Enterprise AI Analysis
Artificial intelligence-based approaches for advance care planning: a scoping review
This scoping review examines the current state of AI applications in Advance Care Planning (ACP), highlighting their potential to optimize patient identification for ACP, aid decision-making, and streamline processes. The review emphasizes that while AI models show promising performance, significant challenges remain regarding data transparency, code availability, and generalizability. It identifies a critical need for future research to prioritize open-source practices, develop AI-driven revision systems for ACP documentation, and ensure culturally sensitive AI tools to enhance patient engagement and equitable ACP practices.
Key Executive Impacts
The analysis reveals that 95.1% of studies focus on identifying patients for ACP, with 77% reporting good to excellent model performance. However, 78.04% of studies lack data or source code availability, posing significant challenges for transparency and reproducibility.
0
Studies focus on patient identification for ACP
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Studies reporting good to excellent model performance
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Studies lacking data transparency or code availability
Deep Analysis & Enterprise Applications
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AI's Role in Optimizing ACP
AI holds significant promise in refining Advance Care Planning (ACP) by supporting the timely identification of suitable patients, enhancing predictive accuracy for future care needs, and personalizing information to individual values. Such applications can significantly streamline decision-making processes, ensuring patient-centered care and improving overall healthcare efficiency.
95.1%
of studies focus on identifying patients who may benefit from ACP.
AI's Impact Across ACP Phases
Identify ACP Candidates
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Initiate Discussion
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Documentation & Sharing ACP
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Identifying Records
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Accessing & Using ACP
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Reviewing & Updating ACP
The majority of studies focused on predicting patient outcomes such as survival, hospital length of stay, or the likelihood of specific events, underlining AI's role in proactive care planning. This often involves categorizing data into discrete classes or groups, or identifying patterns and relationships within the data for descriptive purposes, or finding optimal solutions for treatment plans or resource allocation.
Addressing Data & Transparency Gaps
Despite promising AI model performance, the widespread adoption of AI in ACP faces substantial hurdles, particularly concerning data and code availability. A lack of transparency can impede reproducibility and trust, while the reliance on proprietary datasets limits independent assessment and raises concerns about algorithmic bias. Bridging these gaps is crucial for robust, equitable, and generalizable AI applications in ACP.
78.04%
of studies lack data or source code availability, hindering transparency and reproducibility.
| Aspect | Current State | Ideal State |
|---|---|---|
| Data Availability | Often proprietary or unavailable upon request | Open datasets for replication and analysis |
| Source Code | Rarely shared, hindering verification | Publicly available for scrutiny and collaboration |
| Bias Assessment | Difficult without inspectable data | Routinely assessed through open and diverse datasets |
| Model Validation | Limited to reported metrics | Independent validation by multiple researchers |
The quality and diversity of training data are crucial for model performance and generalizability. The review identified only one study providing an open dataset, underscoring a significant challenge. Future research needs to prioritize open-source practices to enhance transparency, collaboration, and the overall quality of AI-based ACP approaches.
Innovating for Patient-Centered ACP
Future AI research in ACP must focus on developing culturally sensitive tools that understand diverse communication styles and beliefs, moving beyond predictive models to actively support core ACP elements like preference elicitation and shared decision-making. Continuous, AI-driven revision systems are also essential to ensure ACP documents remain living, responsive records of patient wishes.
Culturally Sensitive AI for Diverse ACP Needs
AI systems must be intentionally designed to accommodate a wide range of cultural backgrounds and preferences, as choices in end-of-life care are deeply shaped by cultural factors. This involves utilizing diverse datasets, personalizing ACP content based on cultural or religious affiliation, and ensuring systems can interpret and respond to culturally nuanced terms. Co-design with patients and communities from diverse backgrounds is critical to avoid culturally inappropriate discussions and foster trust.
There is a notable gap in literature concerning AI's role in supporting the reviewing and updating of ACP documentation. AI models have the potential to pick up clinical changes in real-time and facilitate timely review. Patient-directed chatbots or conversational agents could promote reflection and revision, allowing ACP to become a continuous process rather than a one-time event, adapting to evolving health status and preferences.
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