Falls RiskNCT07078240
Examining Nurses' Trust and Acceptance of FAIR, an AI-powered Falls Risk Recommender
A study testing Falls risk - Artificial Intelligence Recommender (FAIR) and modified Western Health Falls Risk Assessment Tool (mWHeFRA) in people with Falls Risk, run by Tan Tock Seng Hospital.
Trial 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 Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Falls Risk
- What is being tested?
- Falls risk - Artificial Intelligence Recommender (FAIR) and modified Western Health Falls Risk Assessment Tool (mWHeFRA)
- Could I receive a placebo?
- Yes, some participants may receive a placebo
- How many people
- 60 participants
- Where
- No sites listed yet
- Study dates
- 1 Jan 2027to 30 Jun 2029
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Who may be able to join
- Practicing nurse involved in falls risk assessments of patients
Who cannot join
- -
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: "You Sure or Not?" Examining the Trust, Acceptance and Adoption of Falls Risk - Artificial Intelligence Recommender (FAIR) System by Nurses
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
Read the 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
- Being tested
- Falls risk - Artificial Intelligence Recommender (FAIR), modified Western Health Falls Risk Assessment Tool (mWHeFRA)
- Keywords
- Artificial Intelligence trust, Artificial Intelligence acceptance, Artificial Intelligence
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Falls risk - Artificial Intelligence Recommender (FAIR)Other
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,
modified Western Health Falls Risk Assessment Tool (mWHeFRA)Other
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
| Study group | Role | What they receive |
|---|---|---|
| Intervention Arm | Experimental | Nurses in the intervention arm will perform will receive training on the nature of AI recommender (FAIR) and how it works, and how they can apply it in their assessment of the patient.
After that, they will be introduced to three simulated patients with different conditions and needs - intended to reflect a patient at "low risk of falls", "moderate risk of falls" and "high risk of falls" and asked to read and interpret the FAIR recommendations before making their own falls risk assessment of the patient using mWHeFRA. Receives: Other: Falls risk - Artificial Intelligence Recommender (FAIR) |
| Control arm | Placebo comparator | Nurses in the control arms will be reinforced on fall risk assessment methods using the modified Western modified Western Health Falls Risk Assessment Tool (mWHeFRA).
After that, they will be introduced to three simulated patients with different conditions and needs - intended to reflect a patient at "low risk of falls", "moderate risk of falls" and "high risk of falls" and asked to perform assessments of the patient mWHeFRA. Receives: Other: modified Western Health Falls Risk Assessment Tool (mWHeFRA) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Incidence of FAIR's flag acceptance
Examination of how often the flags raised by FAIR are accepted by nurses, and whether they are accepted or ignored correctly.
Measured: 1 Day of Study
Time taken to do falls risk assessment
The time taken by the nurses to perform their falls risk assessment will be recorded
Measured: 1 Day of Study
Secondary outcomes (2)
Time spent looking at FAIR
The time each nurses takes looking at the FAIR falls risk assessment will be assessed
Measured: 1 Day of Study
Baseline and Post-Simulation Nurse trust and acceptance of FAIR
Measured 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
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Jul 2025
- Study start1 Jan 2027 (estimated)
- Primary completion31 Dec 2027 (estimated)
- Study completion30 Jun 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Falls RiskStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- 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.
- Masking
- None (open label)
- Enrolment
- 60 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Tan Tock Seng Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Marquette University, Nanyang Technological University
- Responsible party
- Principal investigator, Glass George Frederick Jr, Tan Tock Seng Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07078240
- Sponsor's protocol ID
- NMRCRTF25jan-004
- Status last verified
- Jul 2025
- First submitted
- 22 Jun 2025
- Last updated
- 22 Jul 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Will be agreeable to share Protocol Summary page
Publications
Schulz PJ, Lwin MO, Kee KM, Goh WWB, Lam TYT, Sung JJY. Modeling the influence of attitudes, trust, and beliefs on endoscopists' acceptance of artificial intelligence applications in medical practice. Front Public Health. 2023 Nov 28;11:1301563. doi: 10.3389/fpubh.2023.1301563. eCollection 2023.
