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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In recent years, there has been an exponential increase in the use of health and sports-related smartphone applications (apps). This is also reflected in App-stores, which are stacked with thousands of health- and sports-apps, with new apps launched each day. These apps have great potential to monitor and support people’s physical activity and health. For users, however, it is difficult to know which app suits their needs. In this paper, we present an online tool that supports the decision-making process for choosing an appropriate app. We constructed and validated a screening instrument to assess app content quality, together with the assessment of users’ needs. Both served as input for building the tool through various iterations with prototypes and user tests. This resulted in an online tool which relies on app content quality scores to match the users’ needs with apps that score high in the screening instrument on those particular needs. Users can add new apps to the database via the screening instrument, making the tool self-supportive and future proof. A feedback loop allows users to give feedback on the recommended app and how well it meets their needs. This feedback is added to the database and used in future filtering and recommendations. The principles used can be applied to other areas of sports, physical activity and health to help users to select an app that suits their needs. Potentially increasing the long-term use of apps to monitor and to support physical activity and health.
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Sinds september 2015 is de ‘business rule management wereld’ / ‘decision management wereld’ weer een standaard rijker: The Decision Model and Notation (DMN). De Object Management Group (OMG) heeft deze nieuwe standaard uitgebracht met als doel een standaard taal te creëren om 1) requirements voor beslissingen en 2) de beslissingen zelf te modelleren. De adoptie van DMN heeft een wat lange aanloop gehad, maar begint nu serieuze vormen aan te nemen. Om deze reden brengen wij een vierdelige serie over DMN en het gebruik van DMN uit. In deze introductie, deel 1, gaan we in op de basis van The Decision Model and Notation.
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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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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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In 2015, the Object Management Group published the Decision Model and Notation with the goal to structure and connect business processes, decisions and underlying business logic. Practice shows that several vendors adopted the DMN standard and (started to) integrate the standard with their tooling. However, practice also shows that there are vendors who (consciously) deviate from the DMN standard while still trying to achieve the goal DMN is set out to reach. This research aims to 1) analyze and benchmark available tooling and their accompanied languages according to the DMN-standard and 2) understand the different approaches to modeling decisions and underlying business logic of these vendor specific languages. We achieved the above by analyzing secondary data. In total, 22 decision modelling tools together with their languages were analyzed. The results of this study reveal six propositions with regards to the adoption of DMN with regards to the sample of tools. These results could be utilized to improve the tools as well as the DMN standard itself to improve adoption. Possible future research directions comprise the improvement of the generalizability of the results by including more tools available and utilizing different methods for the data collection and analysis as well as deeper analysis into the generation of DMN directly from tool-native languages.
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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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In this thesis several studies are presented that have targeted decision making about case management plans in probation. In a case management plan probation officers describe the goals and interventions that should help offenders stop reoffending, and the specific measures necessary to reduce acute risks of recidivism and harm. Such a plan is embedded in a judicial framework, a sanction or advice about the sanction in which these interventions and measures should be executed. The topic of this thesis is the use of structured decision support, and the question is if this can improve decision making about case management plans in probation and subsequently improve the effectiveness of offender supervision. In this chapter we first sketch why structured decision making was introduced in the Dutch probation services. Next we describe the instrument for risk and needs assessment as well as the procedure to develop case management plans that are used by the Dutch probation services and that are investigated in this thesis. Then we describe the setting of the studies and the research questions, and we conclude with an overview of this thesis.
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Secondary school physical education (PE) teachers are continuously challenged to find ways to support students learning and motivate them for an active and healthy lifestyle. To address this complexity, continuing teacher professional development (TPD) is key. Technological tools can facilitate the effective delivery of TPD in this context. Successful implementation of this technology, however, is not self-evident. Based on the general aim of effectively integrating technologies in the educational process and focusing on the needs of educators, this study examines how the evidence-based theoretical TARGET framework for creating a motivating PE learning climate might be embedded into a digital professional development tool for PE teachers, useful in everyday practice. It presents a case study in which a multidisciplinary team of researchers, designers, and end-users iteratively went through several phases of need identification, idea generation, designing, development, and testing. By using a participatory approach, we were able to collect contextualized data and gain insights into users’ preferences, requirements, and ideas for designing and engaging with the tool. Based on these insights the TPD TARGET-tool for PE teachers was ultimately developed. The most prominent characteristics of this tool are (1) the combination of an evaluative function with teaching strategy support, (2) the strong emphasis on ease of use due to the complex PE teaching context, (3) the avoidance of social comparison, and suggestions of normative judgment, and (4) the allowance for a high level of customization and teacher autonomy.
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Developers of charging infrastructure, be it public or private parties, are highly dependent on accurate utilization data in order to make informed decisions where and when to expand charging points. The Amsterdam The Amsterdam University of Applied Sciences in close cooperation with the municipalities of Amsterdam, Rotterdam, The Hague, Utrecht and the metropolitan region of Amsterdam developed both the back- and front-end of a decision support tool. This paper describes the design of the decision support tool and its DataWareHouse architecture. The back-end is based on a monthly update of charging data with Charge point Detail Records and Meter Values enriched with location specific data. The design of the front-end is based on Key Performance Indicators used in the decision process for charging infrastructure roll-out. Implementing this design and DataWareHouse architecture allows all kinds of EV related companies and cities to start monitoring their charging infrastructure. It provides an overview of how the most important KPIs are being monitored and represented in the decision support tool based on regular interviews and decision processes followed by four major cities and a metropolitan region in the Netherlands.
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