Generative Artificial Intelligence Nurse Staffing Study (GAINS)

University of Hawaii

Status: Begins enrollment in 3 months

Conditions: Burnout, Healthcare Workers

Treatments: Other: Generative Artificial Intelligence Nurse Staffing (GAINS) Intervention

Study type: Interventional

Funder types: Other

Identifiers

NCT06978790

41775

Details and patient eligibility

About

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.

Enrollment: 660 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: Accepts Healthy Volunteers

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

Trial design

Primary purpose: Health Services Research

Allocation: Non-Randomized

Interventional model: Sequential Assignment

Masking: None (Open label)

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660 participants in 3 patient groups

Arm 1: Standard staffing practice for float pool nurse and nursing assistants.

Experimental group

Description:

This arm represents the control or standard of staffing practice to assign float pool nurse and nursing assistants.

Treatment:

Other: Generative Artificial Intelligence Nurse Staffing (GAINS) Intervention

Arm 2: GAINS intervention applied to float pool nurses

Experimental group

Description:

Generative Artificial Intelligence Nurse Staffing (GAINS) intervention applied to float pool nurses.

Treatment:

Other: Generative Artificial Intelligence Nurse Staffing (GAINS) Intervention

Arm 3: GAINS intervention applied to float pool nurses and nursing assistants

Experimental group

Description:

Generative Artificial Intelligence Nurse Staffing (GAINS) intervention applied to float pool nurses and nursing assistants.

Treatment:

Other: Generative Artificial Intelligence Nurse Staffing (GAINS) Intervention