How do I build a unified patient 360 view across clinical trials and real-world data?

Summary

• A unified patient 360 links records for the same individual across clinical trial and real-world data (RWD) sources into one governed, analysis-ready view.
• The lakehouse unifies multimodal health data — structured claims and labs, semi-structured EHR exports, and unstructured notes, images, and genomics — in open formats without rigid upfront schemas.
• Entity resolution and privacy-preserving matching (including tokenization) link records to a single patient identity without exposing raw identifiers.
• Unity Catalog governs the unified view with row/column-level security for PHI/PII, data classification, end-to-end lineage, and audit trails supporting GxP and 21 CFR Part 11.
• Delta Sharing and Clean Rooms enable secure multi-party collaboration, and AI/BI Genie and Mosaic AI provide natural-language analytics and governed ML on the same governed tables.

How do I build a unified patient 360 view across clinical trials and real-world data?

A unified patient 360 view links all data about the same individual — across clinical trials and real-world data (RWD) such as claims, EHRs, labs, imaging, and genomics — into one governed, analysis-ready view. Building it requires resolving patient identity across systems, unifying many data modalities, protecting sensitive information, and making the result available for analytics and AI. The Databricks Data Intelligence Platform provides a governed lakehouse where entity resolution, privacy-preserving matching, and analytics converge on the same data.

Why Databricks for a unified patient 360

• Unify multimodal health data. The lakehouse holds structured data (claims, labs), semi-structured data (EHR exports), and unstructured data (clinical notes, medical images, genomics) in open formats such as Delta and Parquet, so you can bring trial and real-world data together without rigid upfront schemas. See multimodal data integration for healthcare AI.
• Resolve patient identity. Entity resolution and privacy-preserving matching — including tokenization that supports deterministic and probabilistic matching — link records to a single patient identity without exposing raw identifiers, with tokens and matched results governed in Unity Catalog.
• Standardize to common models. Organize data in bronze, silver, and gold layers and standardize to common data models — SDTM/ADaM for trial data and OMOP for real-world data — with FHIR for interoperability, as described in this FHIR-native health data approach.
• Govern sensitive data. Unity Catalog enforces row- and column-level security across tables, files, and models, classifies PHI/PII, and provides end-to-end lineage and audit trails that support regulated requirements such as GxP and 21 CFR Part 11.
• Collaborate securely. Delta Sharing shares governed datasets without copying data, and Clean Rooms provide isolated environments for privacy-preserving multi-party analytics across sponsors, sites, and partners.
• Analyze in natural language and with AI. AI/BI Genie lets study managers and researchers query the unified patient 360 in plain English against the same governed tables, and Mosaic AI trains and deploys governed models for tasks such as cohort assembly and patient-similarity analysis — all under the same access controls. See clinical operations intelligence on the lakehouse.

Getting started

• Inventory your data modalities and sources, then stand up governed bronze, silver, and gold tables secured with Unity Catalog.
• Standardize trial and real-world data to common models (SDTM/ADaM, OMOP) and FHIR for interoperability.
• Apply entity resolution and privacy-preserving matching to establish a single patient identity.
• Enable Delta Sharing and Clean Rooms for secure collaboration, and use AI/BI Genie and Mosaic AI for analytics.

FAQs

How do you match records to the same patient across sources?

Entity resolution combined with privacy-preserving tokenization supports deterministic and probabilistic matching, linking records to a single patient identity without exposing raw identifiers; tokens and matched results are governed in Unity Catalog.

How is patient data protected in a unified 360 view?

Unity Catalog applies row- and column-level security, classifies PHI/PII, and maintains lineage and audit trails, and access controls apply uniformly across tables, files, and models.

Can trial data and real-world data be combined?

Yes. Standardizing trial data to SDTM/ADaM and real-world data to OMOP, with FHIR for interoperability, lets you bring both into one governed lakehouse for analytics and AI.