부산대학교 도서관

In spite of continuous medical and technological advances, there are still treatments today, e.g., infertility treatments, that have not been addressed using remote monitoring due to the absence of reliable devices and smart health monitoring systems (SHMSs). A recent European Union report highlights the need for scientific investment in remote monitoring efficiency. To expedite cost-effective real-data experiments, the scientific community emphasizes complementing SHMSs with modeling and simulation techniques, allowing the projection of comprehensive treatment scenarios and informed decision-making based on synthetic data. In this paper, we conducted simulations of patient flow in two assisted reproduction clinics, with real data, applying discrete event simulation techniques. Four simulation scenarios were run for the Alicante clinic: (i) current system organization, (ii) using SHMS with the same resources, (iii) SHMS with reduced clinical staff, and (iv) SHMS with modified staff allocation. In a large-scale clinic in Madrid, two scenarios were simulated: (i) current system organization and (ii) SHMS with the same resources. The simulation of different scenarios enabled (i) the identification of a bottleneck in an ultrasound scanning area in the current system’s organization; (ii) modelling of the relocation of the clinical staff within the clinic; and (iii) a proposal of the most optimal scenario with SHMS. Comparing the obtained results, we showed a reduction in the workload of the receptionists, in the ultrasound and blood collection areas and finally reduced the workload of physicians with first half and the second half of the working day schedules in both clinics.