基于统计模拟的重症监护病房预测工作

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引用次数: 0

摘要

俄罗斯正在发生的深刻的经济转型、卫生保健向新的管理机制的过渡以及对保险医疗的重视,为改善医疗和预防机构及相关服务的业务管理提供了特殊的现实。考虑到所研究系统的复杂性,采用了数学模拟方法。研究目的:在研究复苏重症监护病房功能特点的基础上,通过可能的规划,提高复苏重症监护病房的效率。方法:提出了一种统计模拟方法,作为研究复苏和重症监护病房功能的一种特殊的数学建模方法,这种方法以前没有在这个方向上使用过。结果:在工作中,重症监护病房no.;以V.D. Seredavin命名的萨马拉地区临床医院的2个科室和科室运作的业务流程进行了研究,选取平均流程,对业务流程进行统计研究,识别并评价其分布规律。基于单个随机变量的随机数生成器开发了一些仿真算法,并确定了一些统计指标,这些指标是在所有这些算法的实现过程中计算的。使用专门的AnyLogic工具,我们成功地开发了复苏和重症监护病房工作的模型,在程序中确定并考虑了进行实验和比较接收到的数据的可能性。在一个改变各种输入数据的模型上进行了实验。对获得的数据进行了分析,包括使用专门的软件,并确定了一些一致的模式。现实意义:提出科室优化工作方向,提出预测和监测重症监护病房运行的数学公式。
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FORECASTING WORK OF INTENSIVE CARE UNIT ON THE BASIS OF STATISTICAL SIMULATION
The profound economic transformations taking place in Russia, the transition of health care to new management mechanisms, and the focus on insurance medicine give special actuality to improving the operational management of medical and prophylactic institutions and related services. Given the complexity of the system under study, a mathematic simulation was applied. Study purpose: To increase the efficiency of the resuscitation and intensive care unit through possible planning, based on studying the features of the functioning of this type units. Methods: A method of statistical simulation is proposed as a particular method of mathematical modeling for studying the functioning of the resuscitation and intensive care unit, which was not previously used in this direction. Results: During the work, the intensive care unit no. 2 of the Samara Regional Clinical Hospital named after V.D. Seredavin and the business process of the department’s functioning were studied, an averaged process was selected, a statistical study of the business process was carried out, and the distribution laws were identified and evaluated. Some simulation algorithms were developed based on a random number generator for individual random variables, and some statistical indicators were determined, which were calculated during implementation of all these algorithms. Using the specialized AnyLogic tool, we managed to develop a model of resuscitation and intensive care unit work, the number of runs was determined and considered in the program for the possibility of conducting experiments and comparing the data received. An experiment was performed on a model with changing various input data. The data obtained were analyzed, including using specialized software, and some consistent patterns were identified. Practical significance: Directions for optimizing the work of the department were proposed, and mathematical formulas were suggested for predicting and monitoring the operation of an intensive care unit.
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