ABSTRACT
BACKGROUND: Nursing faces increasing pressure due to changing demographics and a shortage of skilled workers. Artificial intelligence (AI) offers an opportunity to relieve nurses and reduce pressure. The perception of AI by nurses is crucial for successful implementation. Due to a limited research state, our study aims to investigate nurses' knowledge and perceptions of AI. METHODS: In June 2023, we conducted a cross-sectional online survey of nurses in Bavaria, Germany. A convenience sample via care facilities was used for the questionnaire oriented on existing AI surveys. Data analysis was performed descriptively, and we used a template analysis to evaluate free-text answers. RESULTS: 114 (â67.5 %, â32.5 %) nurses participated. Results show that knowledge about AI is limited, as only 25.2 % can be described as AI experts. German nurses strongly associate AI with (i) computers and hardware, (ii) programming-based software, (iii) a database tool, (iv) learning, and (v) making decisions. Two-thirds of nurses report AI as an opportunity. Concerns arise as AI is seen as uncontrollable or threat. Administration staff are seen as the biggest profiteers. CONCLUSION: Even though there is a lack of clear understanding of AI technology among nurses, the majority recognizes the benefits that AI can bring in terms of relief or support. We suggest that nurses should be better prepared for AI in the future, e.g., through training and continuing education measures. Nurses are the working group that uses AI and are crucial for implementing nursing AI.
ABSTRACT
Transportation tasks in nursing are common, often overlooked, and directly impact patient care time in the context of staff shortages and an aging society. Current studies lack a specific focus on transportation tasks, a gap our research aims to fill. By providing detailed data on transportation needs in nursing, our study establishes a crucial foundation for the development and integration of assistive robots in clinical settings. In July and September 2023, we conducted weekly observations of nurses to assess clinical transportation needs. We aim to understand the economic impact and the methods nurses use for transportation tasks. We conducted a participant observation using a standardized app-based form over a seven-day observation period in two rural clinics. N = 1830 transports were made by nurses and examined by descriptive analysis. Non-medical supplies account for 27.05% (n = 495) of all transports, followed by medical supplies at 17.32% (n = 317), pharmacotherapy at 14.10% (n = 258) and other other categories like meals or drinks contributing 12.68% (n = 232). Most transports had a factual transport time of under a minute, with patient transport and lab samples displaying more variability. In total, 77.15% of all transports were made by hand. Requirements to collect items or connect transports with patient care were included in 5% of all transports. Our economic evaluation highlighted meals as the most costly transport, with 9596.16 per year in the observed clinics. Budget-friendly robots would amortize these costs over one year by transporting meals. We support understanding nurses' transportation needs via further research on assistive robots to validate our findings and determine the feasibility of transport robots.
ABSTRACT
Our cross-sectional study, conducted from October 2022 to January 2023, aims to assess post-COVID job satisfaction, crucial work dimensions, and self-reported factors influencing nursing retention. Using an online survey, we surveyed 2572 nurses in different working fields in Bavaria, Germany. We employed a quantitative analysis, including a multivariable regression, to assess key influence factors on nursing retention. In addition, we evaluated open-ended questions via a template analysis to use in a joint display. In the status quo, 43.2% of nurses were not committed to staying in the profession over the next 12 months. A total of 66.7% of our surveyed nurses were found to be dissatisfied with the (i) time for direct patient care. Sources of dissatisfaction above 50% include (ii) service organization, (iii) documentation, (iv) codetermination, and (v) payment. The qualitative data underline necessary improvements in these areas. Regarding retention factors, we identified that nurses with (i) older age, (ii) living alone, (iii) not working in elder care, (iv) satisfactory working hours, (v) satisfactory career choice, (vi) career opportunities, (vii) satisfactory payment, and (viii) adequate working and rest times are more likely to remain in the profession. Conversely, dissatisfaction in (ix) supporting people makes nurses more likely to leave their profession and show emotional constraints. We uncovered a dichotomy where nurses have strong empathy for their profession but yearn for improvements due to unmet expectations. Policy implications should include measures for younger nurses and those in elderly care. Nevertheless, there is a need for further research, because our research is limited by potential bias from convenience sampling, and digitalization will soon show up as a potential solution to improve, e.g., documentation and enhanced time for direct patient time.
ABSTRACT
Spectrometers measure diffuse reflectance and create a "molecular fingerprint" of the material under investigation. Ruggedized, small scale devices for "in-field" use cases exist. Such devices might for example be used by companies in the food supply chain for inward inspection of goods. However, their application for the industrial Internet of Things workflows or scientific research is limited due to their proprietary nature. We propose an open platform for visible and near-infrared technology (OpenVNT), an open platform for capturing, transmitting, and analysing spectral measurements. It is built for use in the field, as it is battery-powered and transmits data wireless. To achieve high accuracy, the OpenVNT instrument contains two spectrometers covering a wavelength range of 400-1700 nm. We conducted a study on white grapes to compare the performance of the OpenVNT instrument against the Felix Instruments F750, an established commercial instrument. Using a refractometer as ground truth, we built and validated models to estimate the Brix value. As a quality measure, we used coefficient of determination of the cross-validation (R2CV) between the instrument estimation and ground truth. With 0.94 for the OpenVNT and 0.97 for the F750, a comparable R2CV was achieved for both instruments. OpenVNT matches the performance of commercially available instruments at one tenth of the price. We provide an open bill of materials, building instructions, firmware, and analysis software to enable research and industrial IOT solutions without the limitations of walled garden platforms.
ABSTRACT
Age-associated organ failure and degenerative diseases have a major impact on human health. Cardiovascular dysfunction has an increasing prevalence with age and is one of the leading causes of death. In contrast to humans, zebrafish have extraordinary regeneration capacities of complex organs including the heart. In addition, zebrafish has recently become a model organism in research on aging. Here, we have compared the ventricular transcriptome as well as the regenerative capacity after cryoinjury of old and young zebrafish hearts. We identified the immune system as activated in old ventricles and found muscle organization to deteriorate upon aging. Our data show an accumulation of immune cells, mostly macrophages, in the old zebrafish ventricle. Those immune cells not only increased in numbers but also showed morphological and behavioral changes with age. Our data further suggest that the regenerative response to cardiac injury is generally impaired and much more variable in old fish. Collagen in the wound area was already significantly enriched in old fish at 7 days post injury. Taken together, these data indicate an 'inflammaging'-like process in the zebrafish heart and suggest a change in regenerative response in the old.
