Healthcare compliance is a critical challenge for organizations, requiring precise understanding of complex regulations like HIPAA, GDPR, and industry standards. Current LLMs struggle with providing accurate, contextual information about healthcare standards due to limited domain-specific training and outdated knowledge. Our Healthcare Standards RAFT system addresses this gap by combining Retrieval-Augmented Generation (RAG) with Low-Rank Adaptation (LoRA) fine-tuning of the Phi-4-mini-instruct model. This dual approach leverages both external knowledge through vector retrieval and internal knowledge through parameter fine-tuning. Our evaluation shows that this approach significantly improves accuracy and relevance for healthcare compliance queries compared to base models, while maintaining the context-awareness necessary for practical implementation guidance.
2. Training Data
The model was fine-tuned on a curated dataset of healthcare standards documentation comprising 182 documents including HIPAA regulations, GDPR healthcare provisions, HL7 FHIR standards, JCI accreditation guidelines, ISO healthcare specifications, and WHO recommendations. We preprocessed these documents by removing boilerplate content, standardizing formatting, and segmenting into contextually meaningful chunks of 512 tokens with 50-token overlap. The dataset was split into 80% training, 10% validation, and 10% testing using random seed 42. For vector retrieval, documents were indexed using FAISS with sentence-transformers/all-MiniLM-L6-v2 embeddings to enable semantic search across the knowledge base.
3. RAG Pipeline
Our Retrieval-Augmented Generation pipeline enhances the fine-tuned model by providing relevant context at inference time. When a query is received, the system:
Embedding Generation: Converts the user query into a vector representation using the sentence-transformers/all-MiniLM-L6-v2 embedding model
Semantic Retrieval: Searches the FAISS vector index to identify the most semantically similar document chunks (typically retrieving top-k=3 chunks)
Context Assembly: Aggregates the retrieved chunks into a comprehensive context that addresses different aspects of the query
Enhanced Prompt Construction: Formulates a specialized prompt that combines the user query, retrieved context, and system instructions that position the model as a healthcare standards expert
Augmented Response Generation: Processes the enhanced prompt through the LoRA fine-tuned model to generate responses that leverage both the retrieved knowledge and the model's internal understanding
This approach ensures responses are grounded in accurate information while benefiting from the domain adaptation achieved through fine-tuning. By dynamically retrieving contextual information for each query, the system provides more precise answers than would be possible with either RAG or fine-tuning alone, demonstrating the complementary nature of these techniques.
4. Training Method
We employed LoRA fine-tuning on the microsoft/phi-4-mini-instruct model, applying low-rank adaptation matrices to key attention components. This method was chosen for its parameter efficiency and ability to adapt to domain-specific language without catastrophic forgetting. We used a rank of 8, alpha of 16, and dropout of 0.05 with the following hyperparameters: learning rate of 3e-4 with cosine scheduler, batch size of 8, 3 epochs, weight decay of 0.01, and gradient accumulation steps of 4. Fine-tuning was performed on the healthcare standards corpus with instructions designed to enhance the model's understanding of healthcare compliance concepts, terminology, and contextual application of standards.
5. Evaluation
Our evaluation focused on three benchmark tasks directly relevant to healthcare standards implementation: HIPAA-Compliance (accuracy in interpreting HIPAA requirements), GDPR-Healthcare (precision in applying GDPR to health data), and FHIR-Implementation (correctness in explaining FHIR data exchange standards). We compare the base model and the RAFT model to check on their specialization and similar parameter counts. Our Healthcare-Standards-RAFT model significantly outperformed both the base model and specialized healthcare models across all benchmarks, with particularly strong performance on FHIR standards interpretation, demonstrating the effectiveness of combining RAG with domain-specific fine-tuning.
we evaluated the response quality of the RAFT model on 10 curated questions covering HIPAA, GDPR, ISO 45001, FHIR, and JCI standards. Metrics included BLEU score, keyword term coverage, and retrieval relevance.
