AstraZeneca (China): Leveraging Offline Doctor-Patient Relationships in Online Healthcare Service Platform
Bibliographic record
Abstract
跨国药企阿斯利康公司的多个产品在医保主导的带量采购中受到巨大冲击，同期，新冠肺炎疫情推动了医药互联网行业的发展。在“危中有机”的局面下，阿斯利康尝试结合自身线下资源搭建互联网医院“慧医天下”。 有14年医药电商经验的CEO陈华，对主流互联网医院“由药到医”的模式，反其道而行之，探索出一条“由医到药”的发展战略：以其线下资源带来医生流量和粘性，从赋能和服务好医生入手，将线下的熟医患关系搬到线上，以实体医药产品为基础提供患者病程管理和用药依从性的增值服务，在线下公立医院以外按照患者需求提供基本用药以外的处方药，从而保持和提升其处方药销售能力，并利用其处方药销售能力接入其他的药械生产商，形成销售平台，分摊其维持庞大线下销售团队的固定成本，扩大产出。 这种以线下优势博弈具有线上优势的传统互联网医院、以重服务模式博弈传统互联网医院的轻服务模式，是否能在快速发展的互联网医院建设热潮中赢得竞争优势？是否有机会成为提供具有成本效益的卓越医疗服务的典范？
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".