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1、McKinsey Center for Government How Big Data Can Revolutionize Pharmaceutical R Peter Groves et al., “The Big-Data Revolution in Health Care: Accelerating Value and Innovation,” January 2013; and Ajay Dhankhar et al., “Escaping the Sword of Damocles: Toward a New Future for Pharmaceutical R&D,” McKin
2、sey Perspectives on Drug and Device R&D, 2012. The big-data opportunity is especially compelling in complex business environments experiencing an explosion in the types and volumes of available data. In the health care and pharmaceutical industries, data growth is generated from several sources, inc
3、luding the R&D process itself, retailers, patients, and caregivers. Utilizing these data will improve the ability of pharmaceutical companies to identify new potential drug candidates and develop them into effective, approved, and reimbursed medicines more quickly. Imagine a future in which the foll
4、owing scenarios are possible: Predictive modeling of biological processes and drugs becomes significantly more sophisticated and widespread. By leveraging the diversity of available molecular and clinical data, predictive modeling could help identify new potential-candidate molecules with a high pro
5、bability of being developed into drugs that act on biological targets safely and effectively. How Big Data Can Revolutionize Pharmaceutical R&D Pharmaceutical R&D suffers from declining success rates and a stagnant pipeline. Big data and the analytics that go with it could be key elements of the cur
6、e. Jamie Cattell, Sastry Chilikuri, Michael Levy 2 Patients are identified for enrollment in clinical trials on the basis of more sourcesfor example, social mediathan doctors visits. Furthermore, to target specific populations, the criteria for including patients in a trial could consider significan
7、tly more factors (for instance, genetic information), thereby enabling smaller, shorter, less expensive, and more powerful trials. To avoid significant and potentially costly issues such as adverse events and unnecessary delays, trials are monitored in real time to rapidly identify safety or operati
8、onal signals requiring action.2 Instead of rigid data silos, which are difficult to exploit, data are captured electronically and flow easily between functionsfor example, discovery and clinical developmentas well as to external partners such as physicians and contract research organizations (CROs).
9、 This 2 The term “adverse events” means harm to or death of trial participants. easy flow is essential for powering the real- time and predictive analytics that generate business value. Thats the vision. However, many pharmaceutical companies are wary about investing significantly in improving big-d
10、ata analytical capabilities, partly because there are few examples of peers creating a lot of value from it. However, we believe that investment and value creation will grow and that pharmaceutical companies would do well to get over their hesitation. The road ahead is indeed challenging, but the bi
11、g-data opportunity in pharmaceutical R&D is real, and the rewards will be great for companies that succeed. The big-data prescription for pharmaceutical R&D Our research suggests that by implementing eight technology-enabled measures, pharmaceutical companies can expand the data they collect and imp
12、rove their approach to managing and analyzing these data. Integrate All Data Having access to consistent, reliable, and well- linked data is one of the biggest challenges facing pharmaceutical R&D. The ability to manage and integrate data generated at all stages of the value chainfrom discovery to r
13、eal-world use following regulatory approvalis a fundamental requirement for companies that aim to derive maximum benefit from technology trends. Data are the foundation upon which value-adding analytics are built. Effective end-to-end data integration establishes an authoritative source for all piec
14、es of information and accurately links disparate data regardless of the sourcebe it internal or external, proprietary or publicly available. Data integration also enables comprehensive searches for data subsets based on the linkages established rather than on the information itself. Smart algorithms
15、 that link laboratory and clinical data, for example, could automatically create reports that identify Tremendous value could be unlocked by applying big-data techniques to pharmaceutical R&D. Eight technology-enabled measures including portfolio decision-making support and the use of smart devices
16、should expand data collection and improve data management and analysis. Companies that can overcome challenges related to organization, analytics, and mind-sets could see a much-needed boost in R&D innovation and efficiency. 3 related applications or compounds and raise red flags concerning safety or efficacy. The implementation of end-to-end data integra- tion requires a number of capabilities, including trustworthy data and document sources, the abil- ity to establish cross-linkag
