Turning Healthcare Challenges into Big Data Opportunities: A Use-Case Review Across the Pharmaceutical Development Lifecycle.

In order to draw meaning from the exponentially increasing quantity of healthcare data, it must be dealt with from a big data perspective, using technologies capable of processing massive amounts of data efficiently and securely. The pharmaceutical industry faces the big data challenge through all p...

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Publicado en:Bulletin of the Association for Information Science & Technology Vol. 39; no. 5; pp. 34 - 41
Autor principal: Schultz, Timothy
Formato: tables/charts Journal Article
Publicado: Wiley-Blackwell Jun/Jul2013
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Turning Healthcare Challenges into Big Data Opportunities: A Use-Case Review Across the Pharmaceutical Development Lifecycle.
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        au: Schultz, Timothy
        affil: College of Information Science & Technology (The iSchool), Drexel University
      sug:
        subj:
          Data Management
          Drug Design Trends
          Computers and Computerization Trends
          Cloud Computing
          Genomics
          Bladder Neoplasms Familial and Genetic
          Polymorphism, Genetic
          Monitoring, Physiologic
          Accelerometers
          Collaboration
          Alzheimer's Disease
          Autism Spectrum Disorder
          Patient Protection and Affordable Care Act
          Pharmacovigilance
      ab: In order to draw meaning from the exponentially increasing quantity of healthcare data, it must be dealt with from a big data perspective, using technologies capable of processing massive amounts of data efficiently and securely. The pharmaceutical industry faces the big data challenge through all phases of the drug development lifecycle. Genomics, clinical monitoring and pharmacovigilance illustrate the value of a big data approach. Whether focusing on genetic and environmental disease risks, pattern detection through real time biosensors for patients or post-market monitoring of drug effectiveness, each area involves the collection and analysis of numerous variables and requires extreme computing power to reveal the details of interplay between the variables. To make big data work for pharmaceutical information, attention must be paid to data collection on a vast scale, from multiple sites and over long time periods. Big data support must be incorporated into interoperable electronic medical records and presented intuitively through visual analytics.
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    language: English
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