Optimization of work processes is a prerequisite for high-quality and efficient work performance. The use of optimization methods can significantly reduce the time spent, as well as material and human resources. Such savings are especially critical for medicine when it comes to human lives. Several medical institutions have dangerous shortcomings that optimization methods can solve (Liu et al., 2018). Using such techniques will allow the leaders of medical institutions to bring the provision of medical care to a qualitatively new level.
Many management, logistics, and sorting problems are optimization problems that can be solved mathematically. Such problems in medicine include, for example, the procedure for sending ambulances depending on the urgency of the order. Competent mathematical processing of paths, taking into account the priority of the call and the remoteness of the destination, will reduce the time of arrival of the medical team. The value of optimization in this case is to save time and resources that can save lives. Another example of such a problem is sorting in the warehouse of medicines, especially precursors. Despite the above problems’ complexity, optimization methods can cope with them since they are purely mathematical (Liu et al., 2018). Another, more specific example for which a particular solution can be proposed is the optimization of patient flows. The optimization criterion, in this case, is to ensure the maximum number of patients served: the sum aij*xij should tend to the maximum, where a is the “priority” of the medical institution, x is the number of patients, i is the number of medical institutions, and j is the number of territorial units where they are located.
By changing the guaranteed levels of regional and subregional resources and the intensity of their use, it will be possible to achieve, on the one hand, a balanced mode of operation and, on the other hand, to establish prospects for the development of resource provision based on the capabilities of the multi-channel financing system. Solving this optimization problem will enable the properly distributing of patient flows between institutions.
Reference
Liu, W., Wang, Z., Liu, X., Zeng, N., & Bell, D. (2018). A novel particle swarm optimization approach for patient clustering from emergency departments. IEEE Transactions on Evolutionary Computation, 23(4), 632-644.