How Prescriptive Analytics Influences Decision Making in Precision Medicine

Mosavi, Nasim Sadat; Santos, Manuel Filipe

doi:10.1016/j.procs.2020.10.073

Cited by 18 publications

(9 citation statements)

References 24 publications

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“…Furthermore, this study identified that the physical and medical challenges associated with aging, including susceptibility to diseases and limited accessibility to treatments, find their solutions in the integration of advancements. While telemedicine addresses logistical barriers and provides immediate medical care [104], precision medicine [105], and minimally invasive surgeries [106] offer advanced treatment options. Wearable health technology complements these medical interventions by providing real-time data and supporting continuous health monitoring [107].…”

Section: Discussionmentioning

confidence: 99%

Challenges and Advancements in the Health-Related Quality of Life of Older People

Rony,

Parvin,

Wahiduzzaman

et al. 2024

Advances in Public Health

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Background. With the global trend of increasing life expectancy, the health-related quality of life of older people haspersonalised become an area of significant interest. It is crucial to comprehend the advancements and identify the ongoing challenges in this domain for better health management and policy formation. Objectives. This scoping review aimed to comprehensively investigate the existing challenges and recent advancements associated with the quality of health among older people. Methods. We utilized Arksey and O’Malley’s scoping review framework for this study. A systematic literature search was performed across multiple databases, namely, PubMed, Scopus, and Google Scholar, targeting publications from 2018 to 2023. Selected studies were then rigorously examined based on predefined inclusion and exclusion criteria, followed by an in-depth thematic analysis. Results. Of the initial 406 identified studies, 72 were included in the final review. The analysis underscored enduring challenges in physical and medical issues, psychological difficulties, socioeconomic obstacles, and policy-related challenges. Meanwhile, significant advancements were noted in several areas, including technology, medicine, social and societal facets, and policy. Conclusions. Despite noteworthy improvements in the health-related quality of life of older individuals, the continued presence of substantial challenges emphasizes the need for more targeted initiatives. These results suggest an immediate requirement for revising policies and implementing interventions that specifically address identified challenges, with a particular emphasis on ensuring equitable health care access, effectively managing chronic conditions, and enhancing the training of geriatric health care professionals. This comprehensive review serves as a roadmap for guiding future research and shaping policy directions in the health care of older people.

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Section: Discussionmentioning

confidence: 99%

Challenges and Advancements in the Health-Related Quality of Life of Older People

Rony,

Parvin,

Wahiduzzaman

et al. 2024

Advances in Public Health

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“…As input for the interviews, two distinct imaginary scenarios were developed, outlining different roles for AI within AI-DSSs. The scenarios were inspired by the AI-DSS developed in the HAAL project, and leveraged on the distinction between descriptive, predictive and prescriptive data analytics algorithms [31,32]. The AI-DSSs in the first scenario included only descriptive analytical functions, while the second scenario involved a more advanced AI-DSSs including descriptive, predictive and prescriptive functions.…”

Section: Methodsmentioning

confidence: 99%

AI-Assisted Decision-Making in Long-Term Care: Qualitative Study on Prerequisites for Responsible Innovation (Preprint)

Lukkien,

Stolwijk,

Ipakchian Askari

et al. 2024

Preprint

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BACKGROUND While use of artificial intelligence (AI)-based technologies such as decision-support systems (AI-DSSs) could help sustaining and improving the quality and efficiency of care, their deployment also creates ethical and social challenges. In recent years, there has been a growing prevalence of high-level guidelines and frameworks to provide guidance on responsible AI innovation. However, few studies specify how AI-based technologies such as AI-DSSs can be responsibly embedded in specific contexts such as the nursing process in the long-term care (LTC) for older adults. OBJECTIVE Opportunities and prerequisites for responsible AI-assisted decision-making in the nursing process were explored from the perspectives of nurses and other professional stakeholders in LTC. METHODS Semi-structured interviews were conducted with 24 care professionals in Dutch LTC, including nurses, care coordinators, data specialists and care centralists. Two imaginary scenarios about the future use of AI-DSSs were developed beforehand and used to enable participants to articulate their expectations regarding the opportunities and risks of AI-assisted decision-making. After first openly discussing opportunities and possible risks associated with both scenarios, six high-level principles for responsible AI were used as probing themes to evoke further consideration on risks of using AI-DSSs in LTC. Further, participants were asked to brainstorm about possible strategies and actions in the design, implementation and use of AI-DSSs to address or mitigate the mentioned risks. A thematic analysis was carried out to identify opportunities and prerequisites for responsible innovation in this area. RESULTS Professionals’ stance towards 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 opportunities and prerequisites for responsible AI-assisted decision-making. Both opportunities and risks were identified in relation to early identification of care needs, guidance in devising care strategies, shared decision-making, and caregivers’ workload and work experience. To optimally balance opportunities and risks of AI-assisted decision-making, seven categories of prerequisites for responsible AI-assisted decision-making in the nursing process 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) routinization of using AI-DSSs. CONCLUSIONS Opportunities of AI-assisted decision-making in the nursing process could turn into drawbacks, depending on the specific shaping of the design and the deployment of AI-DSSs. Therefore, we recommend viewing the responsible use of AI-DSSs as a balancing act. Moreover, given the interrelatedness of the identified prerequisites, we call for various actors, including developers and users of AI-DSSs, to cohesively address different factors important to the responsible embedding of AI-DSSs in practice.

