How to Make Any AI Model HIPAA Compliant (It's Easier Than You Think)
Chris Raroque
Co-Founder
How to Make Any AI Model HIPAA Compliant (It's Easier Than You Think)
Most people think they can't use GPT or Claude because they're not HIPAA compliant—and they're right. But what a lot of people don't realize is there's a way to make almost any model HIPAA compliant, and it's not as expensive or complicated as you might think.
At Aloa, we recently solved this exact problem for a HIPAA-compliant medical transcription app we built for a medical group. Here's how we did it and how you can do it for your own health care applications.
What is HIPAA Compliance?
HIPAA regulations (Health Insurance Portability and Accountability Act) are basically laws that protect patient privacy. If your app handles any patient health information or electronic health records, you need to follow strict rules about how you store, share, and protect that data.
If you want to use ChatGPT, Claude, or any AI tool with patient data, you need something called a Business Associate Agreement (BAA). This is a legal contract that says "we promise not to misuse your sensitive patient data or train our AI models on it." Without this BAA, using AI with patient information violates HIPAA regulations.
It sounds complicated, but HIPAA compliance is really about keeping patient health information safe and addressing security risks that could harm patients or expose sensitive patient data.
The HIPAA Compliance Challenge
We hit this wall immediately when building the transcription app for handling electronic health records and patient health information. We wanted to use the best models like GPT-4 and Claude, but they're not HIPAA compliant out of the box for processing sensitive patient data in electronic form.
Standard APIs like going directly to OpenAI don't provide the necessary security measures and compliance solutions required by the Department of Health and Human Services. This creates significant potential risks for health care providers. So most health care organizations either:
- Don't use AI at all, or
- Use local models that live on their own servers (which are usually not as powerful as cloud-hosted models)
Both approaches limit the potential for improving patient care while maintaining compliance with HIPAA regulations and avoiding disclosure of PHI.
The Secret: HIPAA-Compliant Cloud Providers
Here's what most people don't know: there are actually cloud providers that host these same models and offer comprehensive compliance solutions for handling electronic protected health information. They'll sign that BAA with you, provide your own dedicated infrastructure, and ensure that your patient health information is truly secure according to HIPAA security standards and best practices for data protection.
These providers help mitigate security risks and potential risks associated with processing electronic health records through AI. There are three main providers that health care organizations go with:
The key difference between using these providers versus going directly to OpenAI or Anthropic is that these services run on dedicated infrastructure with proper security policies. They will sign the BAA agreements (making you HIPAA compliant) and provide enterprise-grade security measures and compliance solutions you don't get going directly to the model providers.
What Models Are Available?
AWS Bedrock
AWS actually offers the most Claude models for healthcare applications processing electronic health records, including:
- Claude 4 Sonnet and Claude 4 Opus (the latest versions)
- Llama models from Meta
- Titan models from Amazon
- Plus a bunch of other models
Google Vertex AI
Vertex provides access to:
- Gemini Pro and Gemini Flash
- Some PaLM models
- Several other open-source models
Azure OpenAI
Azure focuses on OpenAI models with security standards for health care providers:
- GPT-4 and GPT-4 Turbo
- DALL-E for image generation
- Other OpenAI models configured for handling sensitive patient data
The Surprising Truth About Pricing
What might surprise you is that it's actually not much more expensive to use models on these HIPAA-compliant platforms for processing electronic health records. In fact, sometimes for certain models, it's actually a little bit cheaper.
For the majority of models, the pricing is basically identical to going to the providers directly:
- Claude models on Bedrock: Pricing is basically the same as going directly to Anthropic
- OpenAI models in Azure: Cost is basically identical to OpenAI direct
- Vertex AI: Identical for most models, some surprisingly cheaper, others more expensive (we can't explain this price discrepancy, but it's comparable overall)
Certain features are a little more expensive—for example, Azure adds a hosting cost for fine-tuned models—but on the whole, pricing is very similar for comprehensive compliance solutions that follow best practices for data security.
Implementation: Easier Than Expected
The implementation process for HIPAA-compliant AI and data protection is actually very similar to how you'd implement if you went directly to the providers. In our experience, there really wasn't much difference between implementing Bedrock versus implementing Claude through Anthropic directly for processing patient health information and electronic health records.
Depending on your setup, you may need to perform a few additional steps for authentication. This is because this approach provides the technical safeguards and security policies required by HIPAA compliance and is more secure.
The Approval Process
While implementation was straightforward, getting access to the models was a little more challenging compared to signing up with Anthropic or OpenAI directly. All providers require healthcare organizations to submit an application and get approved for handling electronic protected health information, but from our experience (as of June 2025), approval was quick and easy:
- Bedrock: The review process took literally a few seconds
- Vertex AI: Same quick approval, though we've heard it can take 1-2 business days depending on your account standing with Google
- Azure OpenAI: Took about a day for us, but we've heard it could take up to a week depending on various factors
These providers conduct their own risk analysis to ensure health care providers meet security requirements before granting access to sensitive patient data processing capabilities.
