Challenge
A leading healthcare company sought to develop a patient intake chatbot to streamline their processes and enhance patient experience. Initially, they considered using GPT-3.5, but this presented two major hurdles:
- High Operational Costs: GPT-3.5, while powerful, comes with significant usage costs, especially for a high-volume application like patient intake.
- Privacy Concerns: Due to the sensitive nature of patient data, on-premise deployment was essential, which further limited their options.
Solution
To overcome these challenges, Comerit implemented a strategic solution:
- Fine-tuned Mistral-7B: We selected the Mistral-7B large language model (LLM) as a foundation and fine-tuned it specifically for the healthcare company's needs and data. This allowed us to achieve comparable performance to GPT-3.5 while significantly reducing costs.
- On-Premise Deployment: We deployed the fine-tuned Mistral model on the company's premises, ensuring complete control over their data and addressing privacy concerns.
- Rigorous Evaluation: To ensure the chatbot met the highest standards of quality and accuracy, we implemented comprehensive evaluation metrics, including:some text
- Accuracy: Measuring the chatbot's ability to understand and respond correctly to patient queries.
- Fluency: Assessing the naturalness and coherence of the chatbot's responses.
- Comprehension: Evaluating the chatbot's ability to understand complex medical terminology and patient requests.
- Empathy: Measuring the chatbot's ability to provide supportive and empathetic interactions.
Results
The results of this approach were remarkable:
- Performance Parity: The fine-tuned Mistral-7B model achieved performance on par with GPT-3.5 across all key metrics.
- High Quality: The chatbot consistently maintained high quality scores, ensuring accurate, fluent, and empathetic interactions with patients.
- Enhanced Privacy: On-premise deployment guaranteed the privacy and security of patient data.
- Dramatic Cost Reduction: By leveraging the Mistral model and optimizing the deployment strategy, we significantly reduced the healthcare company's operational costs compared to using GPT-3.5. This translated into substantial savings without compromising quality or performance.
This case study demonstrates how Comerit can help businesses achieve their AI goals while optimizing costs and prioritizing data privacy. By strategically selecting and fine-tuning LLMs, we deliver powerful solutions that meet the unique needs of our clients.
