What challenges did Servizo Galego de Saúde (SERGAS) face before AI implementation?

Clinical trial operations were fragmented across siloed systems. Patient recruitment bottlenecks and manual ethics/regulatory review delayed approvals and slowed evidence generation.

What was the impact of the AI solution?

Reduces patient recruitment timelines by weeks. Accelerates identification of eligible cohorts from weeks to minutes. Improves visibility for sponsors and CROs through dashboards. Reduces manual oversight for ethics committees.

Rank#1,404of 3,449

Source

Evidence: Low25/100

Servizo Galego de Saúde (SERGAS) accelerates clinical trial workflows using RES on AWS (Innovatrial)

Use case typeHealthcare workflow automationUpdated Jun 13, 2026

SERGAS, implemented by NTT DATA, uses Innovatrial built on Research and Engineering Studio (RES) on AWS to centralize structured and unstructured clinical and biomedical data for clinical trial operations. The platform uses Amazon S3, Amazon EC2, Amazon Managed Blockchain, AWS IAM, and Amazon QuickSight, plus customer machine learning models for NLP-driven cohort identification and workflow automation. Researchers can translate natural-language inclusion/exclusion criteria into searchable queries to identify candidate cohorts in minutes instead of weeks, while dashboards and alerts improve visibility and regulatory tracking.

Organization: Servizo Galego de Saúde (SERGAS)
Industry: Healthcare
Location: Spain
Published: March 2026

Reported outcomes

Strategic outcomes

Speed & agilityAccelerated cohort identification workflowsCustomer experience & trustImproved trial visibility and trackingRisk & complianceReduced manual ethics oversightSpeed & agilityShortened patient recruitment timelines

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Servizo Galego de Saúde (SERGAS)
Provider: AWS
Maturity: Unknown
Linked source: AWS Public Sector Blog

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 4

1Clinical trial operations
2Patient recruitment
3Workflow automation

Clinical trial operations were fragmented across siloed systems.
Patient recruitment bottlenecks and manual ethics/regulatory review delayed approvals and slowed evidence generation.

Build a trusted research environment on RES on AWS to unify data, secure access, and support interoperable clinical research workflows.
Use NLP-enabled customer ML models to turn natural-language eligibility criteria into cohort searches.
Provide real-time alerts, dashboards, and automated workflows for trial monitoring and milestones.

Technologies

Research and Engineering Studio (RES) on AWS Amazon S3 Amazon EC2 Amazon Managed Blockchain AWS IAM Amazon QuickSight

Reduces patient recruitment timelines by weeks.
Accelerates identification of eligible cohorts from weeks to minutes.
Improves visibility for sponsors and CROs through dashboards.
Reduces manual oversight for ethics committees.

Architecture

Innovatrial is built on Research and Engineering Studio (RES) on AWS. The architecture uses Amazon S3 as a centralized data lake, Amazon EC2 for compute, AWS IAM for role-based access, Amazon Managed Blockchain for immutability/auditability, and Amazon QuickSight for dashboards. The solution runs secure isolated research environments and uses customer ML models for NLP-driven cohort identification from natural-language inclusion/exclusion criteria.

Sources & evidence1

Evidence: Low25/100Evidence strength