NCT07503665

AI-assisted Fall Prevention Through Evidence

Safe AI-assisted Fall Prevention Through Evidence

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 23,425 (estimated)
• Sponsor: Halmstad University · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Accepted

Summary

The goal of this multi-method study is to investigate how AI-assisted fall-prevention are implemented in routine hospital care what their effects are. The main questions it aims to answer are how these AI systems influence patient safety outcomes, how they affect healthcare professionals work and healthcare resource use, and what factors support or hinder their sustainable integration into hospital environments.

Detailed description

Artificial intelligence (AI) offers new opportunities to strengthen patient safety, particularly in preventing in-hospital falls through real-time, sensor-based monitoring and alerts. As hospitals across Europe begin adopting these proactive fall-prevention technologies, evidence on their routine implementation and impact remains limited. The Safe AI assisted Fall Prevention through Evidence (SAFE) project aims to address this gap by examining the large-scale introduction of an AI-assisted fall prevention system in hospitals within the Västra Götaland Region (VGR), Sweden. Conducted between 2026 and 2028, the multicentre, multimethod project involves collaboration between Halmstad University and VGR hospitals, encompassing up to 2,400 patient beds. Using a multi-method design including surveys, interviews, observations, and a retrospective study, the project will follow the implementation process and evaluate effects on patient safety, healthcare workflows, and resource use multiple sites. Additionally, two learning labs will engage patients, relatives, and healthcare professionals to co-develop strategies that support sustainable system integration. The project will generate evidence-based insights and practical guidance for implementing AI-assisted fall prevention, with relevance for healthcare professionals, patients, hospital managers, and policymakers. While centred on VGR, the findings will offer valuable lessons for future initiatives in Sweden and internationally, contributing to the broader evidence base needed for responsible and scalable use of AI in healthcare fall prevention.

Conditions

• Fall

Interventions

Type	Name	Description
OTHER	Not applicable- observational study	Not applicable- observational study

Timeline

• Start date: 2026-01-01
• Primary completion: 2028-12-01
• Completion: 2028-12-01
• First posted: 2026-03-31
• Last updated: 2026-03-31

Locations

1 site across 1 country: Sweden