Background: Due to the globally increasing demand for care, innovation is important to maintain quality, safety, effectiveness, patient sensitivity, and outcome orientation. Health care technologies could be a solution to innovate, maintain, or improve the quality of care and simultaneously decrease nurses’ workload. Currently, nurses are rarely involved in the design of health care technologies, mostly due to time constraints with clinical nursing responsibilities and limited exposure to technology and design disciplines. To ensure that health care technologies fit into nurses’ core and routine practice, nurses should be actively involved in the design process. Objective: The aim of the present study was to explore the main requirements for nurses’ active participation in the design of health care technologies. Design: An exploratory descriptive qualitative design was used which helps to both understand and describe a phenomenon. Participants: Twelve nurses from three academic hospitals in the Netherlands participated in this study. Method: Data were collected from semistructured interviews with hospital nurses experienced in design programs and thematically analysed. Results: Four themes were identified concerning the main requirements for nurses to participate in the design of health care technologies: (1) nurses’ motivations to participate, (2) the process of technology development, (3) required competence to participate (such as assertiveness, creative thinking, problem solving skills), and (4) facilitating and organizing nurses’ participation. Conclusion: Nurses experience their involvement in the design process as essential, distinctive, and meaningful but experience few possibilities to combine this work with their current workload, flows, routines, and requirements. To participate in the design of health care technologies nurses need motivation and specific competencies. Organizations should facilitate time for nurses to acquire the required competencies and to be intentionally involved in technology design and development activities.
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Abstract: AI tools in radiology are revolutionising the diagnosis, evaluation, and management of patients. However, there is a major gap between the large number of developed AI tools and those translated into daily clinical practice, which can be primarily attributed to limited usefulness and trust in current AI tools. Instead of technically driven development, little effort has been put into value-based development to ensure AI tools will have a clinically relevant impact on patient care. An iterative comprehensive value evaluation process covering the complete AI tool lifecycle should be part of radiology AI development. For value assessment of health technologies, health technology assessment (HTA) is an extensively used and comprehensive method. While most aspects of value covered by HTA apply to radiology AI, additional aspects, including transparency, explainability, and robustness, are unique to radiology AI and crucial in its value assessment. Additionally, value assessment should already be included early in the design stage to determine the potential impact and subsequent requirements of the AI tool. Such early assessment should be systematic, transparent, and practical to ensure all stakeholders and value aspects are considered. Hence, early value-based development by incorporating early HTA will lead to more valuable AI tools and thus facilitate translation to clinical practice. Clinical relevance statement: This paper advocates for the use of early value-based assessments. These assessments promote a comprehensive evaluation on how an AI tool in development can provide value in clinical practice and thus help improve the quality of these tools and the clinical process they support. Key Points: Value in radiology AI should be perceived as a comprehensive term including health technology assessment domains and AI-specific domains. Incorporation of an early health technology assessment for radiology AI during development will lead to more valuable radiology AI tools. Comprehensive and transparent value assessment of radiology AI tools is essential for their widespread adoption.
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Transitions in health care and the increasing pace at which technological innovations emerge, have led to new professional approach at the crossroads of health care and technology. In order to adequately deal with these transition processes and challenges before future professionals access the labour market, Fontys University of Applied Sciences is in a transition to combining education with interdisciplinary practice-based research. Fontys UAS is launching a new centre of expertise in Health Care and Technology, which is a new approach compared to existing educational structures. The new centre is presented as an example of how new initiatives in the field of education and research at the intersection of care and technology can be shaped.
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Both because of the shortcomings of existing risk assessment methodologies, as well as newly available tools to predict hazard and risk with machine learning approaches, there has been an emerging emphasis on probabilistic risk assessment. Increasingly sophisticated AI models can be applied to a plethora of exposure and hazard data to obtain not only predictions for particular endpoints but also to estimate the uncertainty of the risk assessment outcome. This provides the basis for a shift from deterministic to more probabilistic approaches but comes at the cost of an increased complexity of the process as it requires more resources and human expertise. There are still challenges to overcome before a probabilistic paradigm is fully embraced by regulators. Based on an earlier white paper (Maertens et al., 2022), a workshop discussed the prospects, challenges and path forward for implementing such AI-based probabilistic hazard assessment. Moving forward, we will see the transition from categorized into probabilistic and dose-dependent hazard outcomes, the application of internal thresholds of toxicological concern for data-poor substances, the acknowledgement of user-friendly open-source software, a rise in the expertise of toxicologists required to understand and interpret artificial intelligence models, and the honest communication of uncertainty in risk assessment to the public.
