EXPERIENCE OF NEURAL NETWORK MODELING IN MANAGING THE ACHIEVEMENT OF CRITERIA FOR A NEW MODEL OF A MEDICAL ORGANIZATION THAT USES LEAN TECHNOLOGIES

Nowadays the scientific developments connected with increase of readiness of the medical institutions, rendering primary medical and sanitary aid, to work with application of methods and tools of lean technologies for increase of level of availability and quality of medical aid to the population of Russia acquire urgency. The aim of the study is to assess the prognostic importance of common neural network models to analyze the value components of the reception of a local therapist, affecting the level of satisfaction with the quality of medical care, from the position of management to achieve the criteria of a new model of a medical organization using lean technologies. The following types of neural network models were studied: based on a multilayer perceptron, a radial basis function, and a generalized regression neural network. Models based on multiple linear regression equations were used as a control group of networks. In total, 50 artificial neural networks were obtained and analyzed. The effectiveness of neural network models was evaluated based on the following parameters: the ratio of standard deviations of the forecast error and the source data, as well as the Pearson correlation between the observed and predicted indicators of the model. Among the studied neural network models, models based on a multi-layer perceptron and generalized regression neural networks have the highest quality of prediction, which makes them promising for use in systems that monitor and predict the structure of the value component of the main processes in medical organizations for patients. The proposed neural network models can become the basis for creating information management systems that monitor the achievement of performance criteria for a new model of a medical organization that uses lean technologies.