NCT07078240

Examining Nurses' Trust and Acceptance of FAIR, an AI-powered Falls Risk Recommender

"You Sure or Not?" Examining the Trust, Acceptance and Adoption of Falls Risk - Artificial Intelligence Recommender (FAIR) System by Nurses

• Status: Not Yet Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 60 (estimated)
• Sponsor: Tan Tock Seng Hospital · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Accepted

Summary

An exploratory mixed-method study will be conducted to test acceptance and trust of an AI-powered falls risk predictor system by inpatient hospital nurses

Detailed description

This protocol covers the trial component of a 4-year PhD research study covering focus group discussions with nurses on AI risk systems, workshops to gather feedback on the AI system and feasibility testing in a simulated environment and clinical environment

Conditions

• Falls Risk

Interventions

Type	Name	Description
OTHER	Falls risk - Artificial Intelligence Recommender (FAIR)	FAIR is an alert system built into the hospital's electronic medical record system. It is an adaptation of a machine learning model for fall risk calculation built in another hospital in Singapore. FAIR combines multiple patient-specific variables to identify if a patient is at increased risk of falling during their inpatient stay, marking them as a 'falls risk'. Based on the 'flag' raised, the nurse will be instructed to prioritise her falls risk assessment of the patient (If deemed 'high risk') or to do so subsequently as a lower priority once other pressing patient care issues are resolved (if deemed 'low risk'). That way, it ensures the requirements of each patient receiving a falls risk assessment as scored through mWHeFRA are still met, with FAIR allowing nurses to better prioritise their focus and attention on the patient that most needs the assessment at point of admission,
OTHER	modified Western Health Falls Risk Assessment Tool (mWHeFRA)	The mWHeFRA is the hospital's standard falls risk assessment tool. All nurses are expected to be proficient in its use to guide their risk assessment of patients

Timeline

• Start date: 2027-01-01
• Primary completion: 2027-12-31
• Completion: 2029-06-30
• First posted: 2025-07-22
• Last updated: 2025-07-22