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“…The second scenario takes a more speculative and ambitious turn and involves a more advanced AI-DSS adding predictive and prescriptive functions. Predictive functions analyze data to forecast future outcomes, and prescriptive functions analyze data to recommend specific actions or strategies to help achieving specific outcomes [41,42]. Thus, in comparison to the first scenario, the second scenario adopts a more proactive approach in supporting decision making regarding personcentered care strategies.…”

Section: Methodsmentioning

confidence: 99%

AI-Assisted Decision-Making in Long-Term Care: Qualitative Study on Prerequisites for Responsible Innovation

Lukkien,

Stolwijk,

Ipakchian Askari

et al. 2024

JMIR Nursing

View full text Add to dashboard Cite

Background: While use of artificial intelligence (AI)-based technologies such as decision-support systems (AI-DSSs) could help sustaining and improving the quality and efficiency of care, their deployment also creates ethical and social challenges. In recent years, there has been a growing prevalence of high-level guidelines and frameworks to provide guidance on responsible AI innovation. However, few studies specify how AI-based technologies such as AI-DSSs can be responsibly embedded in specific contexts such as the nursing process in the long-term care (LTC) for older adults.Objective: Opportunities and prerequisites for responsible AI-assisted decision-making in the nursing process were explored from the perspectives of nurses and other professional stakeholders in LTC.Methods: Semi-structured interviews were conducted with 24 care professionals in Dutch LTC, including nurses, care coordinators, data specialists and care centralists. Two imaginary scenarios about the future use of AI-DSSs were developed beforehand and used to enable participants to articulate their expectations regarding the opportunities and risks of AI-assisted decision-making. After first openly discussing opportunities and possible risks associated with both scenarios, six high-level principles for responsible AI were used as probing themes to evoke further consideration on risks of using AI-DSSs in LTC. Further, participants were asked to brainstorm about possible strategies and actions in the design, implementation and use of AI-DSSs to address or mitigate the mentioned risks. A thematic analysis was carried out to identify opportunities and prerequisites for responsible innovation in this area.Results: Professionals' stance towards 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 opportunities and prerequisites for responsible AI-assisted decision-making. Both opportunities and risks were identified in relation to early identification of care needs, guidance in devising care strategies, shared decision-making, and caregivers' workload and work experience. To optimally balance opportunities and risks of AI-assisted decision-making, seven categories of prerequisites for responsible AIassisted decision-making in the nursing process 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) routinization of using AI-DSSs.Conclusions: Opportunities of AI-assisted decision-making in the nursing process could turn into drawbacks, depending on the specific shaping of the design and the deployment of AI-DSSs. Therefore, we recommend viewing the responsible use of AI-DSSs as a balancing act. Moreover, given the interrelatedness of the ide...

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How Prescriptive Analytics Influences Decision Making in Precision Medicine

Cited by 18 publications

References 24 publications

Challenges and Advancements in the Health-Related Quality of Life of Older People

Challenges and Advancements in the Health-Related Quality of Life of Older People

AI-Assisted Decision-Making in Long-Term Care: Qualitative Study on Prerequisites for Responsible Innovation (Preprint)

AI-Assisted Decision-Making in Long-Term Care: Qualitative Study on Prerequisites for Responsible Innovation

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