Development of a Multitenant Data Platform for Rare Disease Research and Clinical Data Integration

The client faces operational complexities in aggregating and managing observational patient data from multiple sources, including EHR systems and legacy interfaces. They require a unified, scalable platform to support multi-stakeholder workflows, improve data quality, and enable efficient research collaboration across regions and data sources.

A large healthcare organization or research institution aiming to enhance its clinical data management, integrate diverse patient data sources, and facilitate collaborative research on rare diseases.

• Design and implement a cloud-based, multitenant platform supporting data collection workflows for clinicians, patients, and researchers.
• Build a secure integration pipeline capable of connecting with diverse healthcare data sources via standards like FHIR, HL7, and proprietary interfaces.
• Develop data transformation and mapping tools to convert heterogeneous data into a standardized observational data model (e.g., OMOP/CDM).
• Support complex observational data modeling for specific diseases and workflows to facilitate advanced research.
• Implement clinical content management and collaborative tools for researchers and clinicians to enhance data sharing and analysis.
• Ensure system scalability, automation, and high data quality to support ongoing research needs and future expansion.

• Multitenant architecture supporting diverse research projects and regional data segregation
• Interactive data collection interfaces tailored for clinicians, patients, and researchers
• Integration pipelines utilizing standards like FHIR, HL7, CCDA, and proprietary formats with intelligent patient matching
• Automated data profiling, validation, and quality assurance modules
• Transformation and mapping tools converting external data to a common data model (e.g., OMOP/CDM) with extension capabilities
• Secure, role-based access controls ensuring data privacy and compliance
• Clinical content management infrastructure supporting collaborative research workflows
• Persistent storage solutions using cloud-native databases with support for JSON/shadow storage for UI components

• Electronic Health Record (EHR) systems via FHIR, HL7, or proprietary interfaces
• Legacy data sources requiring data profiling and transformation
• Intelligent patient matching algorithms powered by open-source machine learning software
• Data standards validators and healthcare data validation tools

• System scalability to support increasing data volume and user base across multiple regions
• High availability with minimal downtime
• Data security and compliance with healthcare regulations (e.g., HIPAA, GDPR)
• Robust data quality and validation processes
• Performance targets ensuring real-time or near-real-time data processing and retrieval
• Automated workflows for data ingestion and validation

The implementation of this platform aims to significantly improve data integration efficiency, enhance data quality, and streamline research workflows. It is expected to facilitate collaborative research on rare diseases, support evidence generation from diverse data sources, and accelerate scientific discovery. The scalable architecture and advanced data management capabilities will enable ongoing growth and adaptation, positioning the organization as a leader in clinical research infrastructure.