Dataset Information

Reflecting on shared decision making: A reflection-quantification study.

ABSTRACT:

Background

Reflecting ("stop-and-think") before rating may help patients consider the quality of shared decision making (SDM) and mitigate ceiling/halo effects that limit the performance of self-reported SDM measures.

Methods

We asked a diverse patient sample from the United States to reflect on their care before completing the 3-item CollaboRATE SDM measure. Study 1 focused on rephrasing CollaboRATE items to promote reflection before each item. Study 2 used 5 open-ended questions (about what went well and what could be improved upon, signs that the clinician understood the patient's situation, how the situation will be addressed, and why this treatment plan makes sense) to invite reflection before using the whole scale. A linear analogue scale assessed the extent to which the plan of care made sense to the patient.

Results

In Study 1, 107 participants completed surveys (84% response rate), 43 (40%) rated a clinical decision of which 27 (63%) after responding to reflection questions. Adding reflection lowered CollaboRATE scores ("less" SDM) and reduced the proportion of patients giving maximum (ceiling) scores (not statistically significant). In Study 2, 103 of 212 responders (49%) fully completed the version containing reflection questions. Reflection did not significantly change the distribution of CollaboRATE scores or of top scores. Participants indicated high scores on the sense of their care plan (mean 9.7 out of 10, SD 0.79). This rating was weakly correlated with total CollaboRATE scores (rho = .4, P = .0001).

Conclusion

Reflection-before-quantification interventions may not improve the performance of patient-reported measures of SDM with substantial ceiling/halo effects.

SUBMITTER: Kunneman M

PROVIDER: S-EPMC6803557 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Publications

Reflecting on shared decision making: A reflection-quantification study.

Kunneman Marleen M LaVecchia Christina M CM Singh Ospina Naykky N Abu Dabrh Abd Moain AM Behnken Emma M EM Wilson Patrick P Branda Megan E ME Hargraves Ian G IG Yost Kathleen J KJ Frankel Richard M RM Montori Victor M VM

Health expectations : an international journal of public participation in health care and health policy 20190814 5

<h4>Background</h4>Reflecting ("stop-and-think") before rating may help patients consider the quality of shared decision making (SDM) and mitigate ceiling/halo effects that limit the performance of self-reported SDM measures.<h4>Methods</h4>We asked a diverse patient sample from the United States to reflect on their care before completing the 3-item CollaboRATE SDM measure. Study 1 focused on rephrasing CollaboRATE items to promote reflection before each item. Study 2 used 5 open-ended questions ...[more]

PMID: 31414553

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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.

Project description:Objective: The objective was to develop a decision aid (DA) to facilitate shared decision making (SDM) around whether to obtain computed tomography (CT) imaging in patients presenting to the emergency department (ED) with suspected uncomplicated ureterolithiasis.Methods: We used evidence-based DA development methods, including qualitative methods and iterative stakeholder engagement, to develop and refine a DA. Guided by the Ottawa Decision Support Framework, International Patient Decision Aid Standards (IPDAS), and a steering committee made up of stakeholders, we conducted interviews and focus groups with a purposive sample of patients, community members, emergency clinicians, and other stakeholders. We used an iterative process to code the transcripts and identify themes. We beta-tested the DA with patient-clinician dyads facing the decision in real time.Results: From August 2018 to August 2019, we engaged 102 participants in the design and iterative refinement of a DA focused on diagnostic options for patients with suspected ureterolithiasis. Forty-six were ED patients, community members, or patients with ureterolithiasis, and the remaining were emergency clinicians (doctors, residents, advanced practitioners), researchers, urologists, nurses, or other physicians. Patients and clinicians identified several key decisional needs including an understanding of accuracy, uncertainty, radiation exposure/cancer risk, and clear return precautions. Patients and community members identified facilitators to SDM, such as a checklist of signs and symptoms. Many stakeholders, including both patients and ED clinicians, expressed a strong pro-CT bias. A six-page DA was developed, iteratively refined, and beta-tested.Conclusions: Using stakeholder engagement and qualitative inquiry, we developed an evidence-based DA to facilitate SDM around the question of CT scan utilization in patients with suspected uncomplicated ureterolithiasis. Future research will test the efficacy of the DA in facilitating SDM.

Dataset Information

Reflecting on shared decision making: A reflection-quantification study.

Background

Methods

Results

Conclusion

Publications

Reflecting on shared decision making: A reflection-quantification study.

Similar Datasets

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