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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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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Cybersecurity threat and incident managers in large organizations, especially in the financial sector, are confronted more and more with an increase in volume and complexity of threats and incidents. At the same time, these managers have to deal with many internal processes and criteria, in addition to requirements from external parties, such as regulators that pose an additional challenge to handling threats and incidents. Little research has been carried out to understand to what extent decision support can aid these professionals in managing threats and incidents. The purpose of this research was to develop decision support for cybersecurity threat and incident managers in the financial sector. To this end, we carried out a cognitive task analysis and the first two phases of a cognitive work analysis, based on two rounds of in-depth interviews with ten professionals from three financial institutions. Our results show that decision support should address the problem of balancing the bigger picture with details. That is, being able to simultaneously keep the broader operational context in mind as well as adequately investigating, containing and remediating a cyberattack. In close consultation with the three financial institutions involved, we developed a critical-thinking memory aid that follows typical incident response process steps, but adds big picture elements and critical thinking steps. This should make cybersecurity threat and incident managers more aware of the broader operational implications of threats and incidents while keeping a critical mindset. Although a summative evaluation was beyond the scope of the present research, we conducted iterative formative evaluations of the memory aid that show its potential.
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Artificial Intelligence systems are more and more being introduced into first response; however, this introduction needs to be done responsibly. While generic claims on what this entails already exist, more details are required to understand the exact nature of responsible application of AI within the first response domain. The context in which AI systems are applied largely determines the ethical, legal, and societal impact and how to deal with this impact responsibly. For that reason, we empirically investigate relevant human values that are affected by the introduction of a specific AI-based Decision Aid (AIDA), a decision support system under development for Fire Services in the Netherlands. We held 10 expert group sessions and discussed the impact of AIDA on different stakeholders. This paper presents the design and implementation of the study and, as we are still in process of analyzing the sessions in detail, summarizes preliminary insights and steps forward.
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Aims and objectives To gain insight into the perceived added value of a decision support App for district nurses and case managers intended to support a problem assessment and the provision of advices on possible solutions to facilitate ageing in place of people with dementia, and to investigate how they would implement the App in daily practice. Background District nurses and case managers play an important role in facilitating ageing in place of people with dementia (PwD). Detecting practical problems preventing PwD from living at home and advising on possible solutions is complex and challenging tasks for nurses and case managers. To support them with these tasks, a decision support App was developed. Methods A qualitative study using semi‐structured interviews was conducted. A photo‐elicitation method and an interview guide were used to structure the interviews. The data were analysed according to the principles of content analysis. Results In five interviews with seven district nurses and case managers, the added value was described in terms of five themes: (a) providing a broader/better overview of possible solutions; (b) providing a guideline/checklist for problem assessment and advice on solutions; (c) supporting an in‐depth problem assessment; (d) being a support tool for unexperienced case managers/district nurses; and (e) providing up‐to‐date information. The participants regarded the App as complementary to their current work procedure, which they would use in a flexible manner at different stages in the care continuum. Conclusions The participants valued both parts, the problem assessment and the overview of possible solutions. An important requisite for the usage would be that the content is continuously updated. Before implementation of the App can be recommended, an evaluation of its effectiveness regarding decision‐making should be conducted. Relevance to clinical practice This study underpins the need of nurses and case managers for decision support with regard to problem assessment and providing advices on possible solutions to facilitate ageing in place of PwD. There results also show the importance of listening to users experience and their perceived added value of decision support tools as this helps to explain the lack of statistically significant effects on quantitative outcome measure in contrast to a high willingness to use the App in a previous study.
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Clinical decision support systems (CDSSs) have gained prominence in health care, aiding professionals in decision-making and improving patient outcomes. While physicians often use CDSSs for diagnosis and treatment optimization, nurses rely on these systems for tasks such as patient monitoring, prioritization, and care planning. In nursing practice, CDSSs can assist with timely detection of clinical deterioration, support infection control, and streamline care documentation. Despite their potential, the adoption and use of CDSSs by nurses face diverse challenges. Barriers such as alarm fatigue, limited usability, lack of integration with workflows, and insufficient training continue to undermine effective implementation. In contrast to the relatively extensive body of research on CDSS use by physicians, studies focusing on nurses remain limited, leaving a gap in understanding the unique facilitators and barriers they encounter. This study aimed to explore the facilitators and barriers influencing the adoption and use of CDSSs by nurses in hospitals, using an extended Fit Between Individuals, Tasks, and Technology (FITT) framework.
