Patient Genomics Data Review for Precision Oncology
Turning siloed clinical and genomic records into a governed, self-serve analytics layer — combining an AI/BI dashboard with a conversational Genie Space for clinical researchers.
A precision oncology research team held rich but siloed patient-level data — demographics, diagnoses, exposure history, and raw gene expression profiles — scattered across source systems with no governed, queryable single layer. Clinicians and researchers needed self-serve access to this data without compromising HIPAA-sensitive patient records or waiting on engineering for every new question.
Solution
The multi-domain clinical and genomic dataset was ingested via Lakeflow Connect into a governed Lakehouse, with Unity Catalog applied for column- and row-level access control over sensitive patient attributes. On top of this governed layer, two outputs were delivered:
An AI/BI Dashboard with four analytical views: a Patient Population Overview with live demographic filtering; a Patient Diagnoses Overview surfacing 13.2K diagnoses broken down by race, age, and gender with a searchable record-level table; an Exposures Profile covering 2,919 recorded smoking and alcohol-use events by gender; and a Gene Expression Profile using UMAP embeddings to visualize expression clustering across 14 tissue and organ types.
A Genie Space layered over the same governed dataset, letting non-technical clinical staff ask natural-language questions directly — without writing SQL or waiting on a data team.
Figure 1: The Databricks Genie Space provides a conversational text-to-SQL interface for clinical researchers.
Results
By integrating Lakeflow Connect and Unity Catalog, the team successfully centralized and secured clinical records, exposure events, and genomics data. The resulting analytics suite delivered direct, actionable value to both clinical and operational teams:
The population and diagnoses dashboards allowed clinical researchers to profile patient groups and track diagnosis distributions with zero manual data collection overhead.
The gene expression visualizer enabled researchers to explore clustering across thousands of patient-level genomic records in seconds.
The Genie Space empowered non-technical staff to query data directly using natural language, decreasing the engineering team's reporting request volume.
Result
Clinicians and research partners now access a fully governed, HIPAA-aligned genomics analytics layer with self-serve query capabilities, reducing the time-to-insight for precision oncology protocols from weeks to seconds.
Why It Matters
"Governing patient data is not just about security — it's about enabling discovery. By coupling Unity Catalog governance with conversational analytics, we've given researchers safe, direct access to the clinical insights they need to accelerate precision medicine."
