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Case study: Informatics Changing the Future of Data: Powerful new API solutions from Pharma Intelligence

Informatics Solutions at Informa Pharma Intelligence recently helped a global pharmaceutical client assess candidates faster for potential M&A or licensing opportunities. They already used Biomedtracker and Datamonitor Healthcare but were still worried that manual data extraction from multiple sources left them vulnerable to competitors outpacing them.


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Case study - Informatics make smart decisions for your R&D Projects: How Pharma Intelligence APIs provide trusted data delivered on your terms

Timely, accessible data is your competitive edge

APIs and Custom Analytics from Pharma Intelligence Informatics Solutions provide pharma leaders with fast and easy access to accurate and current information so you can make smart, evidence-based decisions about strategic planning, licensing and partnerships:

  • Stream information directly from our renowned databases into your systems to embed our high-quality datasets directly into your organization’s own workflows
  • Quickly integrate BMT/Meddevicetracker data into your internal systems and build customized dashboards to quickly and dynamically visualize analysis
  • Understand the market landscape, competitive products, the phases of development for drug trials, what trials are being run, and key events that are happening
  • Custom Analytics provide the information you need to confidently make strategic decisions
  • Generate powerful insights without time-consuming manual data collection
Research and Development

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Case Study - Make smart decisions for your R&D Projects: how our Pharma Intelligence APIs provide a harmonised view of the Drug and Device competitive landscape

A Pharma Competitive Intelligence team at a global Pharmaceutical company

To identify and track competitor drug and companion diagnostic pairs.

The Challenge
Their current approach involved running three searches within Biomedtracker (BMT) and Meddevicetracker (MDT), exporting three excel fi les and then manually manipulating, combining and uploading this data into a visualisation tool. This would then need to be updated weekly, using up valuable time.



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