TitanML is now Doubleword
Doubleword logo black
Product
Resources
Resource CenterAI Dictionary
Docs
Pricing
Book a demo
Book a demo
Resources
/
Blog
/
4 best practices when deploying Generative AI in HIPAA compliant environments
January 9, 2024

4 best practices when deploying Generative AI in HIPAA compliant environments

Meryem Arik
Share:
https://doubleword.ai/resources/4-best-practices-when-deploying-generative-ai-in-hipaa-compliant-environments
Copied
To Webinar
•

Navigating the compliance landscape

In the rapidly evolving field of healthcare technology, deploying Generative AI within the rigid framework of HIPAA compliance presents a unique set of challenges. As healthcare institutions look to harness the benefits of AI, it's crucial to adhere to best practices that ensure both innovation and compliance go hand in hand. In this blog, we will look at 4 best practices you can adopt when deploying Generative AI in HIPAA compliant environments.

1. Enrich your AI model: the role of domain-specific data

One effective strategy is the use of Retrieval Augmented Generation (RAG). By enriching base large language models with domain-specific, proprietary information, RAG significantly enhances the accuracy and relevance of AI outputs. This approach not only reduces the likelihood of generating inaccurate information, or 'hallucinations', but also improves audibility – a key concern in healthcare applications.

Retrieval-Augmented Generation (RAG): From Theory to LangChain  Implementation | by Leonie Monigatti | Nov, 2023 | Towards Data Science
RAG (Retrieval Augmented Generation) - Leonie Monigatti

2. Scrub out personal identifying information (PII) / protected health information (PHI)

The cornerstone of HIPAA compliance lies in the stringent handling of PII and PHI. It is imperative that healthcare organizations rigorously scrub out all traces of PII and PHI from their datasets before employing them in training or operating their Generative AI models. This step is vital in mitigating risks of data breaches and ensuring compliance with data protection laws.

3. Expert human oversight: a non-negotiable requirement

Even though Generative AI models are incredibly impressive, they are by no means foolproof. In domains where the outcome is as important as healthcare, it is important that the outputs are trusted. This is why we always recommend that Generative AI applications are only advisory, and expert humans-in-the-loop are responsible for the final decision making and caregiving.

4. Secure deployment: deploying open-source models in your environment

As highlighted by the limitations of platforms like ChatGPT in meeting HIPAA standards, the safest route for deploying Generative AI is within a secure, controlled environment. Open-source models, tailored to specific institutional needs, offer a viable solution. They enable organizations to maintain control over their AI tools while ensuring compliance and data security.

In this context, tools like the Titan Takeoff Inference Server are proving invaluable. They simplify the deployment of open-source Generative AI models, making the process more accessible, especially for institutions with limited access to GPU resources. This server not only facilitates ease of deployment but also aligns with the stringent requirements of HIPAA, ensuring that healthcare providers can confidently leverage the power of AI without compromising on compliance.

As the healthcare sector continues to navigate the complexities of integrating advanced technologies like Generative AI, the emphasis must always be on striking a balance between innovation and compliance. By adhering to these best practices, healthcare institutions can not only leverage the transformative potential of AI but do so in a manner that upholds the highest standards of patient privacy and data security. The road ahead is one of cautious advancement, where compliance with regulations like HIPAA is not just a legal obligation but a moral imperative in the pursuit of better healthcare outcomes.

Reach out to hello@titanml.co if you would like to learn more and find out if the Titan Takeoff Inference Server is right for your Generative AI application.

Footnotes

Table of contents:

Heading 2
Heading 3
Heading 4
Heading 5
Heading 6
Learn more about self-hosted AI Inference
Subscribe to our newsletter
Thanks you for subscription!
Oops! Something went wrong while submitting the form.

Want to learn more?

We work with enterprises at every stage of their self-hosting journey - whether you're deploying your first model in an on-prem environment or scaling dozens of fine-tuned, domain-specific models across a hybrid, multi-cloud setup. Doubleword is here to help you do it faster, easier, and with confidence.

Book a demo
Doubleword logo white
Sitemap
HomePricingDocsResourcesBook a demo
Contact
hello@doubleword.ai
Adress
Farringdon, London
JOIN THE COMMUNITY
Subscribe to our newsletter
Thanks you for subscription!
Oops! Something went wrong while submitting the form.
©2025 Doubleword. All rights reserved.
designed by
celerart
Privacy Policy
We use cookies to ensure you get the best experience on our website.
Accept
Deny