Dataset Information

A three-talk model for shared decision making: multistage consultation process.

ABSTRACT: Objectives To revise an existing three-talk model for learning how to achieve shared decision making, and to consult with relevant stakeholders to update and obtain wider engagement.Design Multistage consultation process.Setting Key informant group, communities of interest, and survey of clinical specialties.Participants 19 key informants, 153 member responses from multiple communities of interest, and 316 responses to an online survey from medically qualified clinicians from six specialties.Results After extended consultation over three iterations, we revised the three-talk model by making changes to one talk category, adding the need to elicit patient goals, providing a clear set of tasks for each talk category, and adding suggested scripts to illustrate each step. A new three-talk model of shared decision making is proposed, based on "team talk," "option talk," and "decision talk," to depict a process of collaboration and deliberation. Team talk places emphasis on the need to provide support to patients when they are made aware of choices, and to elicit their goals as a means of guiding decision making processes. Option talk refers to the task of comparing alternatives, using risk communication principles. Decision talk refers to the task of arriving at decisions that reflect the informed preferences of patients, guided by the experience and expertise of health professionals.Conclusions The revised three-talk model of shared decision making depicts conversational steps, initiated by providing support when introducing options, followed by strategies to compare and discuss trade-offs, before deliberation based on informed preferences.

SUBMITTER: Elwyn G

PROVIDER: S-EPMC5683042 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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A three-talk model for shared decision making: multistage consultation process.

Elwyn Glyn G Durand Marie Anne MA Song Julia J Aarts Johanna J Barr Paul J PJ Berger Zackary Z Cochran Nan N Frosch Dominick D Galasiński Dariusz D Gulbrandsen Pål P Han Paul K J PKJ Härter Martin M Kinnersley Paul P Lloyd Amy A Mishra Manish M Perestelo-Perez Lilisbeth L Scholl Isabelle I Tomori Kounosuke K Trevena Lyndal L Witteman Holly O HO Van der Weijden Trudy T

BMJ (Clinical research ed.) 20171106

Objectives To revise an existing three-talk model for learning how to achieve shared decision making, and to consult with relevant stakeholders to update and obtain wider engagement.Design Multistage consultation process.Setting Key informant group, communities of interest, and survey of clinical specialties.Participants 19 key informants, 153 member responses from multiple communities of interest, and 316 responses to an online survey from medically qualified clinici ...[more]

PMID: 29109079

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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:ObjectiveTo inventory instruments assessing the process of shared decision making and appraise their measurement quality, taking into account the methodological quality of their validation studies.MethodsIn a systematic review we searched seven databases (PubMed, Embase, Emcare, Cochrane, PsycINFO, Web of Science, Academic Search Premier) for studies investigating instruments measuring the process of shared decision making. Per identified instrument, we assessed the level of evidence separately for 10 measurement properties following a three-step procedure: 1) appraisal of the methodological quality using the COnsensus-based Standards for the selection of health status Measurement INstruments (COSMIN) checklist, 2) appraisal of the psychometric quality of the measurement property using three possible quality scores, 3) best-evidence synthesis based on the number of studies, their methodological and psychometrical quality, and the direction and consistency of the results. The study protocol was registered at PROSPERO: CRD42015023397.ResultsWe included 51 articles describing the development and/or evaluation of 40 shared decision-making process instruments: 16 patient questionnaires, 4 provider questionnaires, 18 coding schemes and 2 instruments measuring multiple perspectives. There is an overall lack of evidence for their measurement quality, either because validation is missing or methods are poor. The best-evidence synthesis indicated positive results for a major part of instruments for content validity (50%) and structural validity (53%) if these were evaluated, but negative results for a major part of instruments when inter-rater reliability (47%) and hypotheses testing (59%) were evaluated.ConclusionsDue to the lack of evidence on measurement quality, the choice for the most appropriate instrument can best be based on the instrument's content and characteristics such as the perspective that they assess. We recommend refinement and validation of existing instruments, and the use of COSMIN-guidelines to help guarantee high-quality evaluations.

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

A three-talk model for shared decision making: multistage consultation process.

Publications

A three-talk model for shared decision making: multistage consultation process.

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