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This paper presents the results of an evaluation of a technology-supported leisure game for people with dementia in relation to the stimulation of social behavior.
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Purpose: The aims of this study were to investigate how a variety of research methods is commonly employed to study technology and practitioner cognition. User-interface issues with infusion pumps were selected as a case because of its relevance to patient safety. Methods: Starting from a Cognitive Systems Engineering perspective, we developed an Impact Flow Diagram showing the relationship of computer technology, cognition, practitioner behavior, and system failure in the area of medical infusion devices. We subsequently conducted a systematic literature review on user-interface issues with infusion pumps, categorized the studies in terms of methods employed, and noted the usability problems found with particular methods. Next, we assigned usability problems and related methods to the levels in the Impact Flow Diagram. Results: Most study methods used to find user interface issues with infusion pumps focused on observable behavior rather than on how artifacts shape cognition and collaboration. A concerted and theorydriven application of these methods when testing infusion pumps is lacking in the literature. Detailed analysis of one case study provided an illustration of how to apply the Impact Flow Diagram, as well as how the scope of analysis may be broadened to include organizational and regulatory factors. Conclusion: Research methods to uncover use problems with technology may be used in many ways, with many different foci. We advocate the adoption of an Impact Flow Diagram perspective rather than merely focusing on usability issues in isolation. Truly advancing patient safety requires the systematic adoption of a systems perspective viewing people and technology as an ensemble, also in the design of medical device technology.
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Background: In the past years, a mobile health (mHealth) app called the Dutch Talking Touch Screen Questionnaire (DTTSQ) was developed in The Netherlands. The aim of development was to enable Dutch physical therapy patients to autonomously complete a health-related questionnaire regardless of their level of literacy and digital skills. Objective: The aim of this study was to evaluate the usability (defined as the effectiveness, efficiency, and satisfaction) of the prototype of the DTTSQ for Dutch physical therapy patients with diverse levels of experience in using mobile technology. Methods: The qualitative Three-Step Test-Interview method, including both think-aloud and retrospective probing techniques, was used to gain insight into the usability of the DTTSQ. A total of 24 physical therapy patients were included. The interview data were analyzed using a thematic content analysis approach aimed at analyzing the accuracy and completeness with which participants completed the questionnaire (effectiveness), the time it took the participants to complete the questionnaire (efficiency), and the extent to which the participants were satisfied with the ease of use of the questionnaire (satisfaction). The problems encountered by the participants in this study were given a severity rating that was used to provide a rough estimate of the need for additional usability efforts. Results: All participants within this study were very satisfied with the ease of use of the DTTSQ. Overall, 9 participants stated that the usability of the app exceeded their expectations. The group of 4 average-/high-experienced participants encountered only 1 problem in total, whereas the 11 little-experienced participants encountered an average of 2 problems per person and the 9 inexperienced participants an average of 3 problems per person. A total of 13 different kind of problems were found during this study. Of these problems, 4 need to be addressed before the DTTSQ will be released because they have the potential to negatively influence future usage of the tool. The other 9 problems were less likely to influence future usage of the tool substantially. Conclusions: The usability of the DTTSQ needs to be improved before it can be released. No problems were found with satisfaction or efficiency during the usability test. The effectiveness needs to be improved by (1) making it easier to navigate through screens without the possibility of accidentally skipping one, (2) enabling the possibility to insert an answer by tapping on the text underneath a photograph instead of just touching the photograph itself, and (3) making it easier to correct wrong answers. This study shows the importance of including less skilled participants in a usability study when striving for inclusive design and the importance of measuring not just satisfaction but also efficiency and effectiveness during such studies.
