Showing papers on "R-CAST published in 2017"

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.

A Voting-Based System for Ethical Decision Making

Abstract: We present a general approach to automating ethical decisions, drawing on machine learning and computational social choice In a nutshell, we propose to learn a model of societal preferences, and, when faced with a specific ethical dilemma at runtime, efficiently aggregate those preferences to identify a desirable choice We provide a concrete algorithm that instantiates our approach; some of its crucial steps are informed by a new theory of swap-dominance efficient voting rules Finally, we implement and evaluate a system for ethical decision making in the autonomous vehicle domain, using preference data collected from 13 million people through the Moral Machine website

Shared Decision Making and Improving Health Care: The Answer Is Not In.

Abstract: Achieving health care of higher quality at lower cost has fueled policy interest in shared decision making (SDM).1 In SDM, clinicians and patients work together to understand the patient’s situation and determine how best to address it.2 Programs are in place in the United States to promote SDM using legal and financial incentives, mostly by implementing patient decision aids (PtDAs).1 The Cochrane review3 of SDM tools for people facing treatment or screening decisions is the key evidence cited in policy statements that propose to implement, distribute, and use certified PtDAs. There are at least 2 distinct types of SDM tools, PtDAs and conversation aids (sometimes called within-encounter decision aids).4 Typically, both types of tools describe the current science about a specific medical condition and about the available options to address it. However, they serve different purposes. Patient decision aids aim to provide patients with relevant information, improve knowledge, and encourage patient involvement in decision making. Thus, they directly assist patients in making their own decisions (so-called informed decision making), or indirectly in preparing them to participate in SDM conversations with their clinicians. In contrast, conversation aids are designed to encourage and directly support the conversations that patients and clinicians have when making decisions together.2 Their aim is to improve the quality of the SDM process rather than surrogate outcomes such as patient knowledge. For the last 16 years, an international team of researchers with the Cochrane collaboration has periodically updated meta-analyses summarizing results of the published randomized trials on SDM tools. In this issue of JAMA, Stacey et al5 summarize their recent Cochrane review of 105 randomized trials of SDM tools, focusing on 50 different decisions and involving a total of 31 043 participants. The authors found that PtDAs consistently improved patient knowledge of options and outcomes compared with control interventions (mean knowledge scores, 70% vs 57%, respectively; highquality evidence) and patient knowledge of risks (relative risk, 2.1; moderate-quality evidence) (control interventions included usual care, no intervention, general information, guidelines, and placebo interventions). Also, patients were more clear about what mattered most to them (mean difference, 8.8%; high-quality evidence). These results were consistent with earlier Cochrane reviews on SDM tools. Policy makers interested in improving the quality of care and reducing costs have promoted the use of PtDAs as a tactic to achieve patient-centeredness through SDM and to reduce the use of invasive and costly procedures, such as joint replacement surgery for knee osteoarthritis or coronary angioplasty for stable angina.6 Does the updated Cochrane review support these policy justifications? Stacey et al5 concluded that patients receiving an SDM tool (ie, a PtDA or a conversation aid) reported feeling more involved in decision making. However, only 10 of the 105 trials included in the review sought to estimate the effect of using SDM tools on achieving SDM. No trial of pre-encounter PtDAs assessed whether their distribution increased SDM. Five trials directly observed how patients and clinicians made decisions during consultations and found SDM tools were effective in promoting SDM. All 5 of these trials used conversation aids rather than PtDAs. The Cochrane review found inconsistent effects of PtDAs on health care use, outcomes, and costs. In particular, use of PtDAs did not consistently reduce the use of invasive or expensive treatments. Thus, the extent to which these tools can reduce costs remains unclear. Previous observational data suggesting that PtDAs may reduce health care use and cost were limited by confounding (eg, by observing a reduction in elective orthopedic procedures during the great recession7), by regression to the mean when procedures are overused, or by using PtDAs to reduce access to care. Informed patient choice with PtDAs, nonetheless, seems superior to restricting access (eg, preauthorizations, formulary restrictions) or restricting coverage for invasive and expensive treatments, which may prevent patients from receiving desired and helpful treatments. However, empowering patients who are ill to refuse their clinicians’ recommendations to correct health care overuse is also problematic. Health care should provide care for patients and should not use patients as a means to correct systemic problems. The review reveals important limitations in the evidence regarding SDM tools. For instance, only a small fraction of developed tools has been tested in published randomized trials. Patient-centeredness or even the quality of SDM processes were rarely ascertained. The lack of trustworthy evidence about the effects of SDM tools on quality of care and cost, however, should not be construed as evidence of no effect. In fact, policy makers may be willing to overlook these limitations in the body of evidence given the low cost and low risk of harm from implementing PtDAs. Although the low cost of these tools may reduce the evidence required to justify their use in practice, it does not eliminate the need for reliable evidence that their use is more likely than not to achieve the policy goals. Related article page 657 Opinion

