Hope Noah: Is All-Inclusive Pricing an Effective Strategy for the Medical Tourism Industry?
Bibliographic record
Abstract
厚朴诺亚健康管理（北京）有限公司（以下简称厚朴方舟）是国内最早从事海外医疗服务的机构之一。经过几年的发展，公司已树立了良好的口碑，经营状况渐入佳境。但CEO王刚却不满足于此，他希望能给顾客带来“好”“快”“省”的海外医疗服务。在他看来，目前海外医疗服务市场上按服务量、时长和次数计费的模式在本质上与患者利益相悖：患者越治不了、治不好、拖得越久，收费就越高。因此，王刚考虑能否提供海外医疗包干（即以一价全包的方式兜底患者的医疗费、服务费以及围绕医疗产生的其他所有非医疗支出）这一新的定价模式，从而使企业和患者的利益统一起来，既免除顾客对费用不确定性的担忧，同时倒逼企业提升效率。但由于医疗本身的不确定性太高，这一想法遭到了其他管理层的一致反对。王刚到底该如何决策？
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 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".