The present-day life sciences industry needs to effectively and swiftly organize the rising volumes of structured and unstructured data from myriad systems such as EMRs into comparable formats, analyze them, and visualize the findings. With standard statistical methodologies like regression, the time taken to analyze all the data takes weeks that negatively impacts the industry in real-time diagnoses and development of precise medicines. In most cases of pre- and post-launch drug failures, improperly designed clinical trials, unsuitable patient population, and lack of competitive differentiation are the main reasons. Analytics-driven insights help the life sciences industry to improve their understanding of clinical trial factors and discover unforeseen patterns that could result in the formulation of new hypotheses for better and more improved medicines. In addition, analytics can significantly accelerate drug discovery and development by mapping patient trends to identify target markets, highlighting gaps in the efficacy and safety of current drugs, and narrowing down on test sites with high patient availability.
Although the the life science industry has experienced various challenges in terms of operations like patent cliffs, approval of generics, growing investments in R&D, and stringent regulatory guidelines and compliances, the use of analytical tools and techniques aid in conquering these challenges related to data integration. Some of the key applications of data analytics, including product enablement, marketing support, regulatory compliance reporting, sales support, and data integration assist the industry in adopting digital records for maintaining high standards and sophistication apart from committing minimal errors. Besides, innovative technologies like AI, machine learning, national language processing (NLP) are being used to improve the operations in producing new medicines, to connect better with the patients and understand their needs, and further enhance their sales and marketing functions. These technologies, combined with various analytics tools, facilitate sales executives in quickly sharing information with physicians, developing tailored messages, and more meaningful interactions with patients.
While AI can help drug makers to create new and improved treatments and find ways to get patients’ access to treatment immediately, machine learning algorithms easily classify compounds that have similar effects together and passes on the clean data to drug makers. Moreover, the NLP solutions can detect information like previous research documents within unstructured data that may add value to the drug discovery process.
Today, the life science industry needs to reduce healthcare costs, improve patient outcomes, and enable access to health information for profitable business and better customer experience. And, to assist this industry in taking advantage of the emerging technologies in life sciences analytics, the upcoming edition of Life Sciences Review features a list of leading vendors along with insightful articles from thought leaders in the life sciences analytics arena.
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