Impact of Business Operations and Policies on Healthcare Costs
Bibliographic record
Abstract
It is no surprise that the United States has a problem managing its healthcare costs. To get insurance to cover medical costs, people have to stay in-network, pay their premiums, yearly fees, meet their deductible–which is the maximum out-of-pocket cost–pay a copay–a fee for a physician visit or prescription refill–and finally pay their co-insurance: the percentage of a medical bill the customer has to pay. The United States has access to some of the best medicine in the world with state of the art MRIs, which much research and development happening in the United States, and “the U.S. has four times the number of MRIs per capita as Canada, and three times the number of cardiac surgeons” (Cutler, 2020). There are many reasons as to why healthcare costs are so high in this country, one major reason is inflated health administration costs. This paper examines what is the cause for high health administration costs, and aims to find policies that could be implemented to lower these costs.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".