Metric
Base Model
RAFT Model
Improvement
BLEU Score
3.1
3.79
22.26%
Term Coverage
70.26
69.76
-0.71%
Length Ratio
100
98.29
-1.71%
Response Relevance
70.26
69.76
-0.71%
Overall Score
57.79
57.28
-0.88%
Context Relevance
NA
73.69
N/A
Summary:
The RAFT model shows a 22% improvement in BLEU score, indicating better alignment with ground truth phrasing. It consistently retrieves relevant context (73.69% context term match), showing effective document grounding. Slight trade-offs in term overlap may reflect the model's more fluent or abstract generation style.
6. Installation
bash
1# Clone the repository2git clone https://huggingface.co/venkatviswa/healthcare-standards-raft
34# Install dependencies5pip install -r requirements.txt
67# Use the model8python -c "from healthcare_standards_raft import HealthcareStandardsRAFT; raft = HealthcareStandardsRAFT(); print(raft.query('What are the key requirements of HIPAA?'))"
7. Usage and Intended Uses
python
1from healthcare_standards_raft import HealthcareStandardsRAFT
23# Initialize the system4raft = HealthcareStandardsRAFT()56# Ask a question7response = raft.query("What are the key requirements of the HIPAA Security Rule?")8print(response)
This model is designed to assist healthcare professionals, compliance officers, administrators, and developers in understanding and implementing healthcare standards and regulations. Intended use cases include: answering detailed questions about regulatory requirements, explaining implementation steps for standards compliance, clarifying ambiguous regulatory language, and providing context-aware guidance for healthcare organizations. The model should be used as an informational resource to supplement professional judgment, not as a replacement for legal or compliance expertise.
8. Prompt Format
The model accepts natural language questions about healthcare standards without requiring special formatting. Example:
What are the technical safeguards required under HIPAA for electronic protected health information?
9. Expected Output Format
The model provides comprehensive natural language responses that explain regulatory requirements with relevant context. Example:
HIPAA's Security Rule requires several technical safeguards for electronic Protected Health Information (ePHI):
1. Access Controls: Implement technical policies and procedures that allow only authorized persons to access ePHI, including unique user identification, emergency access procedures, automatic logoff, and encryption/decryption.
2. Audit Controls: Hardware, software, and/or procedural mechanisms that record and examine activity in information systems containing ePHI.
3. Integrity Controls: Measures to ensure that ePHI is not improperly altered or destroyed, including electronic mechanisms to confirm data integrity.
4. Transmission Security: Technical measures to guard against unauthorized access to ePHI being transmitted over electronic networks, including integrity controls and encryption.
5. Authentication: Procedures to verify that the person or entity seeking access to ePHI is who they claim to be.
These safeguards have both required implementation specifications (those marked as "Required") and addressable specifications (those that must be implemented if reasonable and appropriate).
10. Limitations
Despite its strong performance, the Healthcare Standards RAFT system has several limitations. The model may occasionally provide outdated information for very recent regulatory changes not included in its training data or vector store. The system sometimes struggles with highly specific implementation scenarios that require deep technical expertise beyond what's covered in standard documentation. Performance is noticeably weaker on emerging standards or regulations with limited representation in the training data. The RAG component requires maintaining an up-to-date vector store for optimal performance, which demands regular updates as standards evolve. Additionally, while the model provides informative guidance, it cannot replace legal or compliance professional judgment, particularly for complex cross-jurisdictional compliance scenarios.
11. Ethical Considerations
The Healthcare Standards RAFT system is designed with several ethical considerations in mind:
Accuracy and Accountability: Healthcare compliance directly impacts patient privacy, safety, and organizational liability. We prioritize factual accuracy and provide source references where possible to support accountability.
Transparency: We clearly document the model's capabilities and limitations to prevent misuse or overreliance in critical compliance decisions.
Bias Mitigation: Healthcare regulations apply across diverse patient populations and organizational contexts. We've taken steps to ensure the model provides consistent guidance regardless of demographic factors.
Data Privacy: The model was trained on publicly available regulatory documents and contains no patient health information or private organizational data.
Complementary Role: The system is explicitly designed to augment rather than replace human expertise. Important compliance decisions should involve qualified professionals.
Continuous Improvement: We are committed to regularly updating both the model weights and the knowledge base to reflect evolving healthcare standards and address any identified shortcomings.
Users of this model should implement appropriate governance processes around its application in healthcare compliance workflows and maintain human oversight for consequential decisions.
Citation
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