Secure Healthcare AI Data Playbook
Healthcare data is scattered, inconsistent, and hard to police, making any AI initiative risky and unreliable. Our answer is a security-first architecture that begins with a single, canonical data model, adds automated quality checks and role-based access, and then layers in the right AI platform (frontier or on-prem) trained and continuously audited for each use case. The result: traceable data flows, airtight compliance, and AI that actually delivers clinical and operational value without exposing sensitive information. Here is the playbook we've used to help multiple clients solve this problem:
Create a common data model - This is the core to solving problems with AI and security. We need to create a single data model which work and access is being done from. This improves the audibility of work that is being done, makes it possible to understand who is accessing data plus makes it easier for work to happen.
Mapping exercise and connectors (EMR, claims, ADT feeds, etc) - The next major phase is mapping our data to the data model. The first data that we will bring is to support a single use case. We then load more data on a use case by use case basis.
Create data quality checks and alerts - Knowing when something has gone wrong is a major part of the solution. Mistakes get made when transferring data and errors happen. Being able to react to those quickly is critical for actually having useful secure data.
Create roles and access levels that are mapped to jobs to be done - The most important thing for security is access control and making sure people don’t have access they shouldn’t have. To do that we need to map roles to what tasks they have to complete which will inform what data they should have access to.
Decide on AI platforms - The biggest choice here is whether to use a frontier model with a signed BAA or to use an open source model that is running on premise. The frontier models offer better performance with higher costs and additional vendors. On the other hand there is lower query performance and more maintenance needs with an open source model but it allows for total control for an organization which can make it the right choice.
Training of LLM - LLM need additional training to reduce hallucination and mistakes and to make them more healthcare specific. This can be done with a combination of retrieval augmented generation (RAG) and fine tuning. Customizing a model for your organization will give you the best results and a meaningful competitive advantage over the long term.
Creation of auditing layer for LLM - We need to know how the LLM is being used and what queries are being run. This allows us to avoid accidental leaks of PII and identify misuse of the LLM. We create tools to allow for all queries to be audited and to detect requests to send PII and PHI to unauthorized tools.