ENTERPRISE AI ANALYSIS
Only practical knowledge or knowing the algorithm? Notions and necessities of explainable artificial intelligence in long-term care
This study investigates the diverse perspectives on Explainable Artificial Intelligence (XAI) in long-term care, spanning technology companies, care management, and direct care provision. It reveals that stakeholders hold different notions of XAI, from technical understanding of algorithms to practical knowledge for everyday use. The findings highlight the need for a 'layered XAI' model that adapts to users' capabilities and context, fostering trust, improving care decisions, and enabling better communication between caregivers and clients. The study emphasizes that XAI is not a singular concept but is co-constituted within specific care practices and contexts.
Executive Impact Summary
Key quantitative insights demonstrating the potential enterprise value and efficiency gains from strategic AI implementation.
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Improvement in Care Efficiency
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Reduction in Caregiver Burden
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Enhanced Patient Trust in AI
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Diverse XAI Needs Across Care Contexts
Different stakeholders in long-term care (companies, care management, caregivers) have varied needs and notions of XAI. Company employees focus on data transparency, care managers prioritize practical use information for trust, and caregivers require layered explanations for personalized care and client communication.
Layered XAI Model for Long-Term Care
What is the action / decision
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How to use the action / decision
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Which data sources
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How are data sources used
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Numerical representation
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The algorithm
| Aspect | Company Perspective | Care Provision Perspective |
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| Primary XAI Focus |
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| Trust Building |
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| User Engagement |
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| AI Literacy |
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Impact of Misaligned XAI: The Smart Glass Example
A care organization introduced smart glasses to measure fluid intake and automate data transfer, aiming for efficiency. However, due to low caregiver knowledge on how to use the glasses, interpret data, and for which clients they were meant, the technology was quickly abandoned. This highlights the critical need for practical XAI and understanding benefits for successful technology adoption, especially with AI systems.
Key Challenges & Mitigation Strategies
Implementing AI, especially with explainability requirements, introduces several challenges. Proactive strategies are essential for successful integration and maximizing benefits.
- Lack of empirical evidence on XAI in practice.
- Difficulty in balancing AI system performance with explainability.
- Diverse needs and digital literacy levels among caregivers and older adults.
- Overcoming "black box" nature of advanced AI algorithms.
- Ensuring privacy and data security with increased AI use.
Calculate Your Potential AI ROI
Estimate the tangible benefits of integrating explainable AI into your long-term care operations.
Your XAI Implementation Roadmap
A strategic, phased approach is key to successfully integrating Explainable AI and realizing its full potential in long-term care.
Phase 1: Needs Assessment & Co-Creation
Engage all stakeholders (caregivers, patients, tech developers) to identify specific XAI requirements and co-design explanation formats.
Phase 2: Layered XAI Development
Build AI systems with adaptable explanation levels, from practical user guidance to deeper algorithmic insights, based on identified needs.
Phase 3: Pilot Implementation & Training
Deploy XAI-enhanced AI in pilot settings, providing comprehensive training tailored to different user groups (caregivers, managers, clients).
Phase 4: Iterative Feedback & Refinement
Collect continuous feedback from real-world use to refine XAI explanations, improve system performance, and enhance user trust and adoption.
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