Generative Artificial Intelligence Nurse Staffing 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.

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Key information

Conditions

Burnout, Healthcare Workers Behavior Behavioral Symptoms Burnout, Psychological Stress, Psychological

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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

Exclusion criteria

Employees working less than 24 hours per week at The Queen's Medical Center
Employees whose roles are not related to medical-surgical units

Treatment and study plan

Generative Artificial Intelligence Nurse Staffing (GAINS) Intervention

Other

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.

Primary outcomes

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.
- 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.

Secondary outcomes

Optimization Staffing Rates [Target staffing rate - Actual staffing rate]

Time frame: Up to 2 years

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.
Total Cost: Travel Nurse and Nurse Overtime

Time frame: Up to 2 years
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.

Study contacts

Contact information is provided by the study sponsor or research team.

Holly Fontenot, PhD, APRN

CONTACT

[email protected]

Katie A Azama, PhD, APRN

CONTACT

[email protected]

8082563382

Sponsors and collaborators

Lead sponsor

University of Hawaii

Other

Registry information

Official study title

Generative Artificial Intelligence Nurse Staffing (GAINS) Study

Acronym: GAINS

Important dates

Study start: 2026
Primary completion: 2029
Study completion: 2029
First posted: May 18, 2025
Registry last updated: May 18, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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