Planning Data Access and AI for Clinical Research

The question

A national health system and its academic research partner wanted to use modern AI within a tightly controlled data environment. The work had to connect research questions with access agreements, security requirements, and an implementation plan that internal teams could support.

Architecture and research planning

We developed cloud architecture and research-workflow plans around the client’s consumer-project pattern, including BigQuery and Vertex AI options, authorized views, and policy-tag masking. The access design used governed views in place of separate copies of source clinical data.

The proposed workflow combined extraction from unstructured notes with reproducible cohort and outcome analysis. Internal evaluation use cases included reanalysis of published studies and sepsis and payer-mortality questions. The delivery sequence called for internal validation before extending access to the academic partner.

Program economics and governance

The scoped cloud-consumption estimate was approximately $1,000 to $2,000 per month, with tiered assumptions for greater use. It was a planning estimate, not a fixed operating-cost ceiling.

A combined infrastructure and research scope brought together access requirements, operating responsibilities, evaluation steps, and governance language. The broader collaboration extended into a third year of research funding; that program context is separate from the delivery status of the proposed AI environment.

What the client gained

An implementation plan connecting infrastructure, research, and governance, plus a cost model for budgeting. Related advisory work explored how analysts accustomed to SAS could use AI to read, review, and guide analytical code. The architecture and validation plans supported the next stage of the research program without treating proposed workflows as completed deployments.