Multi-Objective Decision Making

Abstract: Many real-world decision problems have multiple objectives. For example, when choosing a medical treatment plan, we want to maximize the efficacy of the treatment, but also minimize the side effects. These objectives typically conflict, e.g., we can often increase the efficacy of the treatment, but at the cost of more severe side effects. In this book, we outline how to deal with multiple objectives in decision-theoretic planning and reinforcement learning algorithms. To illustrate this, we employ the popular problem classes of multi-objective Markov decision processes (MOMDPs) and multi-objective coordination graphs (MO-CoGs). First, we discuss different use cases for multi-objective decision making, and why they often necessitate explicitly multi-objective algorithms. We advocate a utility-based approach to multi-objective decision making, i.e., that what constitutes an optimal solution to a multi-objective decision problem should be derived from the available information about user utility. We show how different assumptions about user utility and what types of policies are allowed lead to different solution concepts, which we outline in a taxonomy of multi-objective decision problems. Second, we show how to create new methods for multi-objective decision making using existing single-objective methods as a basis. Focusing on planning, we describe two ways to creating multi-objective algorithms: in the inner loop approach, the inner workings of a single-objective method are adapted to work with multi-objective solution concepts; in the outer loop approach, a wrapper is created around a single-objective method that solves the multi-objective problem as a series of single-objective problems. After discussing the creation of such methods for the planning setting, we discuss how these approaches apply to the learning setting. Next, we discuss three promising application domains for multi-objective decision making algorithms: energy, health, and infrastructure and transportation. Finally, we conclude by outlining important open problems and promising future directions.

Ability Grouping and Differentiated Instruction in an Era of Data-Driven Decision Making

Abstract: Despite data-driven decision making being a ubiquitous part of policy and school reform efforts, little is known about how teachers use data for instructional decision making. Drawing on data from a qualitative case study of four elementary schools, we examine the logic and patterns of teacher decision making about differentiation and ability grouping. We find that district and school policies conditioned teachers’ decision making through mandated time for instructional differentiation, curricular tools, and online program adoption. Educators used various strategies reflecting different logics, types of data used, and sources of decision making. Implications for theory and research are discussed.

User-driven design of decision support systems for polycentric environmental resources management

Abstract: Open and decentralized technologies such as the Internet provide increasing opportunities to create knowledge and deliver computer-based decision support for multiple types of users across scales. However, environmental decision support systems/tools (henceforth EDSS) are often strongly science-driven and assuming single types of decision makers, and hence poorly suited for more decentralized and polycentric decision making contexts. In such contexts, EDSS need to be tailored to meet diverse user requirements to ensure that it provides useful (relevant), usable (intuitive), and exchangeable (institutionally unobstructed) information for decision support for different types of actors. To address these issues, we present a participatory framework for designing EDSS that emphasizes a more complete understanding of the decision making structures and iterative design of the user interface. We illustrate the application of the framework through a case study within the context of water-stressed upstream/downstream communities in Lima, Peru. Environmental management may involve polycentric governance arrangements.Decision support for such contexts needs to meet diverse user requirements.A user-driven approach is proposed that involves actor and decision making analysis.This is combined with co-design methods from Human-Computer Interaction research.The result is more tailored decision support for users with different experiences.

Communication practices that encourage and constrain shared decision making in health-care encounters: Systematic review of conversation analytic research.

