ORGANIZATIONAL AND ECONOMIC MECHANISM OF FUNCTIONING OF THE HEALTH INSURANCE SYSTEM
Bibliographic record
Abstract
The article examines the organizational and economic mechanism of the medical insurance system functioning in Ukraine and worldwide. The dynamics of premiums and payments for health insurance in Ukraine for the period 2022-2025 has been analyzed, which demonstrates stable market growth with a growth rate of seventeen percent in the first quarter of 2025. The structure of insurance premiums by different lines of business has been considered, where health insurance accounts for eighteen percent of total premiums. The international experience of organizing medical insurance has been studied using examples from Germany, France, the Netherlands, Sweden, Great Britain, and the United States. The main models of healthcare financing and features of their organizational and economic mechanisms have been identified. A comparative analysis of medical insurance indicators in OECD countries and Ukraine has been conducted. The article reveals that the global medical insurance market is growing at approximately six and a half percent annually, with significant variations across different countries depending on their healthcare system models. Countries with mandatory private health insurance, such as the Netherlands and Switzerland, demonstrate the highest share of medical insurance in non-life insurance premiums, reaching eighty-two and fifty-one percent respectively. The study shows that medical insurance inflation in most OECD countries exceeds general inflation, reflecting rising healthcare costs and demographic changes. In Ukraine, the medical insurance market is developing dynamically, with premiums reaching six point two billion hryvnias in the first quarter of 2025, though the coverage remains relatively low at five to seven percent of the population having private insurance. Directions for improving the organizational and economic mechanism of the medical insurance system functioning in Ukraine have been proposed, taking into account international experience and national peculiarities, including the development of competitive environment, expansion of medical facilities network, implementation of quality standards, and creation of effective settlement mechanisms between insurance companies and healthcare providers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".