Abstract Technology has a major impact on the way nurses work. Data-driven technologies, such as artificial intelligence (AI), have particularly strong potential to support nurses in their work. However, their use also introduces ambiguities. An example of such a technology is AI-driven lifestyle monitoring in long-term care for older adults, based on data collected from ambient sensors in an older adult’s home. Designing and implementing this technology in such an intimate setting requires collaboration with nurses experienced in long-term and older adult care. This viewpoint paper emphasizes the need to incorporate nurses and the nursing perspective into every stage of designing, using, and implementing AI-driven lifestyle monitoring in long-term care settings. It is argued that the technology will not replace nurses, but rather act as a new digital colleague, complementing the humane qualities of nurses and seamlessly integrating into nursing workflows. Several advantages of such a collaboration between nurses and technology are highlighted, as are potential risks such as decreased patient empowerment, depersonalization, lack of transparency, and loss of human contact. Finally, practical suggestions are offered to move forward with integrating the digital colleague
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From the article: Business rules management is a mean by which an organization realizes controllability of business activities to fulfill goals. Currently the focus of controllability is mainly on effectiveness, efficiency and output quality. Little attention is paid to risk, stakeholder concerns and high level goals. The purpose of this work is to present a viewpoint relating business rules management with concepts of risks, stakeholder, concerns and goals. The viewpoint is presented by means of a meta-model existing out of six concepts: stakeholder, concern, goal, business rule, requirements and implementation mechanism. In a case study the proposed view is validated in terms of completeness, usability and accuracy. Results illustrate the completeness, usability and a high degree of accuracy of our defined view. Future research is suggested on the development of a modeling language to improve the communicational value and ease of use of the meta-model.
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