The operating rooms (OR) in hospitals is a complex system, with a high number of innovations every year. Therefore the healthcare professionals (HCPs) are required to choose between the several aspects and innovations for the OR. Decision-making requires a mutual vision (Littlejohn et al., 2017) and should be based on evidence (Turner et al., 2017). To improve the well-informed decision-making, the main research question is: “How can a decision-support tool for optimisation in the operating room help a healthcare professional to select the objectives and the assessment criteria for performance optimisation of the operating room and the optimisation impact?”. The evidence for the tool consists of the 223 relations between the objectives and the assessment criteria (metrics) and 253 causalities of metrics that express the impact. There is a high heterogeneous perspective on the objectives of the optimisation of the performance of the OR and the criteria of assessing the quantification of (the optimisation of) the OR performance, which makes it harder to create a mutual vision on the OR performance. To achieve a mutual vision, a holistic view and consider all the perspectives, including the impact of optimisations, the HCPs should all share their perspectives on the objectives and the situation should be considered in its whole (Leinonen et al., 2008; Littlejohn et al., 2017). Therefore, the decision-making process could benefit of a decision-support tool. In this study a new tool is developed, namely the Performance Operating Room Counselling (PORC-)tool. This tool provides a holistic view of the OR performance (optimisation), by displaying evidence of the relations between objectives and metrics and the causalities. It also supports a conversation, which could lead to a mutual vision (Littlejohn et al., 2017). The tool is based on the concepts flowchart, matrix table and Microsoft Excel, for respectively the roadmap, the information display and the running-programme. The PORC-tool consists of an Excel file, a brochure and a manual with a more elaborated version of the functionality and the steps. The PORC-tool provides a clear and structural overview with evidence, to gather information more easily, provides multiple perspectives on the OR performance and supports to gather more insight into the OR organisation and goals before the decision-making of the HCP. The PORC-tool should be validated in practice and the functionality should be approved by HCPs. In the future, the tool can also be extended on perspectives, field of interest, aesthetics and functionality. To conclude, the answer to the main research question is that the HCPs should be facilitated to consider the whole complex system in their decision-making process, and support the decision-making based on evidence and with a mutual vision.

Research on the possibilities to use Artificial Intelligence (AI) at the Operating Room (OR) is rising, although there is no implementation yet. AI-assisted monitoring could provide support to anesthesia assistants in their task of patient monitoring, by making predictions on the future occurrence of deviating events in the vital parameter values of the patient. As AI-assisted monitoring would come close the profession of anesthesia assistants, this explorative, qualitative research investigates the attitude of anesthesia assistants towards the use of AI in their task of patient monitoring. Furthermore, it investigates the motivational factors that are important for their motivation in their monitoring task as well as the expected impact of AI on their motivation. This design-based research subsequently describes how these results are incorporated into the development of an educational module about AI and motivation for anesthesia assistants. Gaining as well as giving insight in the motivation of anesthesia assistants is important, as their motivation is of impact on their professional behaviour while their decisions and actions are directly related to the safety of the patient. Through literature research and semi-structured interviews, it was found that among anesthesia assistants there is little knowledge on the possibilities of AI at the OR, but that their attitude towards AI-assisted monitoring is positive as long as only suggestions are given. Only two out of eight anesthesia assistants are cautiously positive towards the possibility of AI to automate the monitoring process. Furthermore, it was found that the motivational factors of autonomy, relatedness, competence, self-efficacy, and self-actualisation are all important to very important in the motivation of anesthesia assistants. Overall, none of these factors stands out in being most or least important. The expected impact of AI on these motivational factors among anesthesia assistants varies from negative to positive and is highly dependent on the form of AI-assisted monitoring. As various forms of interrelatedness between all these motivational factors are identified by the anesthesia assistants, specific to their working practice and environment, the impact on one motivational factor might indirectly also impact others. These results on attitude and motivation are incorporated in the design of an educational module for anesthesia assistants. The module consists of two parts. One part regarding AI-assisted monitoring and one part regarding the motivational factors that are important for their motivation and the impact of AI on motivation. Design requirements by the anesthesia assistants, as well as an expert validation by an anesthesia educator, are used to finalise the design of the module. This module can help anesthesia assistants to develop awareness and understanding of how AI-assisted monitoring, in the form of giving suggestions, can support them in their patient monitoring task. Furthermore, it helps them to develop awareness and understanding on their own motivation and the impact that the use of AI-assisted monitoring may have on their motivation, guiding them towards becoming deliberate professionals.

Effective management of operating room (OR) schedules is vital for the efficient operation of healthcare facilities, especially considering the impact of decisions on staff, patients and costs. While extensive research has focused on optimizing initial schedules, the dynamic nature of surgery days introduces uncertainties that impact scheduling decisions. The study proposes equipping OR coordinators with a decision support system that leverages real-time insights and readily available hospital data to aid in decision making on the day of surgery. This thesis underscores the usefulness of OR policy documents in guiding decision support system design. Additionally, emphasis is placed on the need for standardized data and frequent real-time updates to improve the decision support systems accuracy and efficiency. To improve the performance of the decision support system efforts should be focussed on refining input data generated by hospitals.