Burnout, Healthcare WorkersGAINSNCT06978790
Generative Artificial Intelligence Nurse Staffing Study
A study testing Generative Artificial Intelligence Nurse Staffing (GAINS) Intervention in people with Burnout, Healthcare Workers, run by University of Hawaii.
Trial summary
This study is guided by Maslach's Burnout Theory and with Normalization Process Theory supporting the implementation of the GAINS intervention by facilitating its integration into routine system-level practice. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Burnout, Healthcare Workers
- What is being tested?
- Generative Artificial Intelligence Nurse Staffing (GAINS) Intervention
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 660 participants
- Where
- No sites listed yet
- Study dates
- 1 Apr 2026to 31 Mar 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 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Registered nurses, nursing assistants, or key stakeholders
- Employed by The Queen's Medical Center
- Working at least 24 hours per week
- Position associated with medical-surgical units where float pool nurses work
Who cannot join
- Employees working less than 24 hours per week at The Queen's Medical Center
- Employees whose roles are not related to medical-surgical units
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
Holly Fontenot, PhD, APRN contact
About this study
Official title: Generative Artificial Intelligence Nurse Staffing (GAINS) Study
This study is guided by Maslach's Burnout Theory and with Normalization Process Theory supporting the implementation of the GAINS intervention by facilitating its integration into routine system-level practice. In Year 1, the investigative team will collaborate with hospital-based nursing leadership and key stakeholders to identify staffing-specific factors essential for operationalizing the GAINS AI model/intervention. In Year 1, the investigators will also conduct a survey amongst nursing staff to measure baseline burnout. In Year 2, the AI-staffing intervention will be implemented with the medical-surgical nursing float pool team. In Year 3, the investigators will first repeat the nurse burnout survey and second, expand the intervention to include the nursing assistant float pool team. In Year 4, the investigators will conduct the final burnout survey with nurses, assess feasibility of GAINS (target vs. actual staffing- nurses and nursing assistants), and assess preliminary efficacy of GAINS to reduce costs related to staffing. the investigators will compare outcomes at three time points (pre, mid, and post-intervention). Interviews with nurses, nursing assistants, unit nurse managers, and leadership will further explicate the intervention's acceptability, feasibility, and impact on burnout.
- Conditions
- Burnout, Healthcare Workers
- Being tested
- Generative Artificial Intelligence Nurse Staffing (GAINS) Intervention
- Medical subject headings
- Burnout, Psychological
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Generative Artificial Intelligence Nurse Staffing (GAINS) InterventionOther
The Generative Artificial Intelligence intervention is an industrial engineering and nursing-informed innovation developed to optimize team-based staffing of registered nurses and nursing assistants. We anticipate that the GAINS intervention will enhance staffing efficiency, reduces reliance on travel nurses, minimizes overtime costs, and supports nurse well-being by proactively managing workload distribution and reducing burnout. At the core of GAINS is a generative AI model that predicts future unit-level staffing needs using historical staffing patterns, patient turnover (admissions and discharges), and patient acuity scores (based on ICU versus medical/surgical status, physician orders, charge nurse input, and other clinical factors) reflective of workload. Based on the prediction, the intervention dynamically recommends float pool assignments by evaluating staffing gaps across units and optimally deploying available nurses and nursing assistants to where they are most needed.
| Study group | Role | What they receive |
|---|---|---|
| Arm 1: Standard staffing practice for float pool nurse and nursing assistants. | Experimental | This arm represents the control or standard of staffing practice to assign float pool nurse and nursing assistants. Receives: Other: Generative Artificial Intelligence Nurse Staffing (GAINS) Intervention |
| Arm 2: GAINS intervention applied to float pool nurses | Experimental | Generative Artificial Intelligence Nurse Staffing (GAINS) intervention applied to float pool nurses. Receives: Other: Generative Artificial Intelligence Nurse Staffing (GAINS) Intervention |
| Arm 3: GAINS intervention applied to float pool nurses and nursing assistants | Experimental | Generative Artificial Intelligence Nurse Staffing (GAINS) intervention applied to float pool nurses and nursing assistants. Receives: Other: Generative Artificial Intelligence Nurse Staffing (GAINS) Intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
Maslach's Burnout Inventory
Using Maslach's Burnout Inventory, burnout is the primary outcome measure and will assess burnout (1) at baseline over a time frame of 2 weeks, (2) 12-months after the GAINS intervention is applied to the float pool nurses over a time frame of 2 weeks, and 12-months after the GAINS intervention is applied to the float pool nurses and nursing assistants over a time frame of 2 weeks.
