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Pharma Tech Outlook | Wednesday, September 30, 2020
Natural language processing can help pharmaceutical companies overcome compliance challenges.
FREMONT, CA: Natural Language Processing (NLP) is an Artificial Intelligence (AI) technology that is programmed to mine and interpret unstructured text-based documents, extract key information, and turn it into structured information, which can then be analyzed by a computer. NLP also plays a critical part in pharmaceutical enterprises' digital transformation efforts, with almost the entire industry embracing NLP in some way. Let’s now take a look at how NLP can help pharma companies overcome compliance challenges.
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Management of Compliance Demand: Conventionally, pharmaceutical compliance professionals have been the most hesitant to implement technology, but new legislation and standards are forcing this transition. NLP can help solve the problem of timely reporting when managing increased data complexity. It does so by integrating and comparing adverse events with new patient data from decades of existing data that can be collected and analyzed in near real-time.
Comprehension of Data Sources: One of the major problems that emerge with the availability of so much information in unstructured formats is the risk of missing out on something. An adverse condition is not necessarily reported consciously by the average patient, nor is it reported in absolute terms. In such situations, NLP plays a bigger role than can be imagined. The efficiency of manual data processing would eventually decline with more data sources available. NLP would, therefore, become necessary for these increasing vital data sources to continually make sense.
Drive Innovation: For pharmaceutical enterprises, compliance has seen as a cost center for the longest time. The industry is, however, waking up to how automation can transform the entire role of safety and regulatory departments through NLP and other technologies. Freeing up resources from the complexities of manual reporting allows businesses to reinvest in activities that generate real market value, with the ability to evaluate the depth and reach of previously untapped data to fuel future activities in research and development.
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