Next-Gen Pharma Communication: Revolutionizing Doctor-Pharma Relationships Using AI-Driven Messaging & Insights


Authors : Vasireddy Surya; Rooma Tyagi; Vineel Sai Kumar Rampally; Shirish Kumar Gonala

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/5x38rs5p

Scribd : https://tinyurl.com/mt9t4sb2

DOI : https://doi.org/10.38124/ijisrt/25apr027

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Abstract : The pharmaceutical industry is a highly competitive industry which strives to build strong, meaningful relationships with doctors but, often struggles to maintain meaningful and timely communication with doctor’s post- meetings. Conventional engagement strategies such as manual post-visit communication is time-consuming, inconsistent, and lacks personalization and often results in generic interactions, reducing the impact of medical representatives' efforts and brand recall. To increase timely and relevant interactions between the pharmaceutical representatives and doctors, we aim to create an AI-driven post-visit messaging service that proactively improves engagement via automated bespoke messages responding to the representative’s conversation with the doctor. The proposed solution integrates LLaMA 3 (Large Language Model Meta AI), the system is fine-tuned to understand the sentiment and intent and learns from previous pharma representative inputs and generates personalized thank you or feedback messages based on the pharma representative’s input. The AI model ensures messages remain highly professional, relevant, and aligned with brand guidelines. A standalone application for the existing pharma company app that the representatives would deploy is created. Minimal post-visit feedback is provided by the representative to be converted by the model using tuned NLU (Natural Language Understanding), which it integrates with. The structured answer is transformed into a message using dynamic AI featurette system, and sent out through available email, SMS, or WhatsApp thus closing the engagement loop. The entire system is fully automated, guaranteeing compliance while maximizing efficiency. The system maximizes engagement and trust by ensuring every interaction correlates with the representative's message, thus building long lasting relationships with the doctors, and enables the company to stand out in a crowded market, strengthening the firm’s dedication to authentic relationships with physicians which ultimately enhances the company’s competitive advantage. Encourage effective communication using AI personalization’s builds trust and dramatically changes market penetration and physician retention.

Keywords : AI-Driven Messaging, Personalized Interaction Strategy, Llama 3 Fine-Tuning, NLU (Natural Language Understanding), Python Dynamic Message, Doctor Engagement, Brand Recall & Trust.

References :

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The pharmaceutical industry is a highly competitive industry which strives to build strong, meaningful relationships with doctors but, often struggles to maintain meaningful and timely communication with doctor’s post- meetings. Conventional engagement strategies such as manual post-visit communication is time-consuming, inconsistent, and lacks personalization and often results in generic interactions, reducing the impact of medical representatives' efforts and brand recall. To increase timely and relevant interactions between the pharmaceutical representatives and doctors, we aim to create an AI-driven post-visit messaging service that proactively improves engagement via automated bespoke messages responding to the representative’s conversation with the doctor. The proposed solution integrates LLaMA 3 (Large Language Model Meta AI), the system is fine-tuned to understand the sentiment and intent and learns from previous pharma representative inputs and generates personalized thank you or feedback messages based on the pharma representative’s input. The AI model ensures messages remain highly professional, relevant, and aligned with brand guidelines. A standalone application for the existing pharma company app that the representatives would deploy is created. Minimal post-visit feedback is provided by the representative to be converted by the model using tuned NLU (Natural Language Understanding), which it integrates with. The structured answer is transformed into a message using dynamic AI featurette system, and sent out through available email, SMS, or WhatsApp thus closing the engagement loop. The entire system is fully automated, guaranteeing compliance while maximizing efficiency. The system maximizes engagement and trust by ensuring every interaction correlates with the representative's message, thus building long lasting relationships with the doctors, and enables the company to stand out in a crowded market, strengthening the firm’s dedication to authentic relationships with physicians which ultimately enhances the company’s competitive advantage. Encourage effective communication using AI personalization’s builds trust and dramatically changes market penetration and physician retention.

Keywords : AI-Driven Messaging, Personalized Interaction Strategy, Llama 3 Fine-Tuning, NLU (Natural Language Understanding), Python Dynamic Message, Doctor Engagement, Brand Recall & Trust.

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