Measured: Up to 2.5 years
Qualitative Interviews to Evaluate Feasibility, Normalization, and Acceptability of the GAINS Intervention
We will interview 10-20 key stakeholders to collect and analyze qualitative data to evaluate the feasibility, normalization, and acceptability of the GAINS intervention.
T3here are two phases of the GAINS intervention.
- Phase I: GAINS study applied to nurses in Year 2
- Phase 2: GAINS study applied to nurses and nursing assistants in Year
These qualitative interviews will be held in Year 3 after Phase 1 over a 1-month time frame and Year 4 after Phase 2 completion of the study over a 1-month time frame. Interviews will be conducted to gather in-depth feedback on the intervention's feasibility, acceptability, and normalized into nursing practice.
Measured: Up to 3 years
Secondary outcomes (2)
Optimization Staffing Rates [Target staffing rate - Actual staffing rate]
Optimization staffing rates (target staffing rate - actual staffing rate) will be tracked at baseline over a 2-weeks, after Phase 1 over 2-weeks, and at the end of the study over 2-weeks.
Measured: Up to 2 years
Total Cost: Travel Nurse and Nurse Overtime
There are two phases of the GAINS intervention.
- Phase I: GAINS study applied to nurses in Year 2.
- Phase 2: GAINS study applied to nurses and nursing assistants in Year 3.
The total cost of travel nurses and nurse overtime will be tracked at baseline over 2-weeks, after Phase 1 is complete over a 2-week time frame, and at the end of Phase 2 over a time frame of 2-weeks.
Measured: Up to 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 May 2025
- Study start1 Apr 2026 (estimated)
- Primary completion31 Mar 2029 (estimated)
- Study completion31 Mar 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Burnout, Healthcare WorkersStudies 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
- Sequential
- Masking
- None (open label)
- Enrolment
- 660 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Hawaii
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Katie Azama, University of Hawaii
- Investigators
- Katie A Azama, PhD, Principal investigator, University of Hawaii at Manoa
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06978790
- Sponsor's protocol ID
- 41775
- Acronym
- GAINS
- Status last verified
- May 2025
- First submitted
- 1 May 2025
- Last updated
- 18 May 2025
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 18 May 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Generative Artificial Intelligence Nurse Staffing (GAINS) Study
- ClinicalTrials.gov ID
- NCT06978790
- Acronym / sponsor ID
- GAINS / 41775
- Conditions
- Burnout, Healthcare Workers
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 660 participants (estimated)
- Lead sponsor
- University of HawaiiUniversity, hospital or other
- Study start
- 1 Apr 2026 (estimated)
- Primary completion
- 31 Mar 2029 (estimated)
- Study completion
- 31 Mar 2029 (estimated)
- First posted
- 18 May 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.
This study is guided by Maslach's Burnout Theory and with Normalization Process Theory supporting the implementation of the GAINS intervention by facilitating its integration into routine system-level practice. In Year 1, the investigative team will collaborate with hospital-based nursing leadership and key stakeholders to identify staffing-specific factors essential for operationalizing the GAINS AI model/intervention. In Year 1, the investigators will also conduct a survey amongst nursing staff to measure baseline burnout. In Year 2, the AI-staffing intervention will be implemented with the medical-surgical nursing float pool team. In Year 3, the investigators will first repeat the nurse burnout survey and second, expand the intervention to include the nursing assistant float pool team. In Year 4, the investigators will conduct the final burnout survey with nurses, assess feasibility of GAINS (target vs. actual staffing- nurses and nursing assistants), and assess preliminary efficacy of GAINS to reduce costs related to staffing. the investigators will compare outcomes at three time points (pre, mid, and post-intervention). Interviews with nurses, nursing assistants, unit nurse managers, and leadership will further explicate the intervention's acceptability, feasibility, and impact on burnout.