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Background: Patient decision aids (PDAs) can support the treatment decision making process and empower patients to take a proactive role in their treatment pathway while using a shared decision-making (SDM) approach making participatory medicine possible. The aim of this study was to develop a PDA for prostate cancer that is accurate and user-friendly. Methods: We followed a user-centered design process consisting of five rounds of semi-structured interviews and usability surveys with topics such as informational/decisional needs of users and requirements for PDAs. Our userbase consisted of 8 urologists, 4 radiation oncologists, 2 oncology nurses, 8 general practitioners, 19 former prostate cancer patients, 4 usability experts and 11 healthy volunteers. Results: Informational needs for patients centered on three key factors: treatment experience, post-treatment quality of life, and the impact of side effects. Patients and clinicians valued a PDA that presents balanced information on these factors through simple understandable language and visual aids. Usability questionnaires revealed that patients were more satisfied overall with the PDA than clinicians; however, both groups had concerns that the PDA might lengthen consultation times (42 and 41%, respectively). The PDA is accessible on http://beslissamen.nl/. Conclusions: User-centered design provided valuable insights into PDA requirements but challenges in integrating diverse perspectives as clinicians focus on clinical outcomes while patients also consider quality of life. Nevertheless, it is crucial to involve a broad base of clinical users in order to better understand the decision-making process and to develop a PDA that is accurate, usable, and acceptable.
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Analyzing historical decision-related data can help support actual operational decision-making processes. Decision mining can be employed for such analysis. This paper proposes the Decision Discovery Framework (DDF) designed to develop, adapt, or select a decision discovery algorithm by outlining specific guidelines for input data usage, classifier handling, and decision model representation. This framework incorporates the use of Decision Model and Notation (DMN) for enhanced comprehensibility and normalization to simplify decision tables. The framework’s efficacy was tested by adapting the C4.5 algorithm to the DM45 algorithm. The proposed adaptations include (1) the utilization of a decision log, (2) ensure an unpruned decision tree, (3) the generation DMN, and (4) normalize decision table. Future research can focus on supporting on practitioners in modeling decisions, ensuring their decision-making is compliant, and suggesting improvements to the modeled decisions. Another future research direction is to explore the ability to process unstructured data as input for the discovery of decisions.
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Physical activity (PA) can have a substantial benefit in the prevention, treatment andrehabilitation of (breast) cancer. Wearable devices offer possibilities to monitor PA,to provide feedback and to set targets. Although the market for PA related wearabledevices is booming, the impact of these wearables is questionable. One of the mainconcerns is the limitations to address individual needs of users, among which specificgroups such as women with breast cancer. Through design, we see opportunities tostimulate for self-enhancement while encouraging PA after cancer treatment.Following a constructive design research approach, Aymée was designed. Aymée is asmart bra that changes its pattern based on the amount of PA. Through thisinteraction, Aymée aims to reinforce women recovering from breast cancer to feelgood about themselves and to be (more) active. In this paper, we describe both thedesign approach in co-creation with former breast-cancer patients, as well as firstresults. We also discuss implications for designing intelligent systems that address PAencouragement.
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The value of a decision can be increased through analyzing the decision logic, and the outcomes. The more often a decision is taken, the more data becomes available about the results. More available data results into smarter decisions and increases the value the decision has for an organization. The research field addressing this problem is Decision mining. By conducting a literature study on the current state of Decision mining, we aim to discover the research gaps and where Decision mining can be improved upon. Our findings show that the concepts used in the Decision mining field and related fields are ambiguous and show overlap. Future research directions are discovered to increase the quality and maturity of Decision mining research. This could be achieved by focusing more on Decision mining research, a change is needed from a business process Decision mining approach to a decision focused approach.
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