Sahlgrenska Hospital
Gothenburg, Sweden
Location status: Recruiting
NCT Number: NCT07503665
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.
Interested in participating?
Request Info18 year and older
All sexes
Observational
Gothenburg, Sweden
Location status: Recruiting
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.
Healthy volunteers accepted: Yes
Only the study team can determine whether someone qualifies for participation.
Individual interviews with key actors in the implementation
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Individual interviews with managers
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Individual interviews with staff
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Observations of staff work
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Individual interviews with patients and family members:
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(for family members, only inclusion criterion 1b applies):
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Web-based surveys with staff:
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Retrospective medical record data:
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Learning labs:
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Not applicable- observational study
Time frame: From earliest January 2025 to latest December 2028
Time frame: Baseline, 12 months follow up, 24 months follow up
The NoMAD (Normalization Measure Development) instrument is a 23-item survey used to assess how complex interventions become implemented, embedded, and integrated into everyday healthcare practice. Grounded in Normalization Process Theory (NPT), it focuses on the collective work staff perform to make a new method part of routine care. Twenty of the items map onto the four core NPT constructs: coherence, which concerns how staff understand and make sense of the new practice; cognitive participation, which captures the work required to engage and involve people; collective action, which reflects the practical efforts needed to enact the intervention; and reflexive monitoring, which assesses how staff appraise its effects once it is in use. Three additional general items measure how "normal" the practice currently feels to users. NoMAD has demonstrated strong validity and reliability.
Time frame: Baseline, 12 months follow up, 24 months follow up
The Copenhagen Psychosocial Questionnaire (COPSOQ) III is an instrument designed to measure, assess, and manage the psychosocial work environment. The Swedish standard version of the tool evaluates 33 different dimensions of work life through 76 items, covering categories such as quantitative and emotional demands, social support, leadership quality, and health outcomes like stress and burnout. Validated at both individual and workplace levels, the instrument demonstrates strong reliability and construct validity, making it a robust tool for identifying psychosocial risks and evaluating organizational interventions. A key feature of its application in Sweden is the establishment of population-based benchmarks, which provide reference values that allow organizations to interpret their specific results in relation to the national average.
Time frame: Baseline, 12 months follow up, 24 months follow up
The Burnout Assessment Tool (BAT) is a validated, theory-based instrument for measuring work-related burnout. It assesses four core dimensions; exhaustion, mental distance, cognitive impairment, and emotional impairment, using 23 items supported by Rasch analysis, allowing the subscales to form one reliable overall score. The BAT performs consistently across demographic groups and enables conversion of ordinal scores into interval-level metrics, making it a precise tool for identifying burnout risk in both clinical and organizational settings.
Time frame: January 2026- December 2028
Contact information is provided by the study sponsor or research team.
Halmstad University
Other
Safe AI-assisted Fall Prevention Through Evidence
Acronym: SAFE
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View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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