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

NCT07078240 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 60

Last updated 2025-07-22

No results posted yet for this study

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

Conditions

  • Falls Risk

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

Sponsors & Collaborators

  • Marquette University

    collaborator OTHER

  • Lee Kong Chian School of Medicine, Nanyang Technological University

    collaborator UNKNOWN

  • Tan Tock Seng Hospital

    lead OTHER

Study Design

Allocation
NON_RANDOMIZED
Purpose
HEALTH_SERVICES_RESEARCH
Masking
NONE
Model
PARALLEL

Eligibility

Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2027-01-01
Primary Completion
2027-12-31
Completion
2029-06-30

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07078240 on ClinicalTrials.gov