Projection of Future Medical Expenses Based on Medical Needs and Physician Availability
Bibliographic record
Abstract
BACKGROUND: Accurate scientific projections of future healthcare expenditures and workforce capacity are vital in South Korea for addressing concerns about the sustainability of the national health insurance system. This study aims to analyze projected changes in healthcare expenditures due to demographic shifts and identify appropriate healthcare workforce to meet future demands. METHODS: Data from Statistics Korea, the National Health Insurance Service, the Bank of Korea, and the Korea Development Institute were used. The Stepwise Auto Regression Model projected healthcare costs and insurance rates, considering future population estimates, the proportion of older people in the population, life expectancy, changes in medical cost rates, nominal Gross National Income, and the ratio of current medical expenses on Gross Domestic Product (GDP). The analysis applied two scenarios: maintaining the current medical school admission quota and increasing it by 1,509 students. RESULTS: The study anticipates a rise in future medical insurance rates alongside a gradual decline in the rate of change in medical costs. The demand for medical services is forecasted to grow by over 4% annually for the next 30 years due to an aging population and low birth rates. The ratio of current medical expenses on GDP is projected to increase significantly, reaching approximately 20.0% in 2060 from 9.7% in 2024. In two scenarios: if 3,058 medical students are added to the existing medical license holders, medical costs per active physician will increase by 2.8 billion won; if 4,567 medical students are added, the costs will increase by 2.3 billion won by 2060. Despite 1,509 new medical students annually, the number of active physicians will increase by only 1% per year, starting a decade later. Consequently, the medical market will continue to expand, and the demand for medical services per physician will not decrease. Health insurance rates are expected to rise steadily from 7.09% in 2024 to 14.39% by 2060. CONCLUSION: This underlines the imperative to prioritize enhancing the sustainability of the healthcare system over solely augmenting medical student numbers. We should scientifically and precisely predict future medical costs and consider deeply whether it is right to shift the burden of intergenerational medical care to future generations at this point.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".