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Objectives Artificial intelligence (AI)-based clinical decision support systems to aid diagnosis are increasingly being developed and implemented but with limited understanding of how such systems integrate with existing clinical work and organizational practices. We explored the early experiences of stakeholders using an AI-based imaging software tool Veye Lung Nodules (VLN) aiding the detection, classification, and measurement of pulmonary nodules in computed tomography scans of the chest. Materials and methods We performed semistructured interviews and observations across early adopter deployment sites with clinicians, strategic decision-makers, suppliers, patients with long-term chest conditions, and academics with expertise in the use of diagnostic AI in radiology settings. We coded the data using the Technology, People, Organizations, and Macroenvironmental factors framework. Results We conducted 39 interviews. Clinicians reported VLN to be easy to use with little disruption to the workflow. There were differences in patterns of use between experts and novice users with experts critically evaluating system recommendations and actively compensating for system limitations to achieve more reliable performance. Patients also viewed the tool positively. There were contextual variations in tool performance and use between different hospital sites and different use cases. Implementation challenges included integration with existing information systems, data protection, and perceived issues surrounding wider and sustained adoption, including procurement costs. Discussion Tool performance was variable, affected by integration into workflows and divisions of labor and knowledge, as well as technical configuration and infrastructure. Conclusion The socio-organizational factors affecting performance of diagnostic AI are under-researched and require attention and further research. [ABSTRACT FROM AUTHOR]

Abstract Background There is growing interest in assisted living technologies to support independence at home. Such technologies should ideally be designed ‘in the wild’ i.e. taking account of how real people live in real homes and communities. The ATHENE (Assistive Technologies for Healthy Living in Elders: Needs Assessment by Ethnography) project seeks to illuminate the living needs of older people and facilitate the co-production with older people of technologies and services. This paper describes the development of a cultural probe tool produced as part of the ATHENE project and how it was used to support home visit interviews with elders with a range of ethnic and social backgrounds, family circumstances, health conditions and assisted living needs. Method Thirty one people aged 60 to 98 were visited in their homes on three occasions. Following an initial interview, participants were given a set of cultural probe materials, including a digital camera and the ‘Home and Life Scrapbook’ to complete in their own time for one week. Activities within the Home and Life Scrapbook included maps (indicating their relationships to people, places and objects), lists (e.g. likes, dislikes, things they were concerned about, things they were comfortable with), wishes (things they wanted to change or improve), body outline (indicating symptoms or impairments), home plan (room layouts of their homes to indicate spaces and objects used) and a diary. After one week, the researcher and participant reviewed any digital photos taken and the content of the Home and Life Scrapbook as part of the home visit interview. Findings The cultural probe facilitated collection of visual, narrative and material data by older people, and appeared to generate high levels of engagement from some participants. However, others used the probe minimally or not at all for various reasons including limited literacy, physical problems (e.g. holding a pen), lack of time or energy, limited emotional or psychological resources, life events, and acute illness. Discussions between researchers and participants about the materials collected (and sometimes about what had prevented them completing the tasks) helped elicit further information relevant to assisted living technology design. The probe materials were particularly helpful when having conversations with non-English speaking participants through an interpreter. Conclusions Cultural probe methods can help build a rich picture of the lives and experiences of older people to facilitate the co-production of assisted living technologies. But their application may be constrained by the participant’s physical, mental and emotional capacity. They are most effective when used as a tool to facilitate communication and development of a deeper understanding of older people’s needs.

Purpose: To identify and explore factors that influence adoption, implementation and continued use of telecare technologies. Method: As part of the Assistive Technologies for Healthy Living in Elders: Needs Assessment by Ethnography (ATHENE) project, 16 semi-structured interviews were conducted with key participants from organisations involved in developing and providing telecare technologies and services. Data were analysed thematically, using a conceptual model of diffusion of innovations. Results: Participants identified numerous interacting factors that facilitated or hindered adoption and use. As predicted by the model, these related variously to the technology, individual adopters, the process of social influence, the innovativeness and readiness of organisations, implementation and routinisation processes following initial adoption, and the nature and strength of linkages between these elements. Key issues included (i) the complexity and uniqueness of the 'user system', (ii) the ongoing work needed to support telecare use beyond initial adoption, and (iii) the relatively weak links that typically exist between users of telecare technologies and the organisations who design and distribute them. Conclusions: Telecare is not merely a technology but a complex innovation requiring input from, and coordination between, people and organisations. To promote adoption and use, these contextual factors must be specified, understood and addressed. [ABSTRACT FROM AUTHOR]

Objectives: Artificial intelligence (AI)-based clinical decision support systems to aid diagnosis are increasingly being developed and implemented but with limited understanding of how such systems integrate with existing clinical work and organizational practices. We explored the early experiences of stakeholders using an AI-based imaging software tool Veye Lung Nodules (VLN) aiding the detection, classification, and measurement of pulmonary nodules in computed tomography scans of the chest., Materials and Methods: We performed semistructured interviews and observations across early adopter deployment sites with clinicians, strategic decision-makers, suppliers, patients with long-term chest conditions, and academics with expertise in the use of diagnostic AI in radiology settings. We coded the data using the Technology, People, Organizations, and Macroenvironmental factors framework., Results: We conducted 39 interviews. Clinicians reported VLN to be easy to use with little disruption to the workflow. There were differences in patterns of use between experts and novice users with experts critically evaluating system recommendations and actively compensating for system limitations to achieve more reliable performance. Patients also viewed the tool positively. There were contextual variations in tool performance and use between different hospital sites and different use cases. Implementation challenges included integration with existing information systems, data protection, and perceived issues surrounding wider and sustained adoption, including procurement costs., Discussion: Tool performance was variable, affected by integration into workflows and divisions of labor and knowledge, as well as technical configuration and infrastructure., Conclusion: The socio-organizational factors affecting performance of diagnostic AI are under-researched and require attention and further research., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.)