Minor Cons to Consider
There are really only two small downsides compared to if you were to go directly:
1. Model Availability Delays
When you go directly to model providers like OpenAI and Anthropic, models are available the day they're announced. With these HIPAA-compliant cloud providers for processing electronic health records, there's a bit of delay:
- Bedrock: Fastest to get new models (less than a week for latest Claude models)
- Vertex AI: Slower (1-4 weeks on average for new models)
- Azure OpenAI: Slowest (2-8 weeks for latest models to appear)
If having the absolute latest models immediately for your applications is critical, this is something to be aware of.
2. Slightly More Approval Friction
There's a bit more friction than going directly to providers, but honestly, it's minimal compared to the data protection benefits you gain for securing patient health information and avoiding potential risks associated with disclosure of PHI and security risks.
Mix and Match for Best Results
The good news is you can mix and match providers for different compliance solutions. You're not locked into one.
In our transcription app case study for handling patient health information in electronic form:
- We use Claude models for transcription cleaning (it's just the best at cleaning transcriptions while maintaining HIPAA privacy and data protection)
- We use Vertex Gemini models for chat-based editing (very quick, fast, and cheap for processing sensitive patient data)
We're using both providers pretty equally and have had absolutely no problems maintaining compliance with HIPAA regulations and security policies. You can actually use all three if you want for different aspects of your healthcare application.
Beyond Just Model Access
Aside from providing these models, all three providers offer tons of additional features for health care organizations processing electronic health records. One cool thing you can do is host your own local models—if you have a custom model for processing patient health information, you can load it into AWS Bedrock, for example, and use it just like the other models while maintaining HIPAA compliance and following best practices for data security.
The Bottom Line
Achieving HIPAA compliance with top-tier AI models for handling electronic protected health information and patient health information is really simple and way more affordable than most people think. In our experience, we didn't find much difference between implementing with these providers compared to going direct. You really don't lose much by doing this, and you gain comprehensive compliance solutions, data protection, and HIPAA compliance required by the Department of Health and Human Services.
If you're building health care applications or any business that handles sensitive patient data, electronic health records, and individually identifiable health information, don't let HIPAA compliance stop you from using the best AI models available. These cloud providers make it easier than ever to stay compliant with HIPAA privacy and security rules while still leveraging cutting-edge AI capabilities.
Frequently Asked Questions About HIPAA-Compliant AI
Q: Can I use ChatGPT or Claude for healthcare applications?
A: Not directly. Standard APIs from OpenAI and Anthropic don't provide Business Associate Agreements (BAAs) required for HIPAA compliance. However, you can access these same models through HIPAA-compliant cloud providers like AWS Bedrock, Google Vertex AI, or Azure OpenAI.
Q: How much does HIPAA-compliant AI cost compared to regular AI APIs?
A: Pricing is surprisingly similar—often identical to going direct to providers. Some models on certain platforms are even cheaper. The main cost difference comes from additional enterprise features, but basic model usage costs are comparable.
Q: What's a Business Associate Agreement (BAA) and why do I need one?
A: A BAA is a legal contract required by HIPAA when working with third-party vendors who handle protected health information. It ensures the vendor follows HIPAA security standards and won't use your patient data for training their models.
Q: How long does it take to get approved for HIPAA-compliant AI services?
A: Approval times vary by provider: AWS Bedrock typically approves in seconds, Google Vertex AI takes 1-2 business days, and Azure OpenAI can take up to a week. All require an application process but approval is generally straightforward for legitimate healthcare organizations.
Q: Can I mix different HIPAA-compliant AI providers?
A: Yes! Many healthcare organizations use multiple providers for different use cases. For example, you might use Claude models on Bedrock for transcription and Gemini models on Vertex AI for chat features.
Q: What happens if there's a data breach with HIPAA-compliant AI?
A: HIPAA-compliant providers implement breach notification procedures and security measures required by the HIPAA Breach Notification Rule and HIPAA Security Rule. They handle incident response according to Department of Health and Human Services guidelines, but you should still have your own breach response plan and compliance program to address potential risks.
Q: Are there any limitations with HIPAA-compliant AI models?
A: The main limitation is that new models take longer to become available (1-8 weeks depending on the provider) compared to direct access. Otherwise, functionality is essentially identical to non-compliant versions for processing electronic health records and patient health information.
Q: Do I need technical safeguards beyond using a HIPAA-compliant AI provider?
A: Yes, using a HIPAA-compliant AI provider is just one part of your overall compliance program. You still need to implement proper access controls, encryption, audit logs, security policies, and other technical and physical safeguards required by HIPAA regulations. Best practices for data security include regular risk analysis and comprehensive data protection measures for sensitive patient data.
Ready to Implement HIPAA-Compliant AI for Your Health Care Organization?
Don't let compliance concerns hold back your health care innovation. Our team at Aloa has successfully implemented HIPAA-compliant AI solutions for medical groups, health care providers, insurance companies, and health tech companies across the United States. We specialize in comprehensive compliance solutions that address security risks and follow best practices for data protection.
Get expert help building your HIPAA-compliant AI application →
Need More Resources?
Looking for additional guidance on AI implementation for health care? Check out our comprehensive AI resources including case studies, implementation guides, compliance frameworks, and best practices for processing electronic health records while maintaining data security.