Study design
- Primary purpose
- Health services research
- Allocation
- Non-randomised
- Intervention model
- Sequential
- Masking
- None (open label)
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Arm 1: Standard staffing practice for float pool nurse and nursing assistants. | Experimental | Other: Generative Artificial Intelligence Nurse Staffing (GAINS) InterventionThis arm represents the control or standard of staffing practice to assign float pool nurse and nursing assistants. |
| Arm 2: GAINS intervention applied to float pool nurses | Experimental | Other: Generative Artificial Intelligence Nurse Staffing (GAINS) InterventionGenerative Artificial Intelligence Nurse Staffing (GAINS) intervention applied to float pool nurses. |
| Arm 3: GAINS intervention applied to float pool nurses and nursing assistants | Experimental | Other: Generative Artificial Intelligence Nurse Staffing (GAINS) InterventionGenerative Artificial Intelligence Nurse Staffing (GAINS) intervention applied to float pool nurses and nursing assistants. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- Registered nurses, nursing assistants, or key stakeholders
- Employed by The Queen's Medical Center
- Working at least 24 hours per week
- Position associated with medical-surgical units where float pool nurses work
Key exclusion criteria (2 listed)
- Employees working less than 24 hours per week at The Queen's Medical Center
- Employees whose roles are not related to medical-surgical units
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 |
|---|---|---|
| Generative Artificial Intelligence Nurse Staffing (GAINS) InterventionThe Generative Artificial Intelligence intervention is an industrial engineering and nursing-informed innovation developed to optimize team-based staffing of registered nurses and nursing assistants. We anticipate that the GAINS intervention will enhance staffing efficiency, reduces reliance on travel nurses, minimizes overtime costs, and supports nurse well-being by proactively managing workload distribution and reducing burnout. At the core of GAINS is a generative AI model that predicts future unit-level staffing needs using historical staffing patterns, patient turnover (admissions and discharges), and patient acuity scores (based on ICU versus medical/surgical status, physician orders, charge nurse input, and other clinical factors) reflective of workload. Based on the prediction, the intervention dynamically recommends float pool assignments by evaluating staffing gaps across units and optimally deploying available nurses and nursing assistants to where they are most needed. | Other | Arm 1: Standard staffing practice for float pool nurse and nursing assistants., Arm 2: GAINS intervention applied to float pool nurses, Arm 3: GAINS intervention applied to float pool nurses and nursing assistants |
Primary outcome
Maslach's Burnout Inventory
Time frame: Up to 2.5 years
Using Maslach's Burnout Inventory, burnout is the primary outcome measure and will assess burnout (1) at baseline over a time frame of 2 weeks, (2) 12-months after the GAINS intervention is applied to the float pool nurses over a time frame of 2 weeks, and 12-months after the GAINS intervention is applied to the float pool nurses and nursing assistants over a time frame of 2 weeks.
Qualitative Interviews to Evaluate Feasibility, Normalization, and Acceptability of the GAINS Intervention
Time frame: Up to 3 years
We will interview 10-20 key stakeholders to collect and analyze qualitative data to evaluate the feasibility, normalization, and acceptability of the GAINS intervention. T3here are two phases of the GAINS intervention. 1. Phase I: GAINS study applied to nurses in Year 2 2. Phase 2: GAINS study applied to nurses and nursing assistants in Year These qualitative interviews will be held in Year 3 after Phase 1 over a 1-month time frame and Year 4 after Phase 2 completion of the study over a 1-month time frame. Interviews will be conducted to gather in-depth feedback on the intervention's feasibility, acceptability, and normalized into nursing practice.
2 secondary outcomes
Optimization Staffing Rates [Target staffing rate - Actual staffing rate]
Time frame: Up to 2 years
Total Cost: Travel Nurse and Nurse Overtime
Time frame: Up to 2 years
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 18 May 2025.
- Individual participant dataSharing planned: No
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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.
Holly Fontenot, PhD, APRN contact
Katie A Azama, PhD principal investigator
University of Hawaii at Manoa
Questions about this trial
What is this clinical trial studying?
A study testing Generative Artificial Intelligence Nurse Staffing (GAINS) Intervention in people with Burnout, Healthcare Workers, run by University of Hawaii.
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 4 inclusion and 2 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 18 May 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. 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 Katie A Azama, PhD, APRN (8082563382, katieu@hawaii.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Maslach's Burnout Inventory (time frame: Up to 2.5 years). 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?
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 18 May 2025.