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This study evaluates the maximum theoretical exposure to radiofrequency (RF) electromag- netic fields (EMFs) from a Fifth-generation (5G) New Radio (NR) base station (BS) while using four commonly used mobile applications: YouTube for video streaming, WhatsApp for voice calls, Instagram for posting pictures and videos, and running a Video game. Three factors that might affect exposure, i.e., distance of the measurement positions from the BS, measurement time, and induced traffic, were examined. Exposure was assessed through both instantaneous and time-averaged extrapolated field strengths using the Maximum Power Extrapolation (MPE) method. The former was calculated for every measured SS-RSRP (Secondary Synchronization Reference Signal Received Power) power sample obtained with a sampling resolution of 1 second, whereas the latter was obtained using a 1-min moving average applied on the applications’ instantaneous extrapolated field strengths datasets. Regarding distance, two measurement positions (MPs) were selected: MP1 at 56 meters and MP2 at 170 meters. Next, considering the measurement time, all mobile application tests were initially set to run for 30 minutes at both MPs, whereas the video streaming test (YouTube) was run for an additional 150 minutes to investigate the temporal evolution of field strengths. Considering the traffic, throughput data vs. both instantaneous and time-averaged extrapolated field strengths were observed for all four mobile applications. In addition, at MP1, a 30-minute test without a User Equipment (UE) device was conducted to analyze exposure levels in the absence of induced traffic. The findings indicated that the estimated field strengths for mobile applications varied. It was observed that distance and time had a more significant impact than the volume of data traffic generated (throughput). Notably, the exposure levels in all tests were considerably lower than the public exposure thresholds set by the ICNIRP guidelines.INDEX TERMS 5G NR, C-band, human exposure assessment, mobile applications, traffic data, maximum extrapolation method, RF-EMF.
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Adverse Outcome Pathways (AOPs) are conceptual frameworks that tie an initial perturbation (molecular initiat- ing event) to a phenotypic toxicological manifestation (adverse outcome), through a series of steps (key events). They provide therefore a standardized way to map and organize toxicological mechanistic information. As such, AOPs inform on key events underlying toxicity, thus supporting the development of New Approach Methodologies (NAMs), which aim to reduce the use of animal testing for toxicology purposes. However, the establishment of a novel AOP relies on the gathering of multiple streams of evidence and infor- mation, from available literature to knowledge databases. Often, this information is in the form of free text, also called unstructured text, which is not immediately digestible by a computer. This information is thus both tedious and increasingly time-consuming to process manually with the growing volume of data available. The advance- ment of machine learning provides alternative solutions to this challenge. To extract and organize information from relevant sources, it seems valuable to employ deep learning Natural Language Processing techniques. We review here some of the recent progress in the NLP field, and show how these techniques have already demonstrated value in the biomedical and toxicology areas. We also propose an approach to efficiently and reliably extract and combine relevant toxicological information from text. This data can be used to map underlying mechanisms that lead to toxicological effects and start building quantitative models, in particular AOPs, ultimately allowing animal-free human-based hazard and risk assessment.
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Clinical decision support systems (CDSSs) have gained prominence in health care, aiding professionals in decision-making and improving patient outcomes. While physicians often use CDSSs for diagnosis and treatment optimization, nurses rely on these systems for tasks such as patient monitoring, prioritization, and care planning. In nursing practice, CDSSs can assist with timely detection of clinical deterioration, support infection control, and streamline care documentation. Despite their potential, the adoption and use of CDSSs by nurses face diverse challenges. Barriers such as alarm fatigue, limited usability, lack of integration with workflows, and insufficient training continue to undermine effective implementation. In contrast to the relatively extensive body of research on CDSS use by physicians, studies focusing on nurses remain limited, leaving a gap in understanding the unique facilitators and barriers they encounter. This study aimed to explore the facilitators and barriers influencing the adoption and use of CDSSs by nurses in hospitals, using an extended Fit Between Individuals, Tasks, and Technology (FITT) framework.
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