Improving Patient Activity Schedules by Multi-agent Pareto Appointment Exchanging
CEC-EEE '06 Proceedings of the The 8th IEEE International Conference on E-Commerce Technology and The 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services
Logistic-based patient grouping for multi-disciplinary treatment
Artificial Intelligence in Medicine
Toward interactive scheduling systems for managing medical resources
Artificial Intelligence in Medicine
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As demand for health care increases, a high efficiency on limited resources is necessary for affordable high patient service levels. Here, we present an adaptive approach to efficient resource usage by automatic optimization of resource calendars. We describe a precise model based on a case study at the radiology department of the Academic Medical Center Amsterdam (AMC). We model the properties of the different groups of patients, with additional differentiating urgency levels. Based on this model, we develop a detailed simulation that is able to replicate the known scheduling problems. In particular, the simulation shows that due to fluctuations in demand, the allocations in the resource calendar must be flexible in order to make efficient use of the resources. We develop adaptive algorithms to automate iterative adjustments to the resource calendar. To test the effectiveness of our approach, we evaluate the algorithms using the simulation. Our adaptive optimization approach is able to maintain overall target performance levels while the resource is used at high efficiency.