Challenges:

For MBCHC, providing high-quality healthcare to underserved communities was always the top priority. However, outdated documentation workflows were creating a serious roadblock. 

Physicians were spending too much time manually writing or dictating notes—an inefficient, inconsistent process that slowed down patient interactions and increased administrative burden. These challenges led to: 

• Excessive time spent on documentation, limiting patient interaction. 

• Inconsistent medical records, leading to potential errors and compliance concerns. 

• Physician burnout, with doctors overloaded by paperwork instead of focusing on patient care. 

• Regulatory pressures, requiring strict compliance with HIPAA and data privacy regulations. 

MBCHC needed a modern, AI-powered solution that could reduce the documentation workload, maintain high medical accuracy, and integrate seamlessly with existing healthcare systems. 

Our Strategic Approach 

Recognizing the need for a domain-specific AI assistant, Kmeleon developed and fine-tuned a private LLM tailored to MBCHC’s unique requirements. Instead of relying on generic AI models, we created a solution that could understand, structure, and enhance medical annotations while maintaining strict compliance standards. 

Our approach included: 

• Fine-Tuning with Weights & Biases (W&B) – Leveraging W&B’s experiment tracking and optimization tools, we refined the model using real doctor-patient interactions, ensuring high accuracy and medical specificity. 

• A secure, HIPAA-compliant training environment – Implementing data encryption, anonymization, and strict access controls to protect patient records. 

• An enterprise-ready deployment on Azure AI Foundry – The fine-tuned model was mounted onto Azure AI Foundry, ensuring enterprise-grade security, governance controls, and seamless cloud integration. 

• Seamless EHR integration – Physicians could dictate, review, and edit AI-assisted notes in real time, allowing for faster and more structured documentation. 

The AI model was designed to not only generate structured and contextually relevant documentation but also adapt and improve over time, ensuring long-term efficiency. 

Implementation: From Development to Deployment 

To ensure the AI system was optimized for MBCHC’s needs, a rigorous fine-tuning and deployment strategy was implemented: 

1. Data Preparation and Compliance – Medical records were anonymized, ensuring data privacy and HIPAA compliance before training. 

1. Model Training and Optimization – W&B’s monitoring tools allowed for continuous hyperparameter tuning, ensuring optimal performance and accuracy. 

1. Secure Enterprise Deployment – The fine-tuned model was deployed within Azure AI Foundry, allowing for scalable, compliant, and real-time medical documentation processing. 

1. Continuous Learning and Adaptation – Real-time feedback loops allowed the model to improve with ongoing physician interactions, ensuring long-term accuracy. 

This end-to-end implementation ensured the AI system was not only powerful and accurate but also fully compliant with industry standards.