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.
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.
Aim To provide insight into the basic characteristics of decision making in the treatment of symptomatic severe aortic stenosis (SSAS) in Dutch heart centres with specific emphasis on the evaluation of frailty, cognition, nutritional status and physical functioning/functionality in (instrumental) activities of daily living [(I)ADL]. Methods A questionnaire was used that is based on the European and American guidelines for SSAS treatment. The survey was administered to physicians and non-physicians in Dutch heart centres involved in the decision-making pathway for SSAS treatment. Results All 16 Dutch heart centres participated. Before a patient case is discussed by the heart team, heart centres rarely request data from the referring hospital regarding patients’ functionality (n = 5), frailty scores (n = 0) and geriatric consultation (n = 1) as a standard procedure. Most heart centres ‘often to always’ do their own screening for frailty (n = 10), cognition/mood (n = 9), nutritional status (n = 10) and physical functioning/functionality in (I)ADL (n = 10). During heart team meetings data are ‘sometimes to regularly’ available regarding frailty (n = 5), cognition/mood (n = 11), nutritional status (n = 8) and physical functioning/functionality in (I)ADL (n = 10). After assessment in the outpatient clinic patient cases are re-discussed ‘sometimes to regularly’ in heart team meetings (n = 10). Conclusions Dutch heart centres make an effort to evaluate frailty, cognition, nutritional status and physical functioning/functionality in (I)ADL for decision making regarding SSAS treatment. However, these patient data are not routinely requested from the referring hospital and are not always available for heart team meetings. Incorporation of these important data in a structured manner early in the decision-making process may provide additional useful information for decision making in the heart team meeting.
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Developing a framework that integrates Advanced Language Models into the qualitative research process.Qualitative research, vital for understanding complex phenomena, is often limited by labour-intensive data collection, transcription, and analysis processes. This hinders scalability, accessibility, and efficiency in both academic and industry contexts. As a result, insights are often delayed or incomplete, impacting decision-making, policy development, and innovation. The lack of tools to enhance accuracy and reduce human error exacerbates these challenges, particularly for projects requiring large datasets or quick iterations. Addressing these inefficiencies through AI-driven solutions like AIDA can empower researchers, enhance outcomes, and make qualitative research more inclusive, impactful, and efficient.The AIDA project enhances qualitative research by integrating AI technologies to streamline transcription, coding, and analysis processes. This innovation enables researchers to analyse larger datasets with greater efficiency and accuracy, providing faster and more comprehensive insights. By reducing manual effort and human error, AIDA empowers organisations to make informed decisions and implement evidence-based policies more effectively. Its scalability supports diverse societal and industry applications, from healthcare to market research, fostering innovation and addressing complex challenges. Ultimately, AIDA contributes to improving research quality, accessibility, and societal relevance, driving advancements across multiple sectors.
The energy transition is a highly complex technical and societal challenge, coping with e.g. existing ownership situations, intrusive retrofit measures, slow decision-making processes and uneven value distribution. Large scale retrofitting activities insulating multiple buildings at once is urgently needed to reach the climate targets but the decision-making of retrofitting in buildings with shared ownership is challenging. Each owner is accountable for his own energy bill (and footprint), giving a limited action scope. This has led to a fragmented response to the energy retrofitting challenge with negligible levels of building energy efficiency improvements conducted by multiple actors. Aggregating the energy design process on a building level would allow more systemic decisions to happen and offer the access to alternative types of funding for owners. “Collect Your Retrofits” intends to design a generic and collective retrofit approach in the challenging context of monumental areas. As there are no standardised approaches to conduct historical building energy retrofits, solutions are tailor-made, making the process expensive and unattractive for owners. The project will develop this approach under real conditions of two communities: a self-organised “woongroep” and a “VvE” in the historic centre of Amsterdam. Retrofit designs will be identified based on energy performance, carbon emissions, comfort and costs so that a prioritisation strategy can be drawn. Instead of each owner investing into their own energy retrofitting, the neighbourhood will invest into the most impactful measures and ensure that the generated economic value is retained locally in order to make further sustainable investments and thus accelerating the transition of the area to a CO2-neutral environment.
The impacts of tourism on destinations and the perceptions of local communities have been a major concern both for the industry and research in the past decades. However, tourism planning has been mainly focused on traditions that promote the increase of tourism without taking under consideration the wellbeing of both residents and visitors. To develop a more sustainable tourism model, the inclusion of local residents in tourism decision-making is vital. However, this is not always possible due to structural, economic and socio-cultural restrictions that residents face resulting to their disempowerment. This study aims to explore and interpret the formal processes around tourism decision-making and community empowerment in urban settings. The research proposes a comparative study of three urban destinations in Europe (The Hague in the Netherlands, San Sebastian in Spain and, Ioannina in Greece) that experience similar degree of tourism growth. The proposed study will use a design-based approach in order to understand tourism decision-making and what empowers or disempowers community participation within the destinations. Based on the findings of primary and secondary data, a community empowerment model will be applied in one the destinations as a pilot for resident engagement in tourism planning. The evaluation of the pilot will allow for an optimized model to be created with implications for tourism planning at a local level that can contribute to sustainable destinations that safeguard the interests of local residents and tourists.
