# Improving care interactions (and training) in nursing homes with artificial intelligence

## TL;DR

This paper explores how AI can improve communication and training in nursing homes by analyzing real-life care interactions.

## Contribution

The novel approach combines in situ data collection with machine learning to provide actionable insights for caregivers.

## Key findings

- Machine learning analysis of care interactions reveals key factors influencing care outcomes.
- Humor and caregiver experience significantly impact the success of care sessions.
- Training based on real-life data improves caregivers' communication and adaptability.

## Abstract

As the population continues to age, nursing homes will increasingly play a key role in caring for dependent individuals. To enhance the well-being of the elderly, it is crucial to focus on the language skills used during care interactions. However, issues such as the taboo surrounding dependency, scandals involving private nursing home management, the pressure for caregiver efficiency, and the variety of care contexts make monitoring these skills challenging. One way to address this is by collecting in situ data, supervised by language researchers and caregivers specialized in elderly care. This is the approach we have followed: the data collected was then analyzed using machine learning models to provide caregivers with crucial insights for improving care outcomes. Our research highlights the importance of specific factors in language-based interactions, especially in varied care situations. Notably, we emphasize the careful use of humor and the impact of caregiver experience on the success of care sessions. Consequently, we advocate for caregiver training that is grounded in real-life practice, focusing on context adaptation, active listening, and dialogue with residents.