Abstract: Background: Shared decision making (SDM) is generally treated as good practice in health-care interactions. Conversation analytic research has yielded detailed findings about decision making in health-care encounters. Objective: To map decision making communication practices relevant to health-care outcomes in face-to-face interactions yielded by prior conversation analyses, and to examine their function in relation to SDM. Search strategy: We searched nine electronic databases (last search November 2016) and our own and other academics’ collections. Inclusion criteria: Published conversation analyses (no restriction on publication dates) using recordings of health-care encounters in English where the patient (and/or companion)was present and where the data and analysis focused on health/illness-related decision making. Data extraction and synthesis: We extracted study characteristics, aims, findings relating to communication practices, how these functioned in relation to SDM, and internal/external validity issues. We synthesised findings aggregatively. Results: Twenty-eight publications met the inclusion criteria. We sorted findings into 13 types of communication practices and organized these in relation to four elements of decision-making sequences: (i) broaching decision making; (ii) putting forward a course of action; (iii) committing or not (to the action put forward); and (iv) HCPs’ responses to patients’ resistance or withholding of commitment. Patients have limited opportunities to influence decision making. HCPs’ practices may constrain or encourage this participation. Conclusions: Patients, companions and HCPs together treat and undertake decision making as shared, though to varying degrees. Even for non-negotiable treatment trajectories, the spirit of SDM can be invoked through practices that encourage participation (eg by bringing the patient towards shared understanding of the decision’s rationale).

Current concepts of shared decision making in orthopedic surgery

Abstract: The Shared Decision Making (SDM) model, a collaborative decision making process between the physician and patient to make an informed clinical decision that enhances the chance of treatment success as defined by each patient’s preferences and values, has become a new and promising tool in the healthcare process; however, minimal data exists on its application in the orthopedic surgical specialty. Increasing evidence has demonstrated that this once novel idea can be implemented successfully in the orthopedic setting to improve patient outcomes. SDM can be applied without significant increases in the office length. Patients report that a physician that takes the time to listen to them is among the most important factors in their care. When time was focused on the SDM process, there was a direct correlation between the time spent with a patient and patient satisfaction. Patients exposed to a decision aid prior to surgery gained a greater knowledge from baseline to make a higher quality decision that was consistent with their values. Involving family members preoperatively can help all patients adhere to postoperative regimens. Exposing patients to a decision aid can reduce expensive elective surgeries, in favor of non-operative management. Incorporating patient goals into the decision-making process has increased satisfaction, compliance, and outcomes. SDM is a two-way exchange of information that attempts to correct the inequality of power between the patient and physician. Decision-aids are helpful tools that facilitate the decision-making process. Treatment decisions are consistent with patient preferences and values when there may be no “best” therapy. A good patient–physician relationship is essential during the process to reduce decisional conflict and increase overall patient outcomes.

Consensus in Group Decision Making and Social Networks

Abstract: The consensus reaching process is the most important step in a group decision making scenario. This step is most frequently identified as a process consisting of some discussion rounds in which several decision makers, which are involved in the problem, discuss their points of view with the purpose of obtaining the maximum agreement before making the decision. Consensus reaching processes have been well studied and a large number of consensus approaches have been developed. In recent years, the researchers in the field of decision making have shown their interest in social networks since they may be successfully used for modelling communication among decision makers. However, a social network presents some features differentiating it from the classical scenarios in which the consensus reaching processes have been applied. The objective of this study is to investigate the main consensus methods proposed in social networks and bring out the new challenges that should be faced in this research field.

Cognitive Control as Cost‐Benefit Decision Making

Abstract: © 2017 John Wiley & Sons, Ltd. Published 2017 by John Wiley & Sons, Ltd. Many everyday situations afford us a set of default behaviours and cognitive processes that could play out automatically in response to stimuli in our environment. Cognitive control enables us to modify our thoughts and actions away from those defaults in a variety of ways, allowing us as a species to perform great intellectual feats such as planning (D. A. Simon & Daw, 2011), reasoning (Christoff et al., 2001), inhibition (Aron, 2011), and working memory maintenance (Goldman‐Rakic, 1987). But what is it that determines when we exert control, how much we do so, and what form(s) this control takes? In other words, by what computational and neural mechanisms is the controller itself controlled (Botvinick & Cohen, 2015; Dayan, 2012)? In this chapter, we address this question by framing it as a reward‐based decision‐making problem. This approach views the exertion of cognitive effort as being determined by the output of a decision that considers both the costs and benefits of mobilising cognitive control at a given moment. We begin by enumerating a set of factors that weigh in favour of the exer­ tion of control and those that oppose it. We then present a theoretical framework that spec­ ifies how these costs and benefits are integrated together to form a decision about whether and how control should be deployed. Finally, we describe the neural underpinnings of this decision process, with a particular focus on the role of the dorsal anterior cingulate cortex (dACC) in determining how best to allocate control.

