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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
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT07078240
Enrollment
60
Registered
2025-07-22
Start date
2027-01-01
Completion date
2029-06-30
Last updated
2025-07-22

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Falls Risk

Keywords

Artificial Intelligence trust, Artificial Intelligence acceptance, Artificial Intelligence

Brief 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

Interventions

OTHERFalls 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,

OTHERmodified 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

Marquette University
CollaboratorOTHER
Lee Kong Chian School of Medicine, Nanyang Technological University
CollaboratorUNKNOWN
Tan Tock Seng Hospital
Lead SponsorOTHER

Study design

Allocation
NON_RANDOMIZED
Intervention model
PARALLEL
Primary purpose
HEALTH_SERVICES_RESEARCH
Masking
NONE

Intervention model description

Two phases of interventional study. First is a parallel arm study comparing nurse acceptance and trust in intervention vs control arm in simulation lab setting.

Eligibility

Sex/Gender
ALL
Healthy volunteers
Yes

Inclusion criteria

* Practicing nurse involved in falls risk assessments of patients

Exclusion criteria

\-

Design outcomes

Primary

MeasureTime frameDescription
Incidence of FAIR's flag acceptance1 Day of StudyExamination of how often the flags raised by FAIR are accepted by nurses, and whether they are accepted or ignored correctly.
Time taken to do falls risk assessment1 Day of StudyThe time taken by the nurses to perform their falls risk assessment will be recorded

Secondary

MeasureTime frameDescription
Time spent looking at FAIR1 Day of StudyThe time each nurses takes looking at the FAIR falls risk assessment will be assessed
Baseline and Post-Simulation Nurse trust and acceptance of FAIRBaselineMeasured by the adapted Unified Theory of Acceptance and Use of Technologies and System Usability Survey, adjusted to better capture the key predictors of nurse acceptance

Contacts

Primary ContactGeorge Glass, PhD Student
GLAS0002@e.ntu.edu.sg+65 6903-5384

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026