Nationwide and across the globe, the quality, affordability, and accessibility of home-based healthcare are under pressure. This issue stems from two main factors: the rapidly growing ageing population and the concurrent scarcity of healthcare professionals. Older people aspire to live independently in their homes for as long as possible. Additionally, governments worldwide have embraced policies promoting “ageing in place,” reallocating resources from institutions to homes and prioritising home-based services to honour the desire of older people to continue living at home while simultaneously addressing the rising costs associated with traditional institutional care.Considering the vital role of district nursing care and the fact that the population of older people in need of assistance at home is growing, it becomes clear that district nursing care plays a crucial role in primary care. The aim of this thesis is twofold: 1) to strengthen the evidence base for district nursing care; and 2) to explore the use of outcomes for learning and improving in district nursing care. The first part of this thesis examines the current delivery of district nursing care and explores its challenges during the COVID-19 pandemic to strengthen the evidence base and get a better understanding of district nursing care. Alongside the goal of strengthening the evidence for district nursing care, the second part of this thesis explores the use of patient outcomes for learning and improving district nursing care. It focuses on nurse-sensitive patient outcomes relevant to district nursing care, their current measurement in practice, and what is needed to use outcomes for learning and improving district nursing practice.
When it comes to hard to solve problems, the significance of situational knowledge construction and network coordination must not be underrated. Professional deliberation is directed toward understanding, acting and analysis. We need smart and flexible ways to direct systems information from practice to network reflection, and to guide results from network consultation to practice. This article presents a case study proposal, as follow-up to a recent dissertation about online simulation gaming for youth care network exchange (Van Haaster, 2014).
BackgroundSpecialist palliative care teams are consulted during hospital admission for advice on complex palliative care. These consultations need to be timely to prevent symptom burden and maintain quality of life. Insight into specialist palliative care teams may help improve the outcomes of palliative care.MethodsIn this retrospective observational study, we analyzed qualitative and quantitative data of palliative care consultations in a six-month period (2017 or 2018) in four general hospitals in the northwestern part of the Netherlands. Data were obtained from electronic medical records.ResultsWe extracted data from 336 consultations. The most common diagnoses were cancer (54.8%) and organ failure (26.8%). The estimated life expectancy was less than three months for 52.3% of all patients. Within two weeks after consultation, 53.2% of the patients died, and the median time until death was 11 days (range 191) after consultation. Most patients died in hospital (49.4%) but only 7.5% preferred to die in hospital. Consultations were mostly requested for advance care planning (31.6%). End-of-life preferences focused on last wishes and maintaining quality of life.ConclusionThis study provides detailed insight into consultations of palliative care teams and shows that even though most palliative care consultations were requested for advance care planning, consultations focus on end-of-life care and are more crisis-oriented than prevention-oriented. Death often occurs too quickly after consultation for end-of-life preferences to be met and these preferences tend to focus on dying. Educating healthcare professionals on when to initiate advance care planning would promote a more prevention-oriented approach. Defining factors that indicate the need for timely palliative care team consultation and advance care planning could help timely identification and consultation.
MULTIFILE
Size measurement plays an essential role for micro-/nanoparticle characterization and property evaluation. Due to high costs, complex operation or resolution limit, conventional characterization techniques cannot satisfy the growing demand of routine size measurements in various industry sectors and research departments, e.g., pharmaceuticals, nanomaterials and food industry etc. Together with start-up SeeNano and other partners, we will develop a portable compact device to measure particle size based on particle-impact electrochemical sensing technology. The main task in this project is to extend the measurement range for particles with diameters ranging from 20 nm to 20 um and to validate this technology with realistic samples from various application areas. In this project a new electrode chip will be designed and fabricated. It will result in a workable prototype including new UMEs (ultra-micro electrode), showing that particle sizing can be achieved on a compact portable device with full measuring range. Following experimental testing with calibrated particles, a reliable calibration model will be built up for full range measurement. In a further step, samples from partners or potential customers will be tested on the device to evaluate the application feasibility. The results will be validated by high-resolution and mainstream sizing techniques such as scanning electron microscopy (SEM), dynamic light scattering (DLS) and Coulter counter.
De druk op de wijkverpleging neemt toe. Zelfredzaamheid van cliënten kan deze druk verlichten, maar zorgprofessionals krijgen onvoldoende steun om dit te bereiken. Data Nurse ondersteunt verpleegkundigen op een datagedreven manier door waardevolle inzichten uit cliëntendossiers te benutten om de zelfredzaamheid van cliënten te vergroten en de zorg te verbeteren.
-Chatbots are being used at an increasing rate, for instance, for simple Q&A conversations, flight reservations, online shopping and news aggregation. However, users expect to be served as effective and reliable as they were with human-based systems and are unforgiving once the system fails to understand them, engage them or show them human empathy. This problem is more prominent when the technology is used in domains such as health care, where empathy and the ability to give emotional support are most essential during interaction with the person. Empathy, however, is a unique human skill, and conversational agents such as chatbots cannot yet express empathy in nuanced ways to account for its complex nature and quality. This project focuses on designing emotionally supportive conversational agents within the mental health domain. We take a user-centered co-creation approach to focus on the mental health problems of sexual assault victims. This group is chosen specifically, because of the high rate of the sexual assault incidents and its lifetime destructive effects on the victim and the fact that although early intervention and treatment is necessary to prevent future mental health problems, these incidents largely go unreported due to the stigma attached to sexual assault. On the other hand, research shows that people feel more comfortable talking to chatbots about intimate topics since they feel no fear of judgment. We think an emotionally supportive and empathic chatbot specifically designed to encourage self-disclosure among sexual assault victims could help those who remain silent in fear of negative evaluation and empower them to process their experience better and take the necessary steps towards treatment early on.
