Partizipative Dienstplanung

The Participatory Work Scheduling project addresses the lack of work privacy conflict and aims to provide a basis for developing participatory and fair AI-based work scheduling.



The Participatory Work Scheduling project focuses on the need for work privacy conflict, as this is one of the most common reasons for staff turnover, not only in the healthcare sector. This balance is significantly influenced by shift patterns, the adverse effects of which are often attributed to a lack of participation. One way to promote fairness in shift scheduling is to use a supporting algorithm with a fair overall perspective. However, it is challenging to consider all factors, such as unplanned absences and individual preferences of healthcare workers. Aim: This collaborative project aims to enhance work privacy conflict in the healthcare sector by identifying relevant requirements and creating an AI-supported procedure for shift scheduling. This is intended to become a long-term part of a practical solution and undergo corresponding refinements.

Course of action

To accomplish this objective, two extensive literature reviews will be conducted. The first review will investigate the reasons and influencing factors for work-life conflicts in duty scheduling and present their interrelationships. The second review will provide an overview of AI solutions for duty scheduling, considering their suitability, customisability, fairness, and trustworthiness. In addition, we will conduct focus group interviews with permanent healthcare professionals to gather their perspectives on fair, trustworthy, and participatory scheduling. The opinions of temporary employees regarding incentives and duty scheduling will also be considered. Options for the practical implementation of fair duty scheduling will be developed based on the findings from the literature research and the focus group interviews. Existing methodological and technological approaches are analysed for this purpose, considering their general advantages and disadvantages and incorporating experiences regarding fairness in their application. This establishes a foundation for developing and evaluating an algorithm in a practical context.

This project contributes to the following SDGs

  • 3: Good health and well-being