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.
A prototype of an indoor monoblock heat pump was tested at multiple ambient temperatures, to determine heat output, COP and the impact of defrosting events. Component efficiencies and the ice accumulation process were analysed. Options to improve performance were suggested.
Developing a framework that integrates Advanced Language Models into the qualitative research process.Qualitative research, vital for understanding complex phenomena, is often limited by labour-intensive data collection, transcription, and analysis processes. This hinders scalability, accessibility, and efficiency in both academic and industry contexts. As a result, insights are often delayed or incomplete, impacting decision-making, policy development, and innovation. The lack of tools to enhance accuracy and reduce human error exacerbates these challenges, particularly for projects requiring large datasets or quick iterations. Addressing these inefficiencies through AI-driven solutions like AIDA can empower researchers, enhance outcomes, and make qualitative research more inclusive, impactful, and efficient.The AIDA project enhances qualitative research by integrating AI technologies to streamline transcription, coding, and analysis processes. This innovation enables researchers to analyse larger datasets with greater efficiency and accuracy, providing faster and more comprehensive insights. By reducing manual effort and human error, AIDA empowers organisations to make informed decisions and implement evidence-based policies more effectively. Its scalability supports diverse societal and industry applications, from healthcare to market research, fostering innovation and addressing complex challenges. Ultimately, AIDA contributes to improving research quality, accessibility, and societal relevance, driving advancements across multiple sectors.
The Dutch floriculture is globally leading, and its products, knowledge and skills are important export products. New challenges in the European research agenda include sustainable use of raw materials such as fertilizer, water and energy, and limiting the use of pesticides. Greenhouse growers however have little control over crop growth conditions in the greenhouse at individual plant level. The purpose of this project, ‘HiPerGreen’, is to provide greenhouse owners with new methods to monitor the crop growth conditions in their greenhouse at plant level, compare the measured growth conditions and the measured growth with expected conditions and expected growth, to point out areas with deviations, recommend counter-measures and ultimately to increase their crop yield. The main research question is: How can we gather, process and present greenhouse crop growth parameters over large scale greenhouses in an economical way and ultimately improve crop yield? To provide an answer to this question, a team of university researchers and companies will cooperate in this applied research project to cover several different fields of expertise The application target is floriculture: the production of ornamental pot plants and cut flowers. Participating companies are engaged in the cultivation of pot plans, flowers and suppliers of greenhouse technology. Most of the parties fall in the SME (MKB) category, in line with the RAAK MKB objectives.Finally, the Demokwekerij and Hortipoint (the publisher of the international newsletter on floriculture) are closely involved. The project will develop new knowledge for a smart and rugged data infrastructure for growth monitoring and growth modeling in the greenhouse. In total the project will involve approximately 12 (teacher) researchers from the universities and about 60 students, who will work in the form of internships and undergraduate studies of interesting questions directly from the participating companies.
