THE FINANCING MODEL OF THE REGIONAL HEALTH SYSTEM

JEL classification: H51, I15, R13 Abstract Subject The research devises the healthcare spending model in line with types and scope of medical aid, assesses indicators of the current and future planned periods. Objectives The research is an attempt to optimize healthcare spending by improving the way it is earmarked in line with the region’s demand for certain types of medical aid and reallocating financial resources. Methods We use the conceptual modeling and Python 3. Results We devised the model to optimize expenditures in order to increment resources available for health care purposes by reallocating them in accordance with the analysis of the financial situation, classes of patients. Our model builds on the type of total medical expenditures on patients with certain diseases and adjustment of healthcare spending against the current year’s plans.


Introduction
Many † countries model certain aspects of their healthcare systems [1][2][3][4]. They strive to make their national health systems more effective in light of the existing social needs and goals 1 .
However, despite the highly elaborate approach to this issue as seen in the scholarly literature, there are still no versatile models allowing to plan and allocate healthcare resources as the region and/or country would really need. As a result, it is difficult to substantially upgrade the health system by lowering the public morbidity rates, curb a growth in medical spending [5][6][7][8][9]. The above effects are indispensable and unattainable without optimizing the mechanism for planning and allocating healthcare resources in line with the region's local distinctions. To ensure further development of the regional healthcare management theory and practices, resource planning and allocation processes should be modeled. In this respect, the appropriate methodological tools should be selected.
Russia's health system is restructured to increase the cost effectiveness of medical aid to the public. The analysis reveals that optimization of resource planning, allocation and use is still pending in this sector [27][28][29][30][31]. Currently, human rights for health protection and medical aid are widely pronounced and specified. To exercise the declared rights, the State needs to form, effectively allocate and use as much financial resources as needed to perform specific tasks and attain healthcare goals. Accessible medical aid and its quality require to integrate various types of medical assistance. The health system shall be organized on the basis of a comprehensive approach to managing all its constituents [32].
Medical aid in case of a certain disease implies a combination of healthcare services varying by disease or a group of diseases. Available resources and financial resources, in particular, become a cornerstone of the quality and effectiveness of such medical care.
Nowadays healthcare services are financed in accordance with the State guarantee program for public healthcare and special-purpose programs for priority healthcare aspects. However, such finance is considered to be insufficient and unbalanced since funds are mainly spent on the priority needs.
The situation determines the purpose of this research. We devise and test an algorithm to build a healthcare spending model and optimize it by -estimation of indicators for the current and planned periods in accordance with the morbidity trend in the Russian regions, and analysis of observed scenarios.

Methodology
The research employs methods of logic and statistical analysis. We devised a special algorithm to optimize healthcare spending in the Russian regions. The regional healthcare spending algorithm is based on the assumption that the optimal structure of expenditures is the principal driver of healthcare effectiveness and high quality. In the Russian regions, the structure of healthcare funds is expressed with the set CR = {C 1 , C 2 ,...C m }, which encompasses financial expenditures C 1 , C 2 ,...C m on various types of medical care. When planning and allocating financial resources of the national health system, authorities consider the delivery of primary health and sanitary care (outpatient clinics), specialized healthcare (inpatient and high-tech healthcare facilities), emergency and palliative care. Financial spending on the delivery of i-type of medical care is expressed with the formula: where c i is the unit cost of i-type of medical care, thousand RUB; n i is the number of patients awaiting i-type of medical care in the region, people.
The following condition determines whether healthcare spending in the national health system is optimal or not: CR is optimal only if C i =C i nd , where C i nd is healthcare institutions' needs for financial resources to deliver i-type of medical care. The needs are expressed with the following formula: where n i nd is the number of patients who need i-type of medical care in the region.
As part of the model for optimizing healthcare spending in the Russian regions, healthcare financing is viewed from perspectives of public needs. Funds may be sufficient or scarce. Financial funds for healthcare can be classified into the following types by scarcity/insufficiency: 1) excessively funded (contributing constituents), where 2) optimally funded (normal constituents), where Therefore, expenditures can be classified as follows depending on the structure of financial resources earmarked for medical care: -Sponsor set: all respective constituents are excessively funded and can be viewed as potential sponsors of other types of total expenditures; -Optimum set: the structure is optimal, i.e. all respective constituents are optimally funded and cannot be viewed as sponsors of other types, and recipients; -Chimera set: there are both excessively and insufficiently funded items; -Recipient set: all respective constituents are insufficiently funded.
The financing of the national health system is optimized by reallocating financial resources among types of medical care and hereby transforming ineffective spending schemes into effective ones. If it is impossible to generate the Optimum set, the financial priority is assigned to those types of medical care which are in the highest demand in terms of the risk and emergency theory.
In pursuit of optimization and more effective finance of medical care in the regional health system, patients can be classified by disease, severity and stability of their disease.
As part of a certain disease or a group of diseases, the model of healthcare spending and its optimization is built in the following coordinates:   If D g is less than 100 percent, financial needs are below the planned amount; if D g equals or approximates 100 percent, financial needs correspond with the planned finance; if D g is higher than 100 percent, financial needs exceed the planned finance. If the healthcare spending structure is optimal with respect to classes of patients, paragraph 4 becomes relevant. If the structure is not optimal and the As-Is model is chosen only, paragraph 4 shall be referred to again. If the structure is not optimal and the To-Be model is chosen, paragraph 3 shall be referred to.

