Dataset Information

Social Innovation in Long-Term Care: Lessons from the Italian Case.

ABSTRACT: The debate on policies addressing the challenges posed by population ageing pays increasing attention to sustainable and innovative ways to tackle the multidimensional impact this phenomenon has on society and individuals. Moving from the findings of two European research projects, a qualitative study based on a rapid review of the literature, expert interviews, focus groups and case studies analysis has been carried out in Italy. This study illustrates which social innovations have been recently implemented in this country's long-term care (LTC) sector, and the areas in which further steps are urgently needed in the future. This takes place by first highlighting the existing links between social innovation and LTC, and then by identifying the key factors that can facilitate or hinder the implementation of these initiatives. Finally, the study suggests how to promote social innovation, by strengthening the "integration" and "coordination" of available services and resources, through a-for this country still relatively-new approach towards ageing, based on pillars such as prevention and education campaigns on how to promote well-being in older age.

SUBMITTER: Casanova G

PROVIDER: S-EPMC7177354 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Social Innovation in Long-Term Care: Lessons from the Italian Case.

Casanova Georgia G Principi Andrea A Lamura Giovanni G

International journal of environmental research and public health 20200331 7

The debate on policies addressing the challenges posed by population ageing pays increasing attention to sustainable and innovative ways to tackle the multidimensional impact this phenomenon has on society and individuals. Moving from the findings of two European research projects, a qualitative study based on a rapid review of the literature, expert interviews, focus groups and case studies analysis has been carried out in Italy. This study illustrates which social innovations have been recentl ...[more]

PMID: 32244446

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Project description:BackgroundAlthough the use of artificial intelligence (AI)-based technologies, such as AI-based decision support systems (AI-DSSs), can help sustain and improve the quality and efficiency of care, their deployment creates ethical and social challenges. In recent years, a growing prevalence of high-level guidelines and frameworks for responsible AI innovation has been observed. However, few studies have specified the responsible embedding of AI-based technologies, such as AI-DSSs, in specific contexts, such as the nursing process in long-term care (LTC) for older adults.ObjectivePrerequisites for responsible AI-assisted decision-making in nursing practice were explored from the perspectives of nurses and other professional stakeholders in LTC.MethodsSemistructured interviews were conducted with 24 care professionals in Dutch LTC, including nurses, care coordinators, data specialists, and care centralists. A total of 2 imaginary scenarios about AI-DSSs were developed beforehand and used to enable participants articulate their expectations regarding the opportunities and risks of AI-assisted decision-making. In addition, 6 high-level principles for responsible AI were used as probing themes to evoke further consideration of the risks associated with using AI-DSSs in LTC. Furthermore, the participants were asked to brainstorm possible strategies and actions in the design, implementation, and use of AI-DSSs to address or mitigate these risks. A thematic analysis was performed to identify the opportunities and risks of AI-assisted decision-making in nursing practice and the associated prerequisites for responsible innovation in this area.ResultsThe stance of care professionals on the use of AI-DSSs is not a matter of purely positive or negative expectations but rather a nuanced interplay of positive and negative elements that lead to a weighed perception of the prerequisites for responsible AI-assisted decision-making. Both opportunities and risks were identified in relation to the early identification of care needs, guidance in devising care strategies, shared decision-making, and the workload of and work experience of caregivers. To optimally balance the opportunities and risks of AI-assisted decision-making, seven categories of prerequisites for responsible AI-assisted decision-making in nursing practice were identified: (1) regular deliberation on data collection; (2) a balanced proactive nature of AI-DSSs; (3) incremental advancements aligned with trust and experience; (4) customization for all user groups, including clients and caregivers; (5) measures to counteract bias and narrow perspectives; (6) human-centric learning loops; and (7) the routinization of using AI-DSSs.ConclusionsThe opportunities of AI-assisted decision-making in nursing practice could turn into drawbacks depending on the specific shaping of the design and deployment of AI-DSSs. Therefore, we recommend considering the responsible use of AI-DSSs as a balancing act. Moreover, considering the interrelatedness of the identified prerequisites, we call for various actors, including developers and users of AI-DSSs, to cohesively address the different factors important to the responsible embedding of AI-DSSs in practice.

Dataset Information

Social Innovation in Long-Term Care: Lessons from the Italian Case.

Publications

Social Innovation in Long-Term Care: Lessons from the Italian Case.

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