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Shared decision making for adults with severe mental illness: A concept analysis.

ABSTRACT: AIM:Shared decision making for adults with severe mental illness has increasingly attracted attention. However, this concept has not been comprehensively clarified. This review aimed to clarify a concept of shared decision making for adults with severe mental illness such as schizophrenia, depression, and bipolar disorder, and propose an adequate definition. METHODS:Rodgers' evolutionary concept analysis was used. MEDLINE, PsychINFO, and CINAHL were searched for articles written in English and published between 2010 and November 2019. The search terms were "psychiatr*" or "mental" or "schizophren*" or "depression" or "bipolar disorder", combined with "shared decision making". In total, 70 articles met the inclusion criteria. An inductive approach was used to identify themes and sub-themes related to shared decision making for adults with severe mental illness. Surrogate terms and a definition of the concept were also described. RESULTS:Four key attributes were identified: user-professional relationship, communication process, user-friendly visualization, and broader stakeholder approach. Communication process was the densest attribute, which consisted of five phases: goal sharing, information sharing, deliberation, mutual agreement, and follow-up. The antecedents as prominent predisposing factors were long-term complex illness, power imbalance, global trend, users' desire, concerns, and stigma. The consequences included decision-related outcomes, users' changes, professionals' changes, and enhanced relationship. CONCLUSIONS:Shared decision making for adults with severe mental illness is a communication process, involving both user-friendly visualization techniques and broader stakeholders. The process may overcome traditional power imbalance and encourage changes among both users and professionals that could enhance the dyadic relationship.

SUBMITTER: Aoki Y

PROVIDER: S-EPMC7590107 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Shared decision making for adults with severe mental illness: A concept analysis.

Aoki Yumi Y

Japan journal of nursing science : JJNS 20200805 4

<h4>Aim</h4>Shared decision making for adults with severe mental illness has increasingly attracted attention. However, this concept has not been comprehensively clarified. This review aimed to clarify a concept of shared decision making for adults with severe mental illness such as schizophrenia, depression, and bipolar disorder, and propose an adequate definition.<h4>Methods</h4>Rodgers' evolutionary concept analysis was used. MEDLINE, PsychINFO, and CINAHL were searched for articles written i ...[more]

PMID: 32761783

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Project description:Shared decision making (SDM) is important in achieving patient-centered care. SDM tools such as decision aids are intended to inform the patient. When used to assist in decision making between treatments, decision aids have been shown to reduce decisional conflict, increase ease of decision making, and increase modification of previous decisions.The purpose of this systematic review is to assess the impact of computerized decision aids on patient-centered outcomes related to SDM for seriously ill patients.PubMed and Scopus databases were searched to identify randomized controlled trials (RCTs) that assessed the impact of computerized decision aids on patient-centered outcomes and SDM in serious illness. Six RCTs were identified and data were extracted on study population, design, and results. Risk of bias was assessed by a modified Cochrane Risk of Bias Tool for Quality Assessment of Randomized Controlled Trials.Six RCTs tested decision tools in varying serious illnesses. Three studies compared different computerized decision aids against each other and a control. All but one study demonstrated improvement in at least one patient-centered outcome. Computerized decision tools may reduce unnecessary treatment in patients with low disease severity in comparison with informational pamphlets. Additionally, electronic health record (EHR) portals may provide the opportunity to manage care from the home for individuals affected by illness. The quality of decision aids is of great importance. Furthermore, satisfaction with the use of tools is associated with increased patient satisfaction and reduced decisional conflict. Finally, patients may benefit from computerized decision tools without the need for increased physician involvement.Most computerized decision aids improved at least one patient-centered outcome. All RCTs identified were at a High Risk of Bias or Unclear Risk of Bias. Effort should be made to improve the quality of RCTs testing SDM aids in serious illness.

Project description:Family Group conferences (FGC) provide a system by which a client and their family reach jointly key intervention decisions, from a number of options proposed by professionals. The system originated in child protection social work. Conceptually FGC is based on the assumption that the family is potentially a supportive social system for an individual with a variety of difficulties, including mental ill health. Reaching a family network agreement can lead to long term positive outcomes in self-confidence and social relationships. This strategy of shared decision making (SDM) can re-unite the family around the client's needs and wishes. It fits well the strengths based and the recovery-oriented approaches to mental ill health. Methodologically, this article provides a narrative review of existing empirical research about FGC in the context of adult mental health. In addition, two community case studies consisting of videos of a mother experiencing mental ill health and a daughter are analysed in terms of their subjective experience of the FGCs they were involved in, and looks at both the process and the outcomes of FGCs. The key findings demonstrate a high level of satisfaction from participating in the FGC meeting, while the evidence pertaining to the outcomes is inconclusive. Only very few systematic review studies, or comparative studies of different approaches to family decision making, exist, and there are no studies which offer cost effectiveness analysis. Discussion: The observed gap between the satisfaction from the process of FGC by the participants vs. the inconclusive outcomes relates to the implementation phase, in which the decisions made by the family are tested. Evaluating FGC processes and outcomes is complex. A systematic and comprehensive research of the implementation process is missing at this stage. In conclusion, FGC is a promising strategy of SDM in adult mental health. The research evidence indicates the need for further exploration of its implementation process, evaluative methodology and methods.

