AstraZeneca (China): Promoting Social Innovation with Holistic Disease Management Solutions throughout the Patient Journey
Notice bibliographique
Résumé
Since 2018, AstraZeneca ("AZ" mentioned herein refers to AstraZeneca Investment (China) Co., Ltd. unless otherwise specified) has been one of the top-performing pharmaceutical multinationals in China in terms of sales, thanks in part to the leadership of its President, Leon Wang. In 2013, Wang joined AZ as Vice President of the Gastroenterology, Respiratory & Anesthesia Division. He spearheaded the project of building pediatric nebulization centers in lower-tier cities and smaller hospitals throughout China, and increased the sales of products such as Pulmicort Respules after their patents expired, generating significant social benefits by making medical treatment and medicines more accessible in lower-tier markets. Wang was promoted to President in less than two years after joining AZ. While working primarily to boost sales in China, Wang introduced another task that was not tied to performance targets: innovation. To facilitate innovation, AZ reached beyond the pharmaceutical sector and worked with partners in the "3D" (diagnostics, device, digital) industries to launch "a patient-centric, integrated disease diagnosis and treatment platform". This initiative enabled pharmaceutical companies, which had acted only as drug suppliers during treatment, to engage in all stages of the patient journey, from education and screening, diagnosis and treatment, to follow-up and rehabilitation. Their first innovation project was the construction of pediatric nebulization centers, which were equipped with smart nebulizers powered by IoT and digital technologies. Following the initial success, AZ co-launched the chest pain center (CPC) project and the prostate cancer integrated diagnosis and treatment (PiDT) project, which not only delivered benefits to patients and hospitals, but also granted partners access to hospital resources. The integrated diagnosis and treatment platform focused on areas where AZ excelled, such as respiratory and cardiovascular diseases and diabetes, so that participating hospitals and patients could "naturally" choose AZ's products. However, a closer look at its sales revenue revealed that AZ was not always the primary beneficiary of the platform, and its return on investment proved to be modest. As the platform’s champion, Wang had to walk a fine line between pursuing business value and generating social value: AZ's global headquarters made it a strict rule that social innovation could not be pegged to sales, which meant medical representatives were not allowed to use the innovation platform to sell drugs. There were also internal debates on "peripheral" innovation projects, as some people were concerned about the amount of financial and human resources invested in them. The headquarters was wary of the business innovation in China, but willing to keep an open mind given the robust performance and growing contribution of the Chinese market. Nevertheless, Wang made up his mind to double down on innovation. By upgrading the integrated diagnosis and treatment platform and leveraging China's new drug R&D platform and the global healthcare industrial fund, he aimed to create synergies between the company’s business growth, social responsibility, internal innovation, and social co-innovation, and ultimately transform AZ into a patient-centric, service-oriented, and platform-based company. Will his efforts pay off?
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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