In case of a major cyber incident, organizations usually rely on external providers of Cyber Incident Response (CIR) services. CIR consultants operate in a dynamic and constantly changing environment in which they must actively engage in information management and problem solving while adapting to complex circumstances. In this challenging environment CIR consultants need to make critical decisions about what to advise clients that are impacted by a major cyber incident. Despite its relevance, CIR decision making is an understudied topic. The objective of this preliminary investigation is therefore to understand what decision-making strategies experienced CIR consultants use during challenging incidents and to offer suggestions for training and decision-aiding. A general understanding of operational decision making under pressure, uncertainty, and high stakes was established by reviewing the body of knowledge known as Naturalistic Decision Making (NDM). The general conclusion of NDM research is that experts usually make adequate decisions based on (fast) recognition of the situation and applying the most obvious (default) response pattern that has worked in similar situations in the past. In exceptional situations, however, this way of recognition-primed decision-making results in suboptimal decisions as experts are likely to miss conflicting cues once the situation is quickly recognized under pressure. Understanding the default response pattern and the rare occasions in which this response pattern could be ineffective is therefore key for improving and aiding cyber incident response decision making. Therefore, we interviewed six experienced CIR consultants and used the critical decision method (CDM) to learn how they made decisions under challenging conditions. The main conclusion is that the default response pattern for CIR consultants during cyber breaches is to reduce uncertainty as much as possible by gathering and investigating data and thus delay decision making about eradication until the investigation is completed. According to the respondents, this strategy usually works well and provides the most assurance that the threat actor can be completely removed from the network. However, the majority of respondents could recall at least one case in which this strategy (in hindsight) resulted in unnecessary theft of data or damage. Interestingly, this finding is strikingly different from other operational decision-making domains such as the military, police and fire service in which there is a general tendency to act rapidly instead of searching for more information. The main advice is that training and decision aiding of (novice) cyber incident responders should be aimed at the following: (a) make cyber incident responders aware of how recognition-primed decision making works; (b) discuss the default response strategy that typically works well in several scenarios; (c) explain the exception and how the exception can be recognized; (d) provide alternative response strategies that work better in exceptional situations.
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Objective: To construct the underlying value structure of shared decision making (SDM) models. Method: We included previously identified SDM models (n = 40) and 15 additional ones. Using a thematic analysis, we coded the data using Schwartz’s value theory to define values in SDM and to investigate value relations. Results: We identified and defined eight values and developed three themes based on their relations: shared control, a safe and supportive environment, and decisions tailored to patients. We constructed a value structure based on the value relations and themes: the interplay of healthcare professionals’ (HCPs) and patients’ skills [Achievement], support for a patient [Benevolence], and a good relationship between HCP and patient [Security] all facilitate patients’ autonomy [Self-Direction]. These values enable a more balanced relationship between HCP and patient and tailored decision making [Universalism]. Conclusion: SDM can be realized by an interplay of values. The values Benevolence and Security deserve more explicit attention, and may especially increase vulnerable patients’ Self-Direction. Practice implications: This value structure enables a comparison of values underlying SDM with those of specific populations, facilitating the incorporation of patients’ values into treatment decision making. It may also inform the development of SDM measures, interventions, education programs, and HCPs when practicing.
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During the past two decades the implementation and adoption of information technology has rapidly increased. As a consequence the way businesses operate has changed dramatically. For example, the amount of data has grown exponentially. Companies are looking for ways to use this data to add value to their business. This has implications for the manner in which (financial) governance needs to be organized. The main purpose of this study is to obtain insight in the changing role of controllers in order to add value to the business by means of data analytics. To answer the research question a literature study was performed to establish a theoretical foundation concerning data analytics and its potential use. Second, nineteen interviews were conducted with controllers, data scientists and academics in the financial domain. Thirdly, a focus group with experts was organized in which additional data were gathered. Based on the literature study and the participants responses it is clear that the challenge of the data explosion consist of converting data into information, knowledge and meaningful insights to support decision-making processes. Performing data analyses enables the controller to support rational decision making to complement the intuitive decision making by (senior) management. In this way, the controller has the opportunity to be in the lead of the information provision within an organization. However, controllers need to have more advanced data science and statistic competences to be able to provide management with effective analysis. Specifically, we found that an important skill regarding statistics is the visualization and communication of statistical analysis. This is needed for controllers in order to grow in their role as business partner..