Dynamic Decision Making: Learning Processes and New Research Directions

Abstract: Objective:The aim of this manuscript is to provide a review of contemporary research and applications on dynamic decision making (DDM)Background:Since early DDM studies, there has been little syst

What is a good medical decision? A research agenda guided by perspectives from multiple stakeholders

Abstract: Informed and shared decision making are critical aspects of patient-centered care, which has contributed to an emphasis on decision support interventions to promote good medical decision making. However, researchers and healthcare providers have not reached a consensus on what defines a good decision, nor how to evaluate it. This position paper, informed by conference sessions featuring diverse stakeholders held at the 2015 Society of Behavioral Medicine and Society for Medical Decision Making annual meetings, describes key concepts that influence the decision making process itself and that may change what it means to make a good decision: interpersonal factors, structural constraints, affective influences, and values clarification methods. This paper also proposes specific research questions within each of these priority areas, with the goal of moving medical decision making research to a more comprehensive definition of a good medical decision, and enhancing the ability to measure and improve the decision making process.

The Tool Supporting Decision Making Process in Area of Job-Shop Scheduling

Abstract: Today most manufacturing companies from machine building industry are operating in single unit or short-run production which is very complex in terms of decision making processes in production planning area. The difficulty in decision making in the area of scheduling is caused by the necessity of analysing multiple factors and evaluating various scheduling options due to numerous criteria. The article presents the author’s tool supporting decision making in the area of job-shop scheduling. The tool introduced in the article enables scheduling based on author’s priority rule allowing maximum usage of the most loaded resource (known as critical resource), which determines efficiency of the production system. The tool has been designed and verified as a part of PhD dissertation research.

Shared decision making in mental health: the importance for current clinical practice

Abstract: Objectives:We reviewed the literature on shared decision making (regarding treatments in psychiatry), with a view to informing our understanding of the decision making process and the barriers that...

Digital technology and clinical decision making in depression treatment: Current findings and future opportunities.

Abstract: Clinical decision making encompasses a broad set of processes that contribute to the effectiveness of depression treatments. There is emerging interest in using digital technologies to support effective and efficient clinical decision making. In this paper, we provide "snapshots" of research and current directions on ways that digital technologies can support clinical decision making in depression treatment. Practical facets of clinical decision making are reviewed, then research, design, and implementation opportunities where technology can potentially enhance clinical decision making are outlined. Discussions of these opportunities are organized around three established movements designed to enhance clinical decision making for depression treatment, including measurement-based care, integrated care, and personalized medicine. Research, design, and implementation efforts may support clinical decision making for depression by (1) improving tools to incorporate depression symptom data into existing electronic health record systems, (2) enhancing measurement of treatment fidelity and treatment processes, (3) harnessing smartphone and biosensor data to inform clinical decision making, (4) enhancing tools that support communication and care coordination between patients and providers and within provider teams, and (5) leveraging treatment and outcome data from electronic health record systems to support personalized depression treatment. The current climate of rapid changes in both healthcare and digital technologies facilitates an urgent need for research, design, and implementation of digital technologies that explicitly support clinical decision making. Ensuring that such tools are efficient, effective, and usable in frontline treatment settings will be essential for their success and will require engagement of stakeholders from multiple domains.

Delivering decision making support to people with cognitive disability - What has been learned from pilot programs in Australia from 2010 to 2015

Abstract: The UNCRPD has generated debate about supported decision making as a way to better enable people with cognitive disability to participate in decision making In Australia, between 2010–2015, a series of projects have piloted various models of delivering decision making support A critical review was conducted on the program documents and evaluations of these pilot projects The pilots were small scale, conducted by both statutory and non-statutory bodies, and adopted similar designs centred on supporting a decision maker/supporter dyad Primarily, participants were people with mild intellectual disability Themes included: positive outcomes; uncertain boundaries of decision support; difficulty securing supporters; positive value of program staff and support to supporters; limited experience and low expectations; and varying value of written resources The lack of depth and rigour of evaluations mean firm conclusions cannot be reached about program logics, costs or outcomes of the pilots The pilots demonstrate feasibility of providing support for decision making rather than resolving issues involved in delivering support They suggest that some form of authority may facilitate the role of decision supporters, help to engage others in a person's life, and integrate decision making support across all life domains

Navigating the Decision Space: Shared Medical Decision Making as Distributed Cognition:

Abstract: Despite increasing prominence, little is known about the cognitive processes underlying shared decision making. To investigate these processes, we conceptualize shared decision making as a form of distributed cognition. We introduce a Decision Space Model to identify physical and social influences on decision making. Using field observations and interviews, we demonstrate that patients and physicians in both acute and chronic care consider these influences when identifying the need for a decision, searching for decision parameters, making actionable decisions Based on the distribution of access to information and actions, we then identify four related patterns: physician dominated; physician-defined, patient-made; patient-defined, physician-made; and patient-dominated decisions. Results suggests that (a) decision making is necessarily distributed between physicians and patients, (b) differential access to information and action over time requires participants to transform a distributed task into a shared ...

