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.
Even though citizen and patient engagement in health research has a long tradition, citizen science in health has only recently gained attention and recognition. However, at present, there is no clear overview of the specifics and challenges of citizen science initiatives in the health domain. Such an overview could contribute to highlighting and articulating the different needs of stakeholders engaged in any form of citizen science in the health domain. It may also encourage the input of citizens and patients alike in health research and innovation, policy, and practice. This paper reports on a survey developed by the European Citizen Science Association (ECSA)’s Working Group “Citizen Science for Health,” to highlight the perceived characteristics and enabling factors of citizen science in the health domain, and to formulate a direction for future work and research. The survey was available in six languages and was open between January and August 2022. The majority of the 254 respondents were from European countries, and the largest stakeholder respondent group was researchers. Respondents were asked about their perspectives on the particular characteristics of citizen science performed in health and biomedical research, as well as the challenges and opportunities it affords. Ethics, the complexity of the health domain, and the overlap in roles whereby the researcher is sometimes also the subject of research, were the main issues suggested as being specific to citizen science in health. The top two areas that respondents identified as in need of development were “balanced return on investment” and “ethics.” This publication discusses these and other conditions with references to current literature.
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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.