Cognitive visualization of results.
The algorithm is implemented via Python 3.
The model was tested while healthcare expenditures were structured and optimized with respect to patients with ischemic heart disease. The unit cost of a certain type and statutory scope of medical care is assessed per person holding a compulsory medical insurance policy by type and form of health care in accordance with the State guarantee program for free medical aid 3 as earmarked for 135 thousand patients with ischemic heart disease.

Results
The regional healthcare spending optimization model treats the finance of healthcare services in terms of the public needs. substantiate financial resources. If we refer to the statutory scope of healthcare services stipulated in under the Statutory guarantee program as a percentage, the breakdown below of key healthcare services will be the most cost effective per person holding a compulsory medical insurance policy: -services of outpatient clinics (healthcare services delivered out of hospital due to a disease) -78%; -emergency healthcare -13%; -specialized healthcare in twenty-four hour hospitals (inpatient care) -7%; -high tech healthcare -2%.
We point out four classes of patients by severity and stability of ischemic heart disease, specifics of their treatment, needs for some type of healthcare services.
Rosebush patients. Treatment is very effective and inexpensive as it results in a protracted remission of a disease so that patients may keep an ordinary lifestyle and continue working. Outpatient care prevails in the rosebush class spending. It is cost effective, very accessible and inexpensive.
Camelthorn patients. High-tech medical services are predominant in the camelthorn spending structure. The treatment is sustainably effective but it requires expensive medical interventions. Such patients seldom suffer from acute conditions of their chronic disease and continue to work given high-tech healthcare services are delivered to them. High costs of this healthcare type stems from the use of innovation and gradually rising indications.
Quicksand patients. Treatment efficacy is not stable, though being rather costly. The disease is severe, with frequently reoccurring acute conditions making patients unable to work as they used to and become physically limited in everyday life. Emergency care and twentyhour inpatient care make the substantial part of expenditures on such patients.
Severe winter patients. This class is associated with the highest costs of twenty-four hour inpatient care and ambulance service. Unplanned costs for high-tech medical care are also incurred. The patients are mostly disabled, thus requiring additional payments for temporary or full loss of ability to work. we obtained as a result of the clinical test research and reassessed the entire population of patients with ischemic heart disease in the region. Fig. 1 depicts the As-Is healthcare spending model, which is based on input data, with respect to classes of patients with ischemic heart disease.
Having analyzed the results, we concluded that the analyzable classes of patients with ischemic heart disease pertain to the Chimera set of healthcare expenditures since each class of financial efficiency has the sponsor constituents and recipient constitutes, namely: -expenditures of the Rosebush class include ambulatory and outpatient care as the recipient constituent (additional funds of RUB 28.32 million are required) and sponsor constituents (excessive funds of RUB 563.8 million); -expenditures of the Camelthorn class include such sponsor constituents as ambulatory and outpatient care and emergency care with excessive funding of RUB 3.9 and 13.9 million respectively, and two recipient constituents -inpatient care and high-tech medical care, which require additional RUB 53. Therefore, healthcare spending on patients with ischemic heart disease cannot be considered as optimal. Hence, measures should be taken to qualify it as such. Fig. 2 shows the healthcare spending optimization model without overlapping with respect to patient with ischemic heart disease (To-Be model).
The utilization of financial resources was optimized by reallocating them among types of healthcare services within a class of patients.
As seen in Fig. 2, after financial resources were streamed from the sponsor constituent to recipient constituents within the same class, the Rosebush expenditures could be reclassified from the Chimera set into the Optimum set. The structure of the other classes remained unchanged. As the next step, all the sets of expenditures shall be brought to the Optimum type, which is feasible only if additional financial resources of RUB 1.12 billion are allocated annually. Most of the funds (RUB 948 million, i.e. 84,6%) should be earmarked for high-tech medical care. We should note that this regional healthcare development course is in accord with departmental special-purpose programs, The 2014-2020 Health System Development.

Discussion
The optimal structure of healthcare spending is modeled to get a more comprehensive and accurate view of resources needed to implement primary goals of healthcare, i.e. substantially reduce the morbidity and mortality rates, cut unplanned healthcare expenditures. Creating the effective mechanism for resource planning, allocation and use in health care, it is necessary to consider the volume and composition of resources needed to deliver healthcare services depending on a disease since the Russian regions have different morbidity trends.
The algorithm we propose allows to consider the morbidity trends in the region and subsequently configure healthcare spending in each region and Russia as a whole. If costs are planned in accordance the financial efficiency classes, there will be sustainable cash flows. Insufficient funding restricts the delivery of healthcare services as needed under the compulsory medical insurance program. In this respect, it is not enough to prioritize expenditures and choose the type, volume of free healthcare services and categories of citizens who are eligible to them. There should be understanding of possible rearrangement of financial flows within the sector. The proposed model has the following advantages.  5. It contributes to the regional allocation of healthcare resources.
6. It enhances the way expenditures are controlled.
Referring to these findings, regional authorities may enhance the management of the regional health system, deciding on each class of diseases and choosing those types of healthcare services which definitely reduce morbidity rates.

Conclusion
Regionally and nationally, challenges of the health system include not only lack of funds but also the effective use of available financial resources. Public healthcare, quality and timeliness of healthcare services will lower morbidity rates, prolong the ability to work and raise labor productivity. The above factors will foster a growth in the national income and public wellbeing.   The healthcare spending optimization model with respect to patients with ischemic heart disease, without overlapping (the To-Be model) Source: Authoring