Fall
Conditions
Keywords
fall prevention, artificial intelligence, hospital, work, patient safety, resource use, implementation, effects
Brief 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.
Interventions
OTHERNot applicable- observational study
Not applicable- observational study
Sponsors
Halmstad University
Study design
Observational model
OTHER
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
Individual interviews with key actors in the implementation Inclusion criteria: 1. Be employed at one of the participating hospitals 2. Hold a role as a key stakeholder in the implementation work 3. Have experience with the implementation of the AI-assisted fall prevention 4. Have the ability to understand and communicate in Swedish
Exclusion criteria
1\. Have insufficient proficiency in Swedish to participate in an interview or observation and to understand the purpose and content of the study Individual interviews with managers Inclusion criteria: 1. Be employed as a manager at one of the participating hospitals 2. Have experience with the implementation or use of the AI-assisted fall prevention 3. Have the ability to understand and communicate in Swedish
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Fall rate | From earliest January 2025 to latest December 2028 |
Countries
Sweden
Contacts
CONTACTProject leader
PRINCIPAL_INVESTIGATORElin Siira, PhD
Halmstad University
Outcome results
None listed