of SDM, to ensure the screening decision was congruent with the patient's values. The depth and type of information presented by each clinician role varied considerably. LCS coordinators presented detailed information including numeric estimates of benefit and harm. Most PCPs explained the process more generally, focusing on logistics and the high rate of nodule detection. No clinician explicitly elicited values or communication preferences. Many PCPs tailored the conversation based on their implicit understanding of patients' values and preferences, gained from past experiences. PCPs reported that time, lack of detailed personal knowledge of LCS, and patient preferences were barriers to SDM. Many clinicians perceived that a significant proportion of patients were not interested in specific percentages and preferred to receive a clinician recommendation.ConclusionsOur results suggest that clinicians support the goal of SDM for LCS decisions but PCPs may not perform some of its elements. The lack of completion of some elements, such as PCPs' lack of in-depth information exchange, may reflect perceived patient preferences for communication. As LCS is implemented, further research is needed to support a personalized, patient-centered approach to produce better outcomes.

Project description:Background. Accurate diagnosis of patients' preferences is central to shared decision making. Missing from clinical practice is an approach that links pretreatment preferences and patient-reported outcomes. Objective. We propose a Bayesian collaborative filtering (CF) algorithm that combines pretreatment preferences and patient-reported outcomes to provide treatment recommendations. Design. We present the methodological details of a Bayesian CF algorithm designed to accomplish 3 tasks: 1) eliciting patient preferences using conjoint analysis surveys, 2) clustering patients into preference phenotypes, and 3) making treatment recommendations based on the posttreatment satisfaction of like-minded patients. We conduct a series of simulation studies to test the algorithm and to compare it to a 2-stage approach. Results. The Bayesian CF algorithm and 2-stage approaches performed similarly when there was extensive overlap between preference phenotypes. When the treatment was moderately associated with satisfaction, both methods made accurate recommendations. The kappa estimates measuring agreement between the true and predicted recommendations were 0.70 (95% confidence interval = 0.052-0.88) and 0.73 (0.56-0.90) under the Bayesian CF and 2-stage approaches, respectively. The 2-stage approach failed to converge in settings in which clusters were well separated, whereas the Bayesian CF algorithm produced acceptable results, with kappas of 0.73 (0.56-0.90) and 0.83 (0.69-0.97) for scenarios with moderate and large treatment effects, respectively. Limitations. Our approach assumes that the patient population is composed of distinct preference phenotypes, there is association between treatment and outcomes, and treatment effects vary across phenotypes. Findings are also limited to simulated data. Conclusion. The Bayesian CF algorithm is feasible, provides accurate cluster treatment recommendations, and outperforms 2-stage estimation when clusters are well separated. As such, the approach serves as a roadmap for incorporating predictive analytics into shared decision making.

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Shared decision-making for people with asthma.

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