Objectives: To (1) provide an up-to-date overview of shared decision making (SDM)-models, (2) give insight in the prominence of components present in SDM-models, (3) describe who is identified as responsible within the components (patient, healthcare professional, both, none), (4) show the occurrence of SDM-components over time, and (5) present an SDM-map to identify SDM-components seen as key, per healthcare setting.Design: Systematic review.Eligibility criteria: Peer-reviewed articles in English presenting a new or adapted model of SDM.Information sources: Academic Search Premier, Cochrane, Embase, Emcare, PsycINFO, PubMed, and Web of Science were systematically searched for articles published up to and including September 2, 2019.Results: Forty articles were included, each describing a unique SDM-model. Twelve models were generic, the others were specific to a healthcare setting. Fourteen were based on empirical data, 26 primarily on analytical thinking. Fifty-three different elements were identified and clustered into 24 components. Overall, Describe treatment optionswas the most prominent component across models. Components present in >50% of models were:Make the decision (75%),Patient preferences (65%),Tailor information (65%),Deliberate (58%), Create choice awareness (55%), andLearn about the patient(53%). In the majority of the models (27/40), both healthcare professional and patient were identified as actors. Over time,Describe treatment optionsandMake the decisionare the two components which are present in most models in any time period.Create choice awarenessstood out for being present in a markedly larger proportion of models over time.Conclusions: This review provides an up-to-date overview of SDM-models, showing that SDM-models quite consistently share some components but that a unified view on what SDM is, is still lacking. Clarity about what SDM constitutes is essential though for implementation, assessment, and research purposes. A map is offered to identify SDM-components seen as key.Trial registration: PROSPERO registration CRD42015019740

Consensus guidelines have recommended that decision aids include a process for helping patients clarify their values. We sought to examine the theoretical and empirical evidence related to the use of values clarification methods in patient decision aids.

This Viewpoint characterizes time as a scarce but vital element of shared patient-clinician decision making and urges development of policies that allocate mean

Background: Patient decision aids should help people make evidence-informed decisions aligned with their values. There is limited guidance about how to achieve such alignment. Purpose: To describe the range of values clarification methods available to patient decision aid developers, synthesize evidence regarding their relative merits, and foster collection of evidence by offering researchers a proposed set of outcomes to report when evaluating the effects of values clarification methods. Data Sources: MEDLINE, EMBASE, PubMed, Web of Science, the Cochrane Library, and CINAHL. Study Selection: We included articles that described randomized trials of 1 or more explicit values clarification methods. From 30,648 records screened, we identified 33 articles describing trials of 43 values clarification methods. Data Extraction: Two independent reviewers extracted details about each values clarification method and its evaluation. Data Synthesis: Compared to control conditions or to implicit values clarification methods, explicit values clarification methods decreased the frequency of values-incongruent choices (risk difference, –0.04; 95% confidence interval [CI], –0.06 to –0.02; P < 0.001) and decisional conflict (standardized mean difference, –0.20; 95% CI, –0.29 to –0.11; P < 0.001). Multicriteria decision analysis led to more values-congruent decisions than other values clarification methods (χ² = 9.25, P = 0.01). There were no differences between different values clarification methods regarding decisional conflict (χ² = 6.08, P = 0.05). Limitations: Some meta-analyses had high heterogeneity. We grouped values clarification methods into broad categories. Conclusions: Current evidence suggests patient decision aids should include an explicit values clarification method. Developers may wish to specifically consider multicriteria decision analysis. Future evaluations of values clarification methods should report their effects on decisional conflict, decisions made, values congruence, and decisional regret.

Patient participation in treatment decision-making is being increasingly advocated, although cancer treatments are often guideline-driven. Trade-offs between benefits and side effects underlying guidelines are made by clinicians. Evidence suggests that clinicians are inaccurate at predicting patient values. The aim was to assess what role oncologists and cancer patients prefer in deciding about treatment, and how they view patient participation in treatment decision-making. Seventy disease-free cancer patients and 60 oncologists (surgical, radiation, and medical) were interviewed about their role preferences using the Control Preferences Scale (CPS) and about their views on patient participation using closed- and open-ended questions. Almost all participants preferred treatment decisions to be the outcome of a shared process. Clinicians viewed participation more often as reaching an agreement, whereas 23% of patients defined participation exclusively as being informed. Of the participants, 81% thought not all patients are able to participate and 74% thought clinicians are not always able to weigh the pros and cons of treatment for patients, especially not quality as compared with length of life. Clinicians seemed reluctant to share probability information on the likely impact of adjuvant treatment. Clinicians should acknowledge the legitimacy of patients' values in treatment decisions. Guidelines should recommend elicitation of patient values at specific decision points.

Shared decision making (SDM) requires an active role from patients, which might be difficult for some. We aimed to identify what patients need to be ready (i.e., well-equipped and enabled) to participate in SDM about treatment, and what patient- and decision-related characteristics may influence readiness. We conducted semi-structured interviews with patients and professionals (physicians, nurses, general practitioners, and researchers). Interviews were analyzed inductively. We identified five elements of patient readiness: 1) understanding of and attitude towards SDM, 2) health literacy, 3) skills in communicating and claiming space, 4) self-awareness, and 5) consideration skills. We identified 10 characteristics that may influence elements of readiness: 1) age, 2) cultural background, 3) educational background, 4) close relationships, 5) mental illness, 6) emotional distress, 7) acceptance of diagnosis, 8) clinician-patient relationship, 9) decision type, and 10) time. We identified a wide range of elements that may constitute patient readiness for SDM. Readiness might vary between and within patients. This variation may result from differences in patient- and decision-related characteristics. Clinicians should be aware that not all patients may be ready for SDM at a given moment and may need support to enhance their readiness.