AstraZeneca (China): Leveraging Offline Doctor-Patient Relationships in Online Healthcare Service Platform
Bibliographic record
Abstract
The sales of various drug products for AstraZeneca, a multinational pharmaceutical company, have been greatly impacted by the volume-based procurement resulting from the medical reform in China. Meanwhile, the Covid-19 epidemic has catapulted the internet pharmaceutical industry into rapid development. When crises are intermingled with opportunities, AstraZeneca attempted to establish Yiliyili.com, an internet medical service platform, by leveraging its offline resources. Chen Hua, the CEO of Yiliyili.com, has 14 years of experience in pharmaceutical e-commerce and has set up an innovative “doctors-to-pharmaceuticals” model in contrast to the mainstream that runs otherwise. This model was created through collaboration with doctors from contracted offline hospitals who were engaged online. The goal was to establish a stable and active group of doctor users while providing them with empowerment and supporting services. The objective was to replicate existing doctor-patient relationships in an online setting. Meanwhile, it has extended services in physical medical and pharmaceutical products to value-additive services such as case management and compliance-related services for patients. It has also extended the provision of prescription drugs other than essential drugs at non-public offline hospitals according to patients’ needs to enhance its marketing abilities for such drugs. Furthermore, it has used its marketing capacity for prescription drugs to establish cooperation with other medical appliance manufacturers to form an integrated marketing platform through which it could average down its fixed costs for maintaining a huge marketing team while better-utilizing marketing capacities. Can Yiliyili.com gain a competitive edge over its traditional competitors by implementing a service-heavy strategy and utilizing its offline strengths to outperform its online advantages? Will it be able to establish itself as a top-notch provider of exceptional and cost-effective services amidst the Internet hospital construction boom?
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".