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NOT YET RECRUITING
NCT07078240
NA

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

Sponsor: Tan Tock Seng Hospital

View on ClinicalTrials.gov

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

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

Key Details

Gender

All

Age Range

Any - Any

Study Type

INTERVENTIONAL

Enrollment

60

Start Date

2027-01-01

Completion Date

2029-06-30

Last Updated

2025-07-22

Healthy Volunteers

Yes

Conditions

Interventions

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