Medical Savings Accounts: will they reduce costs?
Bibliographic record
Abstract
BACKGROUND: Medical Savings Accounts are an attempt to reduce health care costs by transferring responsibility for expenditures to patients, while providing them with state-supported base amounts to cover some of the costs. We wondered whether such a system would actually be effective, given the fact that medical care expenditures (and illness) are unequally distributed across the population. METHODS: We used the Manitoba Population Health Research Data Respository to assess costs incurred by individual residents of Manitoba for all physician visits and admissions to hospital between 1997 and 1999, and we calculated an average expenditure per person per year over the 3 years. RESULTS: During fiscal years 1997-1999, physician and hospital costs that could be attributed to individual Manitoba residents averaged $730 each year. Most users accounted for very little expenditure. About 40% of the entire population of Manitoba used less than $100 each, and 80% used less than $600. The highest-using 1% of the Manitoba population accounted for 26% of all spending on hospital and physician care, whereas the lowest-using 50% accounted for 4%. When examined by age category, the results were similar. Even in the highest age category, most of the population falls into the low-usage category. If the entitlement under a Medical Savings Account scheme was set at the current average cost of $730 per year, then total spending by government on health care for this healthy group would increase (by $505 million) rather than decrease. If the "catastrophic threshold," above which the insurer would pay costs, was set at $1,000 per year, then the sickest 20% of Manitoba residents would become personally responsible for just over $60 million of current health care costs. The net result is a 54% increase in spending on hospital and physician costs that can be allocated to individuals. INTERPRETATION: Medical Savings Accounts will not save money but will instead, under most formulations, lead to an increase in spending on the healthiest members of the population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".