Involving members of vulnerable populations in the development of patient decision aids: a mixed methods sequential explanatory study

Abstract: Patient decision aids aim to present evidence relevant to a health decision in understandable ways to support patients through the process of making evidence-informed, values-congruent health decisions. It is recommended that, when developing these tools, teams involve people who may ultimately use them. However, there is little empirical evidence about how best to undertake this involvement, particularly for specific populations of users such as vulnerable populations. To describe and compare the development practices of research teams that did and did not specifically involve members of vulnerable populations in the development of patient decision aids, we conducted a secondary analysis of data from a systematic review about the development processes of patient decision aids. Then, to further explain our quantitative results, we conducted semi-structured telephone interviews with 10 teams: 6 that had specifically involved members of vulnerable populations and 4 that had not. Two independent analysts thematically coded transcribed interviews. Out of a total of 187 decision aid development projects, 30 (16%) specifically involved members of vulnerable populations. The specific involvement of members of vulnerable populations in the development process was associated with conducting informal needs assessment activities (73% vs. 40%, OR 2.96, 95% CI 1.18–7.99, P = .02) and recruiting participants through community-based organizations (40% vs. 11%, OR 3.48, 95% CI 1.23–9.83, P = .02). In interviews, all developers highlighted the importance, value and challenges of involving potential users. Interviews with developers whose projects had involved members of vulnerable populations suggested that informal needs assessment activities served to center the decision aid around users’ needs, to better avoid stigma, and to ensure that the topic truly matters to the community. Partnering with community-based organizations may facilitate relationships of trust and may also provide a non-threatening and accessible location for research activities. There are a small number of key differences in the development processes for patient decision aids in which members of vulnerable populations were or were not specifically involved. Some of these practices may require additional time or resources. To address health inequities, researchers, communities and funders may need to increase awareness of these approaches and plan accordingly.

How Decision Support Systems Can Benefit from a Theory of Change Approach

Abstract: Decision support systems are now mostly computer and internet-based information systems designed to support land managers with complex decision-making. However, there is concern that many environmental and agricultural decision support systems remain underutilized and ineffective. Recent efforts to improve decision support systems use have focused on enhancing stakeholder participation in their development, but a mismatch between stakeholders’ expectations and the reality of decision support systems outputs continues to limit uptake. Additional challenges remain in problem-framing and evaluation. We propose using an outcomes-based approach called theory of change in conjunction with decision support systems development to support both wider problem-framing and outcomes-based monitoring and evaluation. The theory of change helps framing by placing the decision support systems within a wider context. It highlights how decision support systems use can “contribute” to long-term outcomes, and helps align decision support systems outputs with these larger goals. We illustrate the benefits of linking decision support systems development and application with a theory of change approach using an example of pest rabbit management in Australia. We develop a theory of change that outlines the activities required to achieve the outcomes desired from an effective rabbit management program, and two decision support systems that contribute to specific aspects of decision making in this wider problem context. Using a theory of change in this way should increase acceptance of the role of decision support systems by end-users, clarify their limitations and, importantly, increase effectiveness of rabbit management. The use of a theory of change should benefit those seeking to improve decision support systems design, use and, evaluation.

A systematic literature review and critical assessment of model-driven decision support for IT outsourcing

Abstract: Information technology outsourcing (ITO) is a widely-adopted strategy for IT governance. The decisions involved in IT outsourcing are complicated. Empirical research confirms that a rational and formalized decision-making process results in better decision outcomes. However, formal and systematic approaches for making ITO decisions appear to be scarce in practice. To support organizational decision-makers involved in IT outsourcing (including cloud sourcing), researchers have suggested several decision support methods. To date there is no comprehensive review and assessment of the research in this domain. In this study 133 model-driven decision support research articles for IT outsourcing and cloud sourcing were identified through a systematic literature review and assessed based on a highly-regarded research framework. An analysis of these 133 research articles suggested a range of Multiple Criteria Decision Making (MCDM), optimization and simulation methods to support different IT outsourcing decisions. Our findings raise concerns about the limited use of reference design theories, and the lack of validation and naturalistic evaluation of the decision support artifacts reported in ITO decision support literature. Based on the review, we provide future research directions, as well as a number of recommendations to enhance the rigor and relevance of ITO Decision Support Systems research. IT sourcing decision support researchers adopted diverse decision analysis methods.Use of naturalistic evaluation & reference theories is limited in IT sourcing research.Recommendation for development of IT sourcing decision support artifacts presented.

