Department of Medical Imaging, Unitec Institute of Technology, 5-7 Ratanui St, Henderson, Auckland, New Zealand.
Radiography (Lond). 2024 May;30(3):862-868. doi: 10.1016/j.radi.2024.03.014. Epub 2024 Apr 5.
New Zealand's shortage of medical imaging technicians has intensified due to factors like illness, the pandemic, and an ageing workforce. Addressing staff retention issues requires attention to intrinsic factors like workplace satisfaction and work-life balance. Self-rostering has proven effective in healthcare by enhancing work-life balance, job satisfaction, and retention, but it has not been implemented widely in radiology. This study aimed to explore the perceptions, benefits, and challenges of implementing AI-generated self-rostering in a radiology department through simulated trials.
This study simulated an AI-generated roster in a regional New Zealand radiology department, engaging 23 staff members. A mixed-methods approach included surveys and discussion groups. Community-based participatory action methodology guided discussion groups and informed modifications.
The AI-generated self-rostering method demonstrated success by meeting a high percentage of shift preferences while fulfilling service demands. Participants perceived potential benefits in work-life balance and autonomy, though uncertainties persisted regarding implementation and fairness. Despite staff reservations, we found that an AI-generated self-rostering system may be fairer than manual self-rostering, while saving radiology staff time and cost.
AI-generated self-rostering offers an innovative solution to an old problem. This self-rostering system provides a fair way for staff to have a say in the shifts they do, which increases feelings of work-life balance and autonomy. In this simulation, AI-generated self-rostering was well received, and most staff were receptive to moving to pilot the programme.
Self-rostering could be a potential solution to staff retention issues in radiology; we recommend a pilot study is implemented. When switching to self-rostering, departments should consider implementing one-on-one support systems to assist staff with entering preferences. Education is essential to encourage staff understanding and cooperation.
由于疾病、疫情和劳动力老龄化等因素,新西兰的医学影像技师短缺问题加剧。解决员工保留问题需要关注工作满意度和工作生活平衡等内在因素。自我排班已被证明在医疗保健领域通过提高工作生活平衡、工作满意度和保留率方面非常有效,但在放射科并未广泛实施。本研究旨在通过模拟试验探讨在放射科实施人工智能生成的自我排班的看法、益处和挑战。
本研究通过模拟在新西兰一个地区放射科的人工智能生成的排班,让 23 名员工参与。采用混合方法,包括调查和小组讨论。社区参与式行动方法指导了小组讨论并为修改提供了信息。
人工智能生成的自我排班方法通过满足高比例的班次偏好同时满足服务需求而取得成功。参与者认为在工作生活平衡和自主权方面有潜在的好处,但对实施和公平性仍存在不确定性。尽管员工有保留意见,但我们发现人工智能生成的自我排班系统可能比手动自我排班更公平,同时可以为放射科员工节省时间和成本。
人工智能生成的自我排班为老问题提供了创新的解决方案。这种自我排班系统为员工提供了一种公平的方式来选择他们要做的班次,从而增加工作生活平衡和自主权的感觉。在这次模拟中,人工智能生成的自我排班得到了很好的评价,大多数员工都愿意转而试用该计划。
自我排班可能是放射科员工保留问题的潜在解决方案;我们建议进行试点研究。当转向自我排班时,部门应考虑实施一对一的支持系统,以帮助员工输入偏好。教育对于鼓励员工理解和合作至关重要。