Helping Pharmaceutical Companies respond to Patient Queries
Techniques:
Objectives
We worked with a health tech company to develop an AI assistant to assist representatives in answering patient questions more efficiently.
Problem
We worked with a health tech company that provides call centers with technology to improve the efficiency of handling diverse pharmaceutical inquiries from people with different roles and responsibilities (Sales Representatives, Medical Liaisons, etc.). Their goal is to provide accurate, quick, and up-to-date information about treatments from various pharmacological companies.
Solution
We helped them implement an Artificial Intelligence (AI) driven call center assistant. Official information about treatments from various pharmaceutical companies was processed, split, and grouped by meaning (semantically), then stored in a database that allows searching by similarity (called a vector store). A Large Language Model (LLM) was integrated to manage conversations with users, making the experience similar to speaking with a pharmaceutical company representative.
Impact
The assistant guides humans in call centers, improving the speed and quality of responses to customer queries. By providing precise and timely information, customer satisfaction and efficiency are guaranteed.
