Abstract: The need for mental healthcare professionals in the Netherlands is increasing caused by the growth of patient complexity. The administration burden causes outflow of professionals and therefor they become increasingly scares. Improvement initiatives are aimed as the intended strategy and starts with (re)-structuring organizations through legislation and regulations. They entail both experienced and measured administration burden for healthcare professionals working in Long-Term Care (LTC). However, most studies only provide insight into the current administration burden or the impact of legislation and regulations on the administration burden from a broad perspective. These insights are useful to LTC managers, but more in-depth research is needed to implement laws and regulations to reduce the administration burden for LTC professionals in the future. The Compulsory Mental Healthcare Act (CMHA) was implemented in the Dutch mental healthcare and replaced the Special Admissions Act in Psychiatric Hospitals (SAAPH) on January 1, 2020. The aim of this study is to investigate the effect of the legislative transition and to determine the effect on the administration burden of Dutch mental healthcare professionals. A survey concerning the administration burden for especially psychiatrists before and after the transition was distributed to an addiction institute with a diversity of different mental healthcare professionals and a psychiatric institute that has been led by psychiatrists. Also some interviews with the lead professionals where held. The results show that the administration burden among psychiatrists has increased due to the contact with external healthcare providers and contact with the patient, family and their loved ones (a consequence of the amendment of the law). This effect was significant and in line with the results of the interviews. Therefor we conclude that the administration burden has increased as a result of the legislative amendment.
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Purpose To empirically define the concept of burden of neck pain. The lack of a clear understanding of this construct from the perspective of persons with neck pain and care providers hampers adequate measurement of this burden. An additional aim was to compare the conceptual model obtained with the frequently used Neck Disability Index (NDI). Methods Concept mapping, combining qualitative (nominal group technique and group consensus) and quantitative research methods (cluster analysis and multidimensional scaling), was applied to groups of persons with neck pain (n = 3) and professionals treating persons with neck pain (n = 2). Group members generated statements, which were organized into concept maps. Group members achieved consensus about the number and description of domains and the researchers then generated an overall mind map covering the full breadth of the burden of neck pain. Results Concept mapping revealed 12 domains of burden of neck pain: impaired mobility neck, neck pain, fatigue/concentration, physical complaints, psychological aspects/consequences, activities of daily living, social participation, financial consequences, difficult to treat/difficult to diagnose, difference of opinion with care providers, incomprehension by social environment, and how person with neck pain deal with complaints. All ten items of the NDI could be linked to the mind map, but the NDI measures only part of the burden of neck pain. Conclusion This study revealed the relevant domains for the burden of neck pain from the viewpoints of persons with neck pain and their care providers. These results can guide the identification of existing measurements instruments for each domain or the development of new ones to measure the burden of neck pain.
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Despite all improvement initiatives such as the national action plan [De-]Regulate Healthcare by the Dutch Ministry of Health, Welfare and Sport in 2018 to create more time for care within the Netherlands, the administrative burden for care workers is still increasing. Managers of healthcare institutes struggle with efficiently implementing government legislations in day-to-day operations. They indicate that the time spent on administrative tasks demanded by municipalities and national authorities is too much. In addition, they also indicate that there is a lack of consistency and uniformity when it comes to the way care workers handle administrative tasks. This way of working causes additional, and often ad hoc, work in the run-up to an audit. It seems that before laws and regulations are effectively implemented, new laws or regulations again demand attention. This looks like a vicious circle, but research to confirm this is not found yet. Therefore, the following research question is formulated: "What is the impact of laws and regulations on the administrative burden with regard to the primary and supportive processes of Dutch long-term care?" An explanatory multiple case study was conducted to answer the research question. Three case studies were carried out during September 2019 to January 2020. Based on these studies, we have concluded that between 29% and 62% of the total perceived administrative burden by long-term care professionals can be related to legislation.
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De technische en economische levensduur van auto’s verschilt. Een goed onderhouden auto met dieselmotor uit het bouwjaar 2000 kan technisch perfect functioneren. De economische levensduur van diezelfde auto is echter beperkt bij introductie van strenge milieuzones. Bij de introductie en verplichtstelling van geavanceerde rijtaakondersteunende systemen (ADAS) zien we iets soortgelijks. Hoewel de auto technisch gezien goed functioneert kunnen verouderde software, algorithmes en sensoren leiden tot een beperkte levensduur van de gehele auto. Voorbeelden: - Jeep gehackt: verouderde veiligheidsprotocollen in de software en hardware beperkten de economische levensduur. - Actieve Cruise Control: sensoren/radars van verouderde systemen leiden tot beperkte functionaliteit en gebruikersacceptatie. - Tesla: bij bestaande auto’s worden verouderde sensoren uitgeschakeld waardoor functies uitvallen. In 2019 heeft de EU een verplichting opgelegd aan automobielfabrikanten om 20 nieuwe ADAS in te bouwen in nieuw te ontwikkelen auto’s, ongeacht prijsklasse. De mate waarin deze ADAS de economische levensduur van de auto beperkt is echter nog onvoldoende onderzocht. In deze KIEM wordt dit onderzocht en wordt tevens de parallel getrokken met de mobiele telefonie; beide maken gebruik van moderne sensoren en software. We vergelijken ontwerpeisen van telefoons (levensduur van gemiddeld 2,5 jaar) met de eisen aan moderne ADAS met dezelfde sensoren (levensduur tot 20 jaar). De centrale vraag luidt daarom: Wat is de mogelijke impact van veroudering van ADAS op de economische levensduur van voertuigen en welke lessen kunnen we leren uit de onderliggende ontwerpprincipes van ADAS en Smartphones? De vraag wordt beantwoord door (i) literatuuronderzoek naar de veroudering van ADAS (ii) Interviews met ontwerpers van ADAS, leveranciers van retro-fit systemen en ontwerpers van mobiele telefoons en (iii) vergelijkend rij-onderzoek naar het functioneren van ADAS in auto’s van verschillende leeftijd en prijsklassen.
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.
Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
