In the decision-making environment of evidence-based practice, the following three sources of information must be integrated: research evidence of the intervention, clinical expertise, and the patient’s values. In reality, evidence-based practice usually focuses on research evidence (which may be translated into clinical practice guidelines) and clinical expertise without considering the individual patient’s values. The shared decision-making model seems to be helpful in the integration of the individual patient’s values in evidence-based practice. We aim to discuss the relevance of shared decision making in chronic care and to suggest how it can be integrated with evidence-based practice in nursing. We start by describing the following three possible approaches to guide the decision-making process: the paternalistic approach, the informed approach, and the shared decision-making approach. Implementation of shared decision making has gained considerable interest in cases lacking a strong best-treatment recommendation, and when the available treatment options are equivalent to some extent. We discuss that in chronic care it is important to always invite the patient to participate in the decision-making process. We delineate the following six attributes of health care interventions in chronic care that influence the degree of shared decision making: the level of research evidence, the number of available intervention options, the burden of side effects, the impact on lifestyle, the patient group values, and the impact on resources. Furthermore, the patient’s willingness to participate in shared decision making, the clinical expertise of the nurse, and the context in which the decision making takes place affect the shared decision-making process. A knowledgeable and skilled nurse with a positive attitude towards shared decision making – integrated with evidence-based practice – can facilitate the shared decision-making process. We conclude that nurses as well as other health care professionals in chronic care should integrate shared decision making with evidence- based practice to deliver patient-centred care.
In this paper we explore the influence of the physical and social environment (the design space) son the formation of shared understanding in multidisciplinary design teams. We concentrate on the creative design meeting as a microenvironment for studying processes of design communication. Our applied research context entails the design of mixed physical–digital interactive systems supporting design meetings. Informed by theories of embodiment that have recently gained interest in cognitive science, we focus on the role of interactive “traces,” representational artifacts both created and used by participants as scaffolds for creating shared understanding. Our research through design approach resulted in two prototypes that form two concrete proposals of how the environment may scaffold shared understanding in design meetings. In several user studies we observed users working with our systems in natural contexts. Our analysis reveals how an ensemble of ongoing social as well as physical interactions, scaffolded by the interactive environment, grounds the formation of shared understanding in teams. We discuss implications for designing collaborative tools and for design communication theory in general.
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Proper decision-making is one of the most important capabilities of an organization. Therefore, it is important to have a clear understanding and overview of the decisions an organization makes. A means to understanding and modeling decisions is the Decision Model and Notation (DMN) standard published by the Object Management Group in 2015. In this standard, it is possible to design and specify how a decision should be taken. However, DMN lacks elements to specify the actors that fulfil different roles in the decision-making process as well as not taking into account the autonomy of machines. In this paper, we re-address and-present our earlier work [1] that focuses on the construction of a framework that takes into account different roles in the decision-making process, and also includes the extent of the autonomy when machines are involved in the decision-making processes. Yet, we extended our previous research with more detailed discussion of the related literature, running cases, and results, which provides a grounded basis from which further research on the governance of (semi) automated decision-making can be conducted. The contributions of this paper are twofold; 1) a framework that combines both autonomy and separation of concerns aspects for decision-making in practice while 2) the proposed theory forms a grounded argument to enrich the current DMN standard.
