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

## TL;DR

This review explores how artificial intelligence tools are being used to improve patient care during transitions between healthcare settings.

## Contribution

The study provides a comprehensive overview of AI applications in transitional care, highlighting their potential and limitations.

## Key findings

- AI tools are primarily used to improve discharge planning and follow-up care in transitional settings.
- Care continuity and complexity management are the most supported components of transitional care by AI tools.
- Challenges remain in integrating AI into clinical workflows and decision-making processes.

## Abstract

Care transitions, which involve the movement of patients between different care settings are critical moments in the care continuum but are often compromised by fragmented care delivery or poor information transfer among providers. To address this, Transitional Care (TC) programs were developed to address these challenges. Recently, Artificial Intelligence (AI) tools were introduced to support and streamline care transitions. However, their use in TC remains underexplored, highlighting the need to better understand their potential to optimize patient care and reduce adverse outcomes. This review aims to identify the current AI tools applied in TC, their usage to either prevent or improve care transitions, and their associated outcomes.

A scoping review was conducted following the Arksey and O'Malley framework. Web of Science, PubMed/MEDLINE, and IEEE Xplore were the searched databases, and eligible studies published between 2013 and 2025 were retrieved. Data were extracted from the included studies and mapped to the established categories of AI usages, as well as the eight components of comprehensive TC model. In addition, reported outcomes on the impact of AI on TC were retrieved.

Out of 211 studies identified, 21 were included. The retrieved twenty-one AI tools aimed at enhancing care transitions mostly from hospital to home settings. The majority of the AI tools were used to enhance TC by improving discharge planning, follow-up care, interoperability and system navigation. The components of comprehensive TC mostly promoted by AI tools were care continuity, complexity management, and patient and caregiver well-being. Patient engagement and education were the components least promoted by AI tools. Reported outcomes included rehospitalization rates, earlier prediction and diagnosis, and information exchange.

AI tools for TC are used to enhance care coordination, serving as a catalyst for delivering high-value care. Their application to care trajectories between multiple settings shows a promise for streamlining transitions and fostering patient engagement. However, although challenges lie in integrating these AI tools into clinical decision-making processes and workflows, they hold significant promise for enhancing TC.