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Industry
Life Sciences
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Patient-centered Care Models Taking Center Stage in Life Sciences
Read how patient-centric care models and pharma innovation are ushering new trends in life sciences to improve patient outcomes and enrich lives.
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Know how regulatory teams can overcome hurdles like unclear IVD/CDx regulatory path and lengthy drug approval time to offer personalized and comprehensive care.
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23 Jul 2024
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TCS ADD™
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TCS ADD™: A Suite of Modern and Open Technology Platforms
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25 Oct 2022
Staying up to date with regulatory requirements, especially in the field of life sciences, is extremely important.
Life sciences organizations must adhere to these changing regulations and take data-driven decisions. Manual regulatory website search process is extremely time-consuming and effort-intensive. The sheer data volume, extracting information from unstructured data, limitations of the tools, expertise, and effort required to absorb all the data and interpret the insights for decision making are some of the inherent challenges today. Compliance to data privacy regulations like GDPR (General Data Protection Regulation) and HIPAA (Health Insurance Portability and Accountability Act) further adds to the complexity.
Conversational AI-based solutions have advantages such as natural interaction, contextual awareness, accessibility, scalability, personalization, and multilingual capabilities. LLM (Large Language Model) capabilities include natural language processing, text generation, information retrieval, reasoning, and personalization.
Conversational chatbots in tandem with GenAI solutions based on LLMs are useful to address the challenges with regulatory intelligence. They provide reliable, consistent, and robust building blocks to offer conversational assistance to regulatory professionals.
By utilizing conversational AI, it is simpler to implement user flow and accommodate business logic easily instead of relying on LLMs to guess the flows. The domain knowledge can be stored in a knowledge repository for easy access.
LLMs will help in making the chatbot with minimal training of intents and business flow and providing appealing responses to user queries.
The solution comprises of a natural language understanding (NLU) engine capable of performing intent classifications, entity extraction and dialogue management, LLMs, and a knowledge repository.
The regulatory assistance chatbot provides an easy-to-use interface to regulatory professionals by eliminating the need to visit different sources. It can provide real-time updates, historical information, and guidance on regulatory changes. The knowledge repository gives the chatbot a contextual understanding of the domain and business knowledge. All these together make it possible to devise an effective regulatory strategy. This ultimately leads to improved compliance, faster decision-making, and better patient outcomes.
Please visit the link below to read the complete paper from where these excerpts have been extracted:
About the authors
Image of Saurabh Das
Saurabh Das
Saurabh Das is part of Life Sciences, Advanced Drug Development (ADD) at TCS. In his current role, he is the Product Manager of TCS' pharmacovigilance platform: Integrated Patient Safety.
Image of Rohit Kadam
Rohit Kadam
Rohit Kadam is a Researcher at TCS. He is associated with TCS ADD as a Researcher in the R&I Team for nearly four years and contributed extensively in developing key solutions for the TCS healthcare domain.
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