Artificial intelligence in transitional care: practice, promise, and pitfalls-a scoping review

Area of Science:

• Health Informatics
• Artificial Intelligence in Healthcare
• Patient Care Management

Background:

• Care transitions are critical but often fragmented, leading to adverse outcomes.
• Transitional Care (TC) programs aim to mitigate these issues.
• Artificial Intelligence (AI) offers potential solutions for optimizing TC.

Purpose of the Study:

• To identify AI tools used in TC.
• To analyze their application in preventing or improving care transitions.
• To assess the outcomes associated with AI in TC.

Main Methods:

• A scoping review was conducted using Arksey and O'Malley framework.
• Searched databases included Web of Science, PubMed/MEDLINE, and IEEE Xplore (2013-2025).
• Extracted data mapped AI usages and TC components, alongside reported outcomes.

Main Results:

• Twenty-one AI tools were identified, primarily for hospital-to-home transitions.
• AI improved discharge planning, follow-up care, interoperability, and system navigation.
• Key TC components enhanced were care continuity, complexity management, and patient well-being; patient engagement was least promoted.

Conclusions:

• AI tools enhance care coordination and high-value care delivery during transitions.
• AI shows promise in streamlining patient care trajectories and fostering engagement.
• Integration challenges exist, but AI holds significant potential for improving TC.