Background: The substitution of healthcare is a way to control rising healthcare costs. The Primary Care Plus (PC+) intervention of the Dutch ‘Blue Care’ pioneer site aims to achieve this feat by facilitating consultations with medical specialists in the primary care setting. One of the specialties involved is dermatology. This study explores referral decisions following dermatology care in PC+ and the influence of predictive patient and consultation characteristics on this decision. Methods: This retrospective study used clinical data of patients who received dermatology care in PC+ between January 2015 and March 2017. The referral decision following PC+, (i.e., referral back to the general practitioner (GP) or referral to outpatient hospital care) was the primary outcome. Stepwise logistic regression modelling was used to describe variations in the referral decisions following PC+, with patient age and gender, number of PC+ consultations, patient diagnosis and treatment specialist as the predicting factors. Results: A total of 2952 patients visited PC+ for dermatology care. Of those patients with a registered referral, 80.2% (N = 2254) were referred back to the GP, and 19.8% (N = 558) were referred to outpatient hospital care. In the multivariable model, only the treating specialist and patient’s diagnosis independently influenced the referral decisions following PC+. Conclusion: The aim of PC+ is to reduce the number of referrals to outpatient hospital care. According to the results, the treating specialist and patient diagnosis influence referral decisions. Therefore, the results of this study can be used to discuss and improve specialist and patient profiles for PC+ to further optimise the effectiveness of the initiative.
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Due to the ageing population, the prevalence of musculoskeletal disorders will continue to rise, as well as healthcare expenditure. To overcome these increasing expenditures, integration of orthopaedic care should be stimulated. The Primary Care Plus (PC+) intervention aimed to achieve this by facilitating collaboration between primary care and the hospital, in which specialised medical care is shifted to a primary care setting. The present study aims to evaluate the referral decision following orthopaedic care in PC+ and in particular to evaluate the influence of diagnostic tests on this decision. Therefore, retrospective monitoring data of patients visiting PC+ for orthopaedic care was used. Data was divided into two periods; P1 and P2. During P2, specialists in PC+ were able to request additional diagnostic tests (such as ultrasounds and MRIs). A total of 2,438 patients visiting PC+ for orthopaedic care were included in the analysis. The primary outcome was the referral decision following PC+ (back to the general practitioner (GP) or referral to outpatient hospital care). Independent variables were consultation- and patient-related predictors. To describe variations in the referral decision, logistic regression modelling was used. Results show that during P2, significantly more patients were referred back to their GP. Moreover, the multivariable analysis show a significant effect of patient age on the referral decision (OR 0.86, 95% CI = 0.81– 0.91) and a significant interaction was found between the treating specialist and the period (p = 0.015) and between patient’s diagnosis and the period (p < 0.001). Despite the significant impact of the possibility of requesting additional diagnostic tests in PC+, it is important to discuss the extent to which the availability of diagnostic tests fits within the vision of PC+. In addition, selecting appropriate profiles for specialists and patients for PC+ are necessary to further optimise the effectiveness and cost of care.
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Purpose: The increasing number of cancer survivors has heightened demands on hospital-based follow-up care resources. To address this, involving general practitioners (GPs) in oncological follow-up is proposed. This study explores secondary care providers’ views on integrating GPs into follow-up care for curatively treated breast and colorectal cancer survivors. Methods: A qualitative exploratory study was conducted using semi-structured interviews with Dutch medical specialists and nurse practitioners. Interviews were recorded, transcribed verbatim, and analyzed using thematic analysis by two independent researchers. Results: Fifteen medical specialists and nine nurse practitioners participated. They identified barriers such as re-referral delays, inexperience to perform structured follow-up, and worries about the lack of oncological knowledge among GPs. Benefits included the GPs’ accessibility and their contextual knowledge. For future organization, they emphasized the need for hospital logistics changes, formal GP training, sufficient case-load, proper staffing, remuneration, and time allocation. They suggested that formal GP involvement should initially be implemented for frail older patients and for prevalent cancer types. Conclusions: The interviewed Dutch secondary care providers generally supported formal involvement of primary care in cancer follow-up. A well-organized shared-care model with defined roles and clear coordination, supported by individual patients, was considered essential. This approach requires logistics adaptation, resources, and training for GPs. Implications for cancer survivors: Integrating oncological follow-up into routine primary care through a shared-care model may lead to personalized, effective, and efficient care for survivors because of their long-term relationships with GPs.
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Youth care is under increasing pressure, with rising demand, longer waiting lists, and growing staff shortages. In the Netherlands, one in seven children and adolescents is currently receiving youth care. At the same time, professionals face high workloads, burnout risks, and significant administrative burdens. This combination threatens both the accessibility and quality of care, leading to escalating problems for young people and families. Artificial intelligence (AI) offers promising opportunities to relieve these pressures by supporting professionals in their daily work. However, many AI initiatives in youth care fail to move beyond pilot stages, due to barriers such as lack of user acceptance, ethical concerns, limited professional ownership, and insufficient integration into daily practice. Empirical research on how AI can be responsibly and sustainably embedded in youth care is still scarce. This PD project aims to develop practice-based insights and strategies that strengthen the acceptance and long-term adoption of AI in youth care, in ways that support professional practice and contribute to appropriate care. The focus lies not on the technology itself, but on how professionals can work with AI within complex, high-pressure contexts. The research follows a cyclical, participatory approach, combining three complementary implementation frameworks: the Implementation Guide (Kaptein), the CFIR model (Damschroder), and the NASSS-CAT framework (Greenhalgh). Three case studies serve as core learning environments: (1) a speech-to-text AI tool to support clinical documentation, (2) Microsoft Copilot 365 for organization-wide adoption in support teams, and (3) an AI chatbot for parents in high-conflict divorces. Throughout the project, professionals, clients, ethical experts, and organizational stakeholders collaborate to explore the practical, ethical, and organizational conditions under which AI can responsibly strengthen youth care services.
In this project, we explore how healthcare providers and the creative industry can collaborate to develop effective digital mental health interventions, particularly for survivors of sexual assault. Sexual assault victims face significant barriers to seeking professional help, including shame, self-blame, and fear of judgment. With over 100,000 cases reported annually in the Netherlands the need for accessible, stigma-free support is urgent. Digital interventions, such as chatbots, offer a promising solution by providing a safe, confidential, and cost-effective space for victims to share their experiences before seeking professional care. However, existing commercial AI chatbots remain unsuitable for complex mental health support. While widely used for general health inquiries and basic therapy, they lack the human qualities essential for empathetic conversations. Additionally, training AI for this sensitive context is challenging due to limited caregiver-patient conversation data. A key concern raised by professionals worldwide is the risk of AI-driven chatbots being misused as therapy substitutes. Without proper safeguards, they may offer inappropriate responses, potentially harming users. This highlights the urgent need for strict design guidelines, robust safety measures, and comprehensive oversight in AI-based mental health solutions. To address these challenges, this project brings together experts from healthcare and design fields—especially conversation designers—to explore the power of design in developing a trustworthy, user-centered chatbot experience tailored to survivors' needs. Through an iterative process of research, co-creation, prototyping, and evaluation, we aim to integrate safe and effective digital support into mental healthcare. Our overarching goal is to bridge the gap between digital healthcare and the creative sector, fostering long-term collaboration. By combining clinical expertise with design innovation, we seek to develop personalized tools that ethically and effectively support individuals with mental health problems.
Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