Decision quality and satisfaction: the effects of online information sources and self-efficacy

Abstract: Purpose Digital libraries and social media are two sources of online information with different characteristics. The purpose of this paper is to integrate self-efficacy into the analysis of the relationship between information sources and decision making, and to explore the effect of self-efficacy on decision making, as well as the interacting effect of self-efficacy and information sources on decision making. Design/methodology/approach Survey data were collected and the partial least squares structural equation modeling was employed to verify the research model. Findings The effect of digital library usage for acquiring information on perceived decision quality (PDQ) is larger than that of social media usage for acquiring information on PDQ. Self-efficacy in acquiring information (SEAI) stands out as the key determinant for PDQ. The effect of social media usage for acquiring information on PDQ is positively moderated by SEAI. Practical implications Decision making is a fundamental activity for individuals, but human decision making is often subject to biases. The findings of this study provide useful insights into decision quality improvement, highlighting the importance of SEAI in the face of information overload. Originality/value This study integrates self-efficacy into the analysis of the relationship between information sources and decision making, presenting a new perspective for decision-making research and practice alike.

Human Aspects in Software Architecture Decision Making: A Literature Review

Abstract: Despite past efforts, we have little understanding and limited research efforts on how architects make decisions in the real-world settings It seems that software architecture researchers make implicit assumption that decision making by software architects can be a rational and prescribed process Such an assumption is disputed in other fields such as economics and decision research This paper studies the current state of software architecture decision making research in terms of human behaviors and practice We carried out a literature review on software architecture decision making We classified papers into decision making behavior and decision making practice and identified the research relationships between them We found that decision making is a mental activity Research into the behavioral aspects of software architecture decision making for incorporation into architectural design practices is required We suggest three research topics on human aspects to improve software architecture practices

Managing Non-Homogeneous Information and Experts’ Psychological Behavior in Group Emergency Decision Making

Abstract: After an emergency event (EE) happens, emergency decision making (EDM) is a common and effective way to deal with the emergency situation, which plays an important role in mitigating its level of harm. In the real world, it is a big challenge for an individual emergency manager (EM) to make a proper and comprehensive decision for coping with an EE. Consequently, many practical EDM problems drive group emergency decision making (GEDM) problems whose main limitations are related to the lack of flexibility in knowledge elicitation, disagreements in the group and the consideration of experts’ psychological behavior in the decision process. Hence, this paper proposes a novel GEDM approach that allows more flexibility for preference elicitation under uncertainty, provides a consensus process to avoid disagreements and considers experts’ psychological behavior by using the fuzzy TODIM method based on prospect theory. Eventually, a group decision support system (GDSS) is developed to support the whole GEDM process defined in the proposed method demonstrating its novelty, validity and feasibility.

A Review of Shared Decision-Making and Patient Decision Aids in Radiation Oncology

Abstract: Cancer treatment decisions are complex and may be challenging for patients, as multiple treatment options can often be reasonably considered. As a result, decisional support tools have been developed to assist patients in the decision-making process. A commonly used intervention to facilitate shared decision-making is a decision aid, which provides evidence-based outcomes information and guides patients towards choosing the treatment option that best aligns with their preferences and values. To ensure high quality, systematic frameworks and standards have been proposed for the development of an optimal aid for decision making. Studies have examined the impact of these tools on facilitating treatment decisions and improving decision-related outcomes. In radiation oncology, randomized controlled trials have demonstrated that decision aids have the potential to improve patient outcomes, including increased knowledge about treatment options and decreased decisional conflict with decision-making. This article provides an overview of the shared-decision making process and summarizes the development, validation, and implementation of decision aids as patient educational tools in radiation oncology. Finally, this article reviews the findings from decision aid studies in radiation oncology and offers various strategies to effectively implement shared decision-making into clinical practice.