Generative Artificial Intelligence Nurse Staffing Study

Официальное название

Generative Artificial Intelligence Nurse Staffing (GAINS) Study

Обзор

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.

Вмешательства

• Другое 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

Первичные конечные точки

• Maslach's Burnout Inventory [Срок оценки: Up to 2.5 years]
• Qualitative Interviews to Evaluate Feasibility, Normalization, and Acceptability of the GAINS Intervention [Срок оценки: Up to 3 years]

Вторичные конечные точки (2)

• Optimization Staffing Rates [Target staffing rate - Actual staffing rate] [Срок оценки: Up to 2 years]
• Total Cost: Travel Nurse and Nurse Overtime [Срок оценки: Up to 2 years]

Критерии участия

Критерии включения

• 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

Критерии исключения

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

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Да

Дизайн исследования

Распределение

Нерандомизированное

Модель

Последовательный дизайн

Маскирование

Открытое

Основная цель

Организация здравоохранения

Центры проведения

Список центров уточняется — проверьте первичный протокол.

Идентификаторы

NCT: NCT06978790 · 41775

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

Открыть это исследование на ClinicalTrials.gov ↗