Dataset Information

Preference Phenotypes in Support of Shared Decision-Making at Point-of-Care for Patients With Rheumatoid Arthritis: A Proof-of-Concept Study.

ABSTRACT:

Objective

In this proof-of-concept study, we sought to evaluate whether a value clarification tool enabling patients to view a set of rheumatoid arthritis (RA) treatment preference phenotypes could be used to support shared decision-making at the point-of-care.

Methods

We conducted a pretest/post test study. English-speaking patients with RA presenting to their scheduled outpatient visits were asked to participate. Visits for patients with active RA were transcribed. Shared decision-making components were measured using a quantitative coding scheme based on an established model of shared decision-making.

Results

Forty-six visits were included in the pretest and 40 in the post test phases. Providers offered more disease-modifying antirheumatic drugs (DMARDs) (2 or more) in the post test visits (60%) compared to the pretest visits (47.8%). Overall, more patients vocalized their values and/or preferences in the post test visits compared to the pretest visits for treatment escalation decisions including a choice of 1 new DMARD (90.9% versus 56.3%), 2 or more new DMARDs (95.8% versus 86.4%), as well as prednisone (87.5% versus 66.7%). Providers were also more likely to base their recommendations on patients' values and/or preferences in the post test (100% of 6 visits) than the pretest (64.3% of 14 visits) phases during visits in which a recommendation was made. The mean ± SD length of the visit was 29.9 ± 11.6 minutes and 25.1 ± 10.7 minutes in the pretest and post test phases, respectively.

Conclusion

This study provides an early indication that a value clarification tool allowing patients to consider a set of preference phenotypes can support shared decision-making at the point-of-care without extending visit time.

SUBMITTER: Hsiao B

PROVIDER: S-EPMC6310675 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Preference Phenotypes in Support of Shared Decision-Making at Point-of-Care for Patients With Rheumatoid Arthritis: A Proof-of-Concept Study.

Hsiao Betty B Binder-Finnema Pauline P Nowell W Benjamin WB Michel George G Wiedmeyer Carole C Fraenkel Liana L

Arthritis care & research 20190408 5

<h4>Objective</h4>In this proof-of-concept study, we sought to evaluate whether a value clarification tool enabling patients to view a set of rheumatoid arthritis (RA) treatment preference phenotypes could be used to support shared decision-making at the point-of-care.<h4>Methods</h4>We conducted a pretest/post test study. English-speaking patients with RA presenting to their scheduled outpatient visits were asked to participate. Visits for patients with active RA were transcribed. Shared decisi ...[more]

PMID: 29953733

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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 in Support of Shared Decision-Making at Point-of-Care for Patients With Rheumatoid Arthritis: A Proof-of-Concept Study.

Objective

Methods

Results

Conclusion

Publications

Preference Phenotypes in Support of Shared Decision-Making at Point-of-Care for Patients With Rheumatoid Arthritis: A Proof-of-Concept Study.

Similar Datasets

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