Efficiency of Functioning of Health Care Systems as a Subject of State Financial Control: A Review of Russian and Foreign Experience

Budarin, S. S.; Elbek, Y V

doi:10.32687/0869-866x-2022-30-s1-976-982

Cited by 3 publications

(1 citation statement)

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“…In many countries, including Chile, the government is the most important funding source for healthcare. Since it is money collected from tax-payers, there is a real necessity for being efficient in the use of these resources (Budarin and Elbek, 2022).…”

Section: Introductionmentioning

confidence: 99%

Predicting the Number of Emergency Department Patients with Forecasting Models Based on Machine Learning

Proceedings of the International Conference on Industrial Engineering and Operations Management

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An emergency department (ED) is a specialized area where patients usually arrive in critical condition and require immediate care. Medical staff know that in these cases reacting quickly is crucial for saving patients' life. Since supplies, personnel and infrastructure are limited resources, the efficiency in their use is fundamental to operate adequately. Therefore, planning well in advance is fundamental to optimize the use of the existing resources. This research presents an interesting approach to forecast the number of patients received at the ED of a public hospital by means of implementing and comparing model bases in machine learning algorithms. This investigation was carried out following a 4-phase methodology: analysis, design, development, and validation. During the phase of analysis, the ED database with recorded collected during 2020 was reviewed and preprocessed. Data were prepared and organized to show number of patients visiting ED every day. During the phase of design, machine learning algorithms for forecasting were analyzed and compared. Among others: linear regression, artificial neural network, machine support vectors, and Gaussian methods. The development and the validation phases were carried out entirely using the data processing software WEKA 3.9.6 with the forecasting package version 1.1.27. In total, over seventy thousand records corresponding to ED visits occurred during 2020 were used in the investigation. Data were divides in two datasets: one for building forecasting models and another one for comparing predicted and actual values. Six forecasting horizons were study: 60, 45, 30, 15, 10, and 6 days. For each horizon four machine learning algorithms were used to predict the number of ED visits. To evaluate and compare predictions, the usual error metrics were considered: MAE, MSE, RMSE, and MAPE. Forecasting results revealed notorious differences in the accuracy of the predicted values. Although no big differences are noticeable when forecasting short periods, the prediction error is considerable when forecasting a long period of 60 days. In this case, MAPE fluctuated between 39% and 16% depending on the algorithm. When forecasting short periods, for instance 5 days, MAPE varied between 17% and 14%. In conclusion, experimental results showed that ML-based forecasting algorithms can be used to predict the number of ED visits with accuracy even when with long forecasting horizons. The application of such approach might be of great help to estimate resource requirements and to support decision making.

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Section: Introductionmentioning

confidence: 99%

Predicting the Number of Emergency Department Patients with Forecasting Models Based on Machine Learning

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Diagnostics of the influence of medical and demographic indicators on regional socio-economic systems

Barinov

2024

Vestnik universiteta

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The article presents approaches to monitoring and assessing social aspects of regional systems with identification of elements of the health system and demographic components that potentially affect the economic efficiency of functioning of territories. The relevance of the article is determined by objective features of the development of mesoterritories. The analysis of spatial features of the systems’ functioning was conducted on the materials of the Central Federal District. The purpose of the study is to assess the features of the regional genesis using regression analysis and the resulting indicator – gross regional product (hereinafter referred to as GRP), which allows us to give a more reliable characterisation of the mesoterritorial transformation. The subjects were grouped according to the quality of medical and demographic characteristics on the basis of the cluster analysis with further allocation of flagship, catching-up and lagging regions. To substantiate the reliability of the territories’ affiliation to a specific cluster group, a statistical analysis was performed on average values with consideration to identification of the level of medical and demographic development. The conclusion is made about the influence of some factor criteria on the regional space with the construction of a scatter plot. It displays the affiliation to the selected confidence interval and distribution of the elements of the set in the plane between the resulting and factor values, describing the dependence of the GRP on the medical and demographic indicator. These econometric and analytical models can be used by regional governments in the process of strategic planning.

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Prerequisites and practice of introducing integrated systems of medical care in England

Budarin

2023

Zdravoohran. Ross. Fed.

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The introduction of new conceptual approaches to public health protection is a trend of public health reform in Russia and European countries. The success of the implementation of public health policy in Russia depends both on the development of their own ideas and their implementation, on the study and use of the experience of foreign countries. For example, in England in the last decade, as part of the reform of the public health system, the idea of creating integrated medical care systems (ICS) has been actively developing and the experience of its implementation is of practical interest for Russian healthcare. Goal. To study the experience of England in implementing integrated medical care systems and analyze the possibility of using its individual elements in Russia. Materials and methods. The study was conducted on the basis of a comprehensive study of open reporting data of the Department of Health and Social Security of England, the National Health Service and the National Audit Office of England (NAO), as well as scientific reports on integrated public health management systems. Results. The development of integrated public health management systems being a trend in world practice, contributes to the improvement of public health indicators. The study of the experience of implementing ICS in England showed the presence of a number of risks during its implementation. Limitations. When conducting research, there were no restrictions that could influence the preparation of the article. Conclusions. Dissatisfaction with the level of management and financing of the healthcare system in England served as an impetus for the start of reforms that contribute to improving the management of medical care to the population, and the introduction of integrated medical care systems. The analysis of the NAO auditors revealed the main risks of the success of the ICS implementation. Studying the experience of implementing ICS is useful for use in the implementation of Russian national projects in the field of healthcare and demography.

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Efficiency of Functioning of Health Care Systems as a Subject of State Financial Control: A Review of Russian and Foreign Experience

Cited by 3 publications

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Predicting the Number of Emergency Department Patients with Forecasting Models Based on Machine Learning

Predicting the Number of Emergency Department Patients with Forecasting Models Based on Machine Learning

Diagnostics of the influence of medical and demographic indicators on regional socio-economic systems

Prerequisites and practice of introducing integrated systems of medical care in England

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