Dataset Information

Preference phenotypes to facilitate shared decision-making in rheumatoid arthritis.

ABSTRACT: OBJECTIVE:Implementing treat-to-target (TTT) strategies requires that patients with rheumatoid arthritis (RA) and their rheumatologists decide on how best to escalate care when indicated. The objective of this study was to develop preference phenotypes to facilitate shared decision-making at the point of care for patients failing methotrexate monotherapy. METHODS:We developed a conjoint analysis survey to measure the preferences of patient with RA for triple therapy, biologics and Janus kinase (JAK) inhibitors. The survey included seven attributes: administration, onset, bothersome side effects, serious infection, very rare side effects, amount of information and cost. Each choice set (n=12) included three hypothetical profiles. Preference phenotypes were identified by applying latent class analysis to the conjoint data. RESULTS:1273 participants completed the survey. A five-group solution was chosen based on progressively lower values of the Akaike and Bayesian information criteria. Members of the largest group (group 3: 38.4%) were most strongly impacted by the cost of the medication. The next largest group (group 1: 25.8%) was most strongly influenced by the risk of bothersome side effects. Members of group 2 (11.2%) were also risk averse, but were most concerned with the risk of very rare side effects. Group 4 (6.6%) strongly preferred oral over parenteral medications. Members of group 5 (18.0%) were most strongly and equally influenced by onset of action and the risk of serious infections. CONCLUSIONS:Treatment preferences of patients with RA can be measured and represented by distinct phenotypes. Our results underscore the variability in patients' values and the importance of using a shared decision-making approach to implement TTT.

SUBMITTER: Fraenkel L

PROVIDER: S-EPMC5898982 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Preference phenotypes to facilitate shared decision-making in rheumatoid arthritis.

Fraenkel Liana L Nowell W Benjamin WB Michel George G Wiedmeyer Carole C

Annals of the rheumatic diseases 20171215 5

<h4>Objective</h4>Implementing treat-to-target (TTT) strategies requires that patients with rheumatoid arthritis (RA) and their rheumatologists decide on how best to escalate care when indicated. The objective of this study was to develop preference phenotypes to facilitate shared decision-making at the point of care for patients failing methotrexate monotherapy.<h4>Methods</h4>We developed a conjoint analysis survey to measure the preferences of patient with RA for triple therapy, biologics and ...[more]

PMID: 29247126

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Project description:AIM:The aim of this study was to explore shared decision-making among residents, their families and staff to determine relevant strategies to support shared decision-making in long-term care (LTC). BACKGROUND:Meaningful engagement of long-term care home (LTCH) residents and their families in care decisions is key in the provision of quality of care. Shared decision-making is an interprofessional approach to increasing resident and family engagement in care decisions which can lead to higher quality decisions, more relevant care interventions and greater resident, family, and staff satisfaction. Despite these advantages, shared decision-making has not been widely implemented in practice in LTC. METHODS:The study took place in one LTCH in Toronto, Ontario, Canada. A qualitative descriptive design was used to explore how residents, family members and staff described how they collaborate when making decisions concerning resident care, and their perceptions of facilitators and challenges to a collaborative approach to decision-making. Individual interviews were conducted with nine participants: residents, families and staff. Data were analysed using content and thematic analysis. FINDINGS:Four main themes that described resident, family and staff perspectives of shared decision-making were as follows: (a) oral communication pathways for information sharing; (b) supporting resident decision-making autonomy; (c) relational aspects of care facilitate shared decision-making; and (d) lack of effective communication creates barriers to shared decision-making. CONCLUSION:As the demand for LTC continues to increase, it is crucial that healthcare providers engage in collaborative, relational practices that foster high-quality resident care. While a relational approach to care can facilitate shared decision-making, there are opportunities to further cultivate shared decision-making in LTCHs through more effective communication and collaboration. IMPLICATIONS FOR PRACTICE:Understanding how information is shared and decisions are made can facilitate shared decision-making in LTCHs. The strategies identified from this study could be further co-developed and implemented in LTCHs.

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.

Dataset Information

Preference phenotypes to facilitate shared decision-making in rheumatoid arthritis.

Publications

Preference phenotypes to facilitate shared decision-making in rheumatoid arthritis.

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