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Purpose: This study assesses the perceived quality of the outcomes in decision-making phases by using the conversational ai approach. Design/Method: The research adopts an experimental design to explore the impact of conversational artificial intelligence (AI) in the separate phases of a decision-making making process. This investigation is conducted through a team-level “Shark Tank” game, which serves as a dynamic platform. Findings: The data indicated a discernible pattern across all five steps of the decision-making process. The integration of AI, particularly Conversational AI, consistently improves the perceived quality of decisions, with varying degrees of impact at various stages. Originality/Value: This study contributes by particularly focusing on the conversational AI approach, to discern its efficacy in enhancing decision making processes. It is critical in contributing to the rapidly growing field of AI in decision making, providing a deeper understanding of AI's role in improving decision making quality.
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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.
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Introduction: Many older patients with cancer have their family members, often their adult children, involved in a process of treatment decision making. Despite the growing awareness that family members can facilitate a process of shared decision making (SDM), literature about SDM pays little attention to family relations and strategies to facilitate family involvement in decision making processes. Objective: This study aimed to 1. explore surgeons' and nurses' perceptions about involvement of adult children in treatment decision-making for older patients; and 2. identify strategies they use to ensure positive family involvement. Methods: Semi-structured interviews were conducted with 13 surgical oncologists and 13 oncology nurses in two Dutch hospitals. Qualitative content analysis was conducted according to the steps of thematic analysis. Results: Surgeons and nurses indicated that adult children's involvement in decision-making increases when patients become frail. They reported beneficial and challenging characteristics of this involvement. Six strategies to stimulate positive involvement of adult children in the decision-making process were revealed: 1. Focus on the patient; 2. Actively involve adult children; 3. Acknowledge different perspectives; 4. Get to know the family system; 5. Check that the patient and family members understand the information; and 6. Stimulate communication and deliberation with adult children.Conclusion: Surgeons and nurses perceive involvement of adult in treatment decision making as beneficial. However, family involvement can trigger specific complexities and challenges in treatment decision conversations that call for practical patient and family-centered strategies.
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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.
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People with dementia are confronted with many decisions. However, they are often not involved in the process of the decision-making. Shared Decision-Making (SDM) enables involvement of persons with dementia in the decision-making process. In our study, we develop a supportive IT application aiming to facilitate the decision-making process in care networks of people with dementia. A key feature in the development of this SDM tool is the participation of all network members during the design and development process, including the person with dementia. In this paper, we give insight into the first phases of this design and development process in which we conducted extensive user studies and translated wishes and needs of network members into user requirements
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Introduction: Shared decision-making is considered to be a key aspect of woman-centered care and a strategy to improve communication, respect, and satisfaction. This scoping review identified studies that used a shared decision-making support strategy as the primary intervention in the context of perinatal care. Methods: A literature search of PubMed, CINAHL, Cochrane Library, PsycINFO, and SCOPUS databases was completed for English-language studies conducted from January 2000 through November 2019 that examined the impact of a shared decision-making support strategy on a perinatal decision (such as choice of mode of birth after prior cesarean birth). Studies that only examined the use of a decision aid were excluded. Nine studies met inclusion criteria and were examined for the nature of the shared decision-making intervention as well as outcome measures such as decisional evaluation, including decisional conflict, decisional regret, and certainty. Results: The 9 included studies were heterogeneous with regard to shared decision-making interventions and measured outcomes and were performed in different countries and in a variety of perinatal situations, such as women facing the choice of mode of birth after prior cesarean birth. The impact of a shared decision-making intervention on women’s perception of shared decision-making and on their experiences of the decision-making process were mixed. There may be a decrease in decisional conflict and regret related to feeling informed, but no change in decisional certainty. Discussion: Despite the call to increase the use of shared decision-making in perinatal care, there are few studies that have examined the effects of a shared decision-making support strategy. Further studies that include antepartum and intrapartum settings, which include common perinatal decisions such as induction of labor, are needed. In addition, clear guidance and strategies for successfully integrating shared decision-making and practice recommendations would help women and health care providers navigate these complex decisions.
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Patients with a hematologic malignancy increasingly prefer to be actively involved in treatment decision-making. Shared decision-making (SDM), a process that supports decision-making in preference-sensitive decisions, fits well with this need. A decision is preference sensitive when well-informed patients considerably differ in their trade-offs between the pros and cons of one option, or if more equal treatment options are available, including no treatment. SDM involves several steps: the first is choice talk, where the professional informs the patient that a decision needs to be made between the various relevant options and that the patient's opinion is important. The second is option talk, where the professional explains the options and their pros and cons. In the third step, preference talk, the professional and the patient discuss the patient's preferences. The professional supports the patient in deliberation. The final step is decision talk, where the professional and patient discuss the patient's decisional role preference, make or defer the decision and discuss possible follow-up.
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