PubMed 38089040
Study at a glance
From the registry record, last updated by the sponsor on 22 Jul 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- "You Sure or Not?" Examining the Trust, Acceptance and Adoption of Falls Risk - Artificial Intelligence Recommender (FAIR) System by Nurses
- ClinicalTrials.gov ID
- NCT07078240
- Acronym / sponsor ID
- NMRCRTF25jan-004
- Conditions
- Falls Risk
- Keywords
- Artificial Intelligence trust, Artificial Intelligence acceptance, Artificial Intelligence
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 60 participants (estimated)
- Lead sponsor
- Tan Tock Seng HospitalUniversity, hospital or other
- Collaborators
- Marquette University, Nanyang Technological University
- Study start
- 1 Jan 2027 (estimated)
- Primary completion
- 31 Dec 2027 (estimated)
- Study completion
- 30 Jun 2029 (estimated)
- First posted
- 22 Jul 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
Study design
- Primary purpose
- Health services research
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- 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.
| Arm | Type | Receives |
|---|---|---|
| Intervention Arm | Experimental | Other: Falls risk - Artificial Intelligence Recommender (FAIR)Nurses in the intervention arm will perform will receive training on the nature of AI recommender (FAIR) and how it works, and how they can apply it in their assessment of the patient. After that, they will be introduced to three simulated patients with different conditions and needs - intended to reflect a patient at "low risk of falls", "moderate risk of falls" and "high risk of falls" and asked to read and interpret the FAIR recommendations before making their own falls risk assessment of the patient using mWHeFRA. |
| Control arm | Placebo comparator | Other: modified Western Health Falls Risk Assessment Tool (mWHeFRA)Nurses in the control arms will be reinforced on fall risk assessment methods using the modified Western modified Western Health Falls Risk Assessment Tool (mWHeFRA). After that, they will be introduced to three simulated patients with different conditions and needs - intended to reflect a patient at "low risk of falls", "moderate risk of falls" and "high risk of falls" and asked to perform assessments of the patient mWHeFRA. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (1 listed)
- Practicing nurse involved in falls risk assessments of patients
Key exclusion criteria (1 listed)
- -
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| 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 | Intervention Arm |
| 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 | Other | Control arm |
Primary outcome
Incidence of FAIR's flag acceptance
Time frame: 1 Day of Study
Examination 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 assessment
Time frame: 1 Day of Study
The time taken by the nurses to perform their falls risk assessment will be recorded
2 secondary outcomes
Time spent looking at FAIR
Time frame: 1 Day of Study
Baseline and Post-Simulation Nurse trust and acceptance of FAIR
Time frame: Baseline
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Jul 2025.
- Background: PubMed 38089040Schulz PJ, Lwin MO, Kee KM, Goh WWB, Lam TYT, Sung JJY. Modeling the influence of attitudes, trust, and beliefs on endoscopists' acceptance of artificial intelligence applications in medical practice. Front Public Health. 2023 Nov 28;11:1301563. doi: 10.3389/fpubh.2023.1301563. eCollection 2023.
- Individual participant dataSharing planned: Yes. Will be agreeable to share Protocol Summary page
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Questions about this trial
What is this clinical trial studying?
A study testing Falls risk - Artificial Intelligence Recommender (FAIR) and modified Western Health Falls Risk Assessment Tool (mWHeFRA) in people with Falls Risk, run by Tan Tock Seng Hospital.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 22 Jul 2025. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. Some participants in this study may receive a placebo. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact George Glass, PhD Student (+65 6903-5384, GLAS0002@e.ntu.edu.sg), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Incidence of FAIR's flag acceptance (time frame: 1 Day of Study). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07078240, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 Jul 2025.