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Patients undergoing anesthetic surgery are treated postoperatively in a Post-Anesthesia Care Unit (PACU). Traditional planning methods often fail to account for the complexity of patient data. This study aims to develop a machine learning (ML) tool to predict PACU-care times and to improve patient throughput. By integrating local-explanation models, we seek to gain clinical acceptance by providing insights into individual predictions. The project utilizes data from over 84,000 patients, including more than 170 variables.

Introduction: The need for innovative technology in healthcare is apparent due to challenges posed by the lack of resources. This study investigates the adoption of AI-based systems, specifically within the postanesthesia care unit (PACU). The aim of the study was to explore staff needs and expectations concerning the development and implementation of a digital patient flow system based on ML predictions.

Methods: A qualitative approach was employed, gathering insights through interviews with 20 healthcare professionals, including nurse managers and staff involved in planning patient flows and patient care. The interview data were analyzed using reflexive thematic analysis, following steps of data familiarization, coding, and theme generation. The resulting themes were then assessed for their alignment with the modified technology acceptance model (TAM2).

Results: The respondents discussed the benefits and drawbacks of the proposed ML system versus current manual planning. They emphasized the need for controlling PACU throughput and expected the ML system to improve the length of stay predictions and provide a comprehensive patient flow overview for staff. Prioritizing the patient was deemed important, with the ML system potentially allowing for more patient interaction time. However, concerns were raised regarding potential breaches of patient confidentiality in the new ML system. The respondents suggested new communication strategies might emerge with effective digital information use, possibly freeing up time for more human interaction. While most respondents were optimistic about adapting to the new technology, they recognized not all colleagues might be as convinced.

Conclusion: This study showed that respondents were largely favorable toward implementing the proposed ML system, highlighting the critical role of nurse managers in patient workflow and safety, and noting that digitization could offer substantial assistance. Furthermore, the findings underscore the importance of strong leadership and effective communication as key factors for the successful implementation of such systems.