Project description:Background: Shared decision-making (SDM) in mental healthcare has received increased attention as a process to reinforce person-centered care. With the rapid development of digital health technology, researchers investigate how digital interventions may be utilized to support SDM. Despite the promise of digital interventions to support SDM, the effect of these in mental healthcare has not been evaluated before. Thus, this paper aims to assess the effect of SDM interventions complimented by digital technology in mental healthcare. Objective: The objective of this review was to systematically examine the effectiveness of digital SDM interventions on patient outcomes as investigated in randomized trials. Methods: We performed a systematic review and meta-analysis of randomized controlled trials on digital SDM interventions for people with a mental health condition. We searched for relevant studies in MEDLINE, PsycINFO, EMBASE, CINAHL, and the Cochrane Central Register of Controlled Trials. The search strategy included terms relating to SDM, digital systems, mental health conditions, and study type. The primary outcome was patient activation or indices of the same (e.g., empowerment and self-efficacy), adherence to treatment, hospital admissions, severity of symptoms, and level of functioning. Secondary outcomes were satisfaction, decisional conflict, working alliance, usage, and adherence of medicine; and adverse events were defined as harms or side effects. Results: Sixteen studies met the inclusion criteria with outcome data from 2,400 participants. Digital SDM interventions had a moderate positive effect as compared with a control condition on patient activation [standardized mean difference (SMD) = 0.56, CI: 0.10, 1.01, p = 0.02], a small effect on general symptoms (SMD = -0.17, CI: -0.31, -0.03, p = 0.02), and working alliance (SMD = 0.21, CI: 0.02, 0.41, p = 0.03) and for improving decisional conflict (SMD = -0.37, CI: -0.70, -0.05, p = 0.02). No effect was found on self-efficacy, other types of mental health symptoms, adverse events, or patient satisfaction. A total of 39 outcomes were narratively synthesized with results either favoring the intervention group or showing no significant differences between groups. Studies were generally assessed to have unclear or high risk of bias, and outcomes had a Grading of Recommendations Assessment, Development and Evaluation (GRADE) rating of low- or very low-quality evidence. Conclusions: Digital interventions to support SDM may be a promising tool in mental healthcare; but with the limited quality of research, we have little confidence in the estimates of effect. More quality research is needed to further assess the effectiveness of digital means to support SDM but also to determine which digital intervention features are most effective to support SDM. Systematic Review Registration: PROSPERO, identifier CRD42020148132.

Project description:IntroductionShared decision making (SDM) has been advocated as a key component of person-centred care and recovery from mental illness. Although the principles of SDM have been well documented, there is a lack of guidance about how to accomplish SDM in mental healthcare. The objective of the present protocol is to describe the methods for an umbrella review to determine the effectiveness elements of SDM interventions for persons diagnosed with a mental illness. An umbrella review's key characteristic is that it only considers for inclusion the highest level of evidence, namely other systematic reviews and meta-analyses.Methods and analysisElectronic searches will be performed in CINAHL, PubMed, Scopus, Ovid MEDLINE, Ovid EMBASE, Ovid Cochrane Library, Web of Science, Scopus and Ovid PsycINFO. Based on Joanna Briggs Institute recommended guidelines, review articles will be included if they were published between 2010 and 2021. This approach will help identify current and emerging evidence-based treatment options in mental illness. Included articles will be assessed for quality using Assessment of Multiple Systematic Reviews 2 tool and ratings of the quality of evidence in each review. Presentation of results will align with guidelines in the Cochrane Handbook for Systematic Reviews of Interventions and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 statement. Findings will be stratified by mode of intervention and implementation characteristics and will inform development of SDM taxonomy in mental healthcare.Ethics and disseminationThis umbrella review will focus on the analysis of secondary data and does not require ethics approval. Findings will be disseminated widely to clinicians, researchers and services users via journal publication, conference presentations and social media. The results will contribute to the conceptualisation and understanding of effective SDM interventions in mental healthcare and to improving the quality of SDM for individuals with a mental illness.Prospero registration numberCRD42020190700.

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

Shared decision making for adults with severe mental illness: A concept analysis.

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Shared decision making for adults with severe mental illness: A concept analysis.

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