AI & HealthcareJanuary 31, 2025

Healthcare Chatbot — Slashing Costs While Maintaining Quality

By Comerit Team

All Articles

The Challenge

A healthcare organization needed a patient intake chatbot to streamline front-desk operations and improve the patient experience. However, they faced significant obstacles with their initial GPT-3.5 approach.

  • Prohibitive usage fees — High-volume patient interactions made API costs unsustainable at scale.
  • Data sensitivity — Patient health information demanded on-premise infrastructure for regulatory compliance and data governance.

The organization needed a solution that delivered comparable conversational quality at a fraction of the cost, while keeping all patient data within their own infrastructure.

The Solution

Comerit deployed a purpose-built AI system tailored to the healthcare organization's specific requirements.

  • Fine-tuned Mistral-7B — A customized open-source model trained specifically for healthcare patient intake operations, delivering results comparable to GPT-3.5 at dramatically reduced expense.
  • On-premise infrastructure — Complete data governance and privacy compliance through self-hosted deployment, eliminating third-party data exposure.
  • Comprehensive quality assessment — Performance evaluated across four key dimensions:
    • Accuracy in query interpretation
    • Fluency of communication
    • Comprehension of medical terminology
    • Empathy in patient interactions

The Results

The fine-tuned Mistral-7B deployment exceeded expectations across every measured dimension.

  • Performance parity — Achieved results on par with GPT-3.5 across all key metrics.
  • Consistent quality — High-quality patient interactions maintained across thousands of daily conversations.
  • Complete data privacy — All patient data remained within the organization's on-premise infrastructure.
  • Substantial cost reduction — Operational costs reduced significantly compared to the original cloud-based approach.

This engagement demonstrates that open-source models, when properly fine-tuned and deployed, can match proprietary alternatives while providing superior cost control and data governance.

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