Generative Artificial Intelligence Nurse Staffing Study
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Generative Artificial Intelligence Nurse Staffing (GAINS) Intervention.
- Who it may be relevant to
- Registry conditions: Burnout, Healthcare Workers. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Center list to be confirmed — check the primary protocol.
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Generative Artificial Intelligence Nurse Staffing (GAINS) Study
Overview
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.
Interventions
- Other Generative Artificial Intelligence Nurse Staffing (GAINS) Intervention
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 futur
Primary outcome measures
- Maslach's Burnout Inventory [Time frame: Up to 2.5 years]
- Qualitative Interviews to Evaluate Feasibility, Normalization, and Acceptability of the GAINS Intervention [Time frame: Up to 3 years]
Secondary outcome measures (2)
- 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]
Eligibility criteria
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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Allocation
- Non-randomized
- Model
- Sequential
- Masking
- Open label
- Primary purpose
- Health services research
Study locations
Center list to be confirmed — check the primary protocol.
Identifiers
NCT: NCT06978790 · 41775