AI-assisted fall prevention in hospital care

Safe AI-assisted Fall Prevention Through Evidence

This project will see if AI-driven sensor monitoring used in hospitals can help reduce patient falls and support staff who care for people at risk of falling.

Quick facts

• Study type: Observational
• Enrollment: 23425 (estimated)
• Ages: 18 Years and up
• Sex: All
• Sponsor: Halmstad University (Academic / other)
• Locations: 1 site (Gothenburg)
• Trial ID: NCT07503665 on ClinicalTrials.gov

What this trial studies

This multicentre, multimethod project follows the large-scale introduction of an AI-assisted, sensor-based fall-prevention system in hospitals in the Västra Götaland Region, Sweden, between 2026 and 2028. Researchers from Halmstad University and participating VGR hospitals will combine retrospective data analysis with surveys, interviews, observations, and two co‑design learning labs involving patients, relatives, and healthcare professionals. The study will track effects on patient safety outcomes (including fall rates), changes in healthcare workflows and staff tasks, and healthcare resource use across up to 2,400 hospital beds. Findings will identify barriers and enablers for sustainable integration and produce practical guidance for clinicians, managers, and policymakers.

Who should consider this trial

Good fit: Ideal candidates are adults admitted to participating Västra Götaland hospitals who are at risk of falls, and healthcare staff or managers involved in implementing the AI system who can communicate in Swedish.

Not a fit: Patients who are not treated at participating hospitals, those not at risk of falling, or people in hospitals that do not adopt the AI system are unlikely to receive benefit from this project.

Why it matters

Potential benefit: If successful, the system could reduce in-hospital falls and related injuries by alerting staff earlier and improving preventive care.

How similar studies have performed: Small pilot studies and sensor-based detection systems have shown promising fall reductions, but robust large-scale evidence for routine AI implementation in hospitals remains limited.

Eligibility 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

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 staff

Inclusion criteria:

1. Be employed as staff on a ward at one of the participating hospitals where the AI-assisted fall prevention has been decided to be implemented
2. Have experience with the implementation or use of the AI-assisted fall prevention
3. 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

Observations of staff work

Inclusion criteria:

1. Be employed as staff on a ward at one of the participating hospitals where the AI-assisted fall prevention has been decided to be implemented
2. Have experience with the implementation or use of the AI-assisted fall prevention
3. 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 patients and family members:

Inclusion criteria (for family members, only inclusion criterion 1b applies):

1. Either (a) have been a patient on a ward at one of the participating hospitals where the AI-assisted fall prevention has been implemented, or (b) be a family member involved in the care of a patient who meets the inclusion criteria but lacks the ability to provide informed consent. The family member must be able to understand and communicate in Swedish
2. Have experience with the AI-assisted fall prevention as part of their care
3. Have the ability to understand and communicate in Swedish
4. Be 18 years of age or older
5. Have the ability to provide informed consent. If there is any uncertainty regarding a patient's ability to provide informed consent, the research team will refrain from conducting the interview.

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
2. Lack the ability to provide informed consent

Web-based surveys with staff:

Inclusion criteria:

1. Be employed as staff on a ward at one of the participating hospitals where the AI-assisted fall prevention is decided to be implemented.

Exclusion criteria:

1. Not being employed on a ward at one of the participating hospitals where the AI-assisted fall prevention is decided to be implemented
2. Lack the ability to access information and respond to the survey in Swedish

Retrospective medical record data:

Inclusion criteria:

1\. Patients who have been cared for on a ward at one of the participating hospitals where the AI-assisted fall prevention has been implemented, either (a) up to 24 months after implementation or (b) up to 12 months before implementation.

Exclusion criteria:

1\. Patients who have only received care outside the defined time period, meaning not within 24 months after or 12 months before the implementation of the AI-assisted fall prevention

Learning labs:

Inclusion criteria:

1. (a) Be employed at one of the participating hospitals and hold a role as a key stakeholder in the implementation work, or be staff or a manager with experience of the implementation or use of the AI-assisted fall prevention; or (b1) have been a patient cared for on a ward at one of the participating hospitals where the AI-assisted fall prevention has been implemented and have experience with the AI-assisted fall prevention as part of their care; or (b2) be a family member of a patient with such experience; or (c) be a patient representative for a patient group, patient organization, or user organization where falls are an identified issue
2. 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
2. Lack the ability to provide informed consent

Where this trial is running

Gothenburg

• Sahlgrenska Hospital — Gothenburg, Sweden (Recruiting)

Study contacts

• Principal investigator: Elin Siira, PhD — Halmstad University
• Study coordinator: Project leader

Conditions Fall; fall prevention; artificial intelligence; hospital; work; patient safety; resource use; implementation