Evaluating the impact of artificial intelligence-driven self-rostering: A dual-site pilot in medical imaging

IF 2.8 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Radiography Pub Date : 2025-09-01 DOI:10.1016/j.radi.2025.103150

S. Sitters , K. O'Callahan , M. O'Callahan , M. Petersen , T. Clasper , L. Sicely

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引用次数: 0

Abstract

Introduction

Work-life balance is an increasing priority in healthcare. However, this presents a challenge for public hospitals, where shift work, weekend work and night shifts are essential to providing 24-h care. Self-rostering (SR) is a widely researched method that can increase work-life balance for health professionals. However, the introduction of artificial intelligence (AI) is crucial to overcome the complexity of multi-department scheduling common in medical imaging departments. Guided by previous findings, this study explores work-life balance, shift-swapping and sick-leave in relation to AI-SR.

Methods

This research was undertaken alongside an AI-SR pilot, in two medical imaging departments (one urban, one regional) in Aotearoa, New Zealand (NZ). There were 71 participants, 81 % were female, all aged between 20 and 70. An explanatory-sequential mixed methods design was utilised. Data of shift-swaps, sick leave and work-life balance was collected, and thematic analysis was undertaken.

Results

Sick leave results varied between sites, linked qualitatively to improved staffing-levels and decreased work-life conflict, no change was noted in shift-swapping. Qualitative themes were agency, work fitting around life, fairness and loss aversion and receptivity.

Conclusion

AI-based self-rostering can help medical imaging staff balance work with their personal lives and may reduce sick leave, but more research is needed on its impact on wellbeing. Our findings also suggest that incorporating explainable AI could improve fairness and user acceptance, although guidance on XAI design for healthcare is scarce.

Implications for practice

Our study shows that AI-SR systems improve work-life balance. However, those looking to implement AI-SR systems should consider system transparency, to help mitigate loss aversion and improve perceptions of fairness. This is crucial to improving user understanding and, ultimately, acceptance of the scheduling approach and AI technologies more generally.

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评估人工智能驱动的自我名册的影响：医学成像的双站点试点

在医疗保健中，工作与生活的平衡越来越受到重视。然而，这对公立医院提出了挑战，因为轮班工作、周末工作和夜班对提供24小时护理至关重要。自我排班（SR）是一种被广泛研究的方法，可以提高健康专业人员的工作与生活平衡。然而，人工智能（AI）的引入对于克服医学影像部门常见的多部门调度的复杂性至关重要。在以往研究结果的指导下，本研究探讨了工作与生活平衡、轮班交换和病假与人工智能sr的关系。方法本研究与AI-SR试点一起在新西兰（NZ） Aotearoa的两个医学成像部门（一个城市，一个区域）进行。共有71名参与者，81%为女性，年龄在20至70岁之间。采用解释-序列混合方法设计。收集了轮班、病假和工作与生活平衡方面的数据，并进行了专题分析。结果不同地点的病假结果不同，与员工水平的提高和工作与生活冲突的减少有质的联系，轮班交换没有变化。定性主题是能动性、工作适应生活、公平、损失厌恶和接受性。结论基于人工智能的自我排班可以帮助医疗影像人员平衡工作与个人生活，并可能减少病假，但其对健康的影响还需要更多的研究。我们的研究结果还表明，纳入可解释的人工智能可以提高公平性和用户接受度，尽管关于医疗保健XAI设计的指导很少。我们的研究表明，AI-SR系统改善了工作与生活的平衡。然而，那些希望实施AI-SR系统的人应该考虑系统透明度，以帮助减轻损失厌恶情绪并提高对公平性的看法。这对于提高用户的理解，并最终更普遍地接受调度方法和人工智能技术至关重要。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Radiography RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-

CiteScore

4.70

自引率

34.60%

发文量

169

审稿时长

63 days

期刊介绍： Radiography is an International, English language, peer-reviewed journal of diagnostic imaging and radiation therapy. Radiography is the official professional journal of the College of Radiographers and is published quarterly. Radiography aims to publish the highest quality material, both clinical and scientific, on all aspects of diagnostic imaging and radiation therapy and oncology.