Scarce, scarcer, scarcest: performance-flexible AI-based planning of elective surgeries for efficient and effective intensive care capacity management

Operating room and intensive care unit (ICU) capacities belong to the scarcest resources in hospitals and strongly depend on each other. When planning elective surgeries, it is therefore important to consider both resources in an integrated way and to guarantee a certain flexibility in planning to avoid under- and overutilization, e.g., in the form of cancellations. In this work, we introduce a performance-flexible artificial intelligence (AI)-based planning approach for predicting whether an elective patient will be transferred to the ICU after elective surgery. This approach includes a performance-flexible loss function in a machine learning (ML) model and a subsequent simulation about ICU occupancy. The algorithm is evaluated by a large data set of the University Hospital of Augsburg, Germany, consisting of more than 26,600 elective surgeries between 2017 and 2021, and extensive simulation studies. This approach is generalizable as it uses data typically available during surgery planning in the outpatient clinic. Our findings demonstrate that, unlike state-of-the-art ML algorithms, our performance-flexible AI-based planning approach can prioritize a specific label in binary classification (i.e., ICU or non-ICU) subject to capacity considerations while maintaining high accuracy. This ensures a stable ratio of realized demand to planned ICU capacity that is close to 1 across different scenarios. Our performance-flexible AI-based planning algorithm outperforms state-of-the-art ML algorithms and supports hospital decision-makers with a flexible planning tool.