Disease Registries Development with OMOP CDM Repository
About the Client
Our client, an innovative company that has developed a platform for rare disease registries, is building registries and supporting related research in multiple countries. Each registry aggregates data from multiple sources including EHR and other systems.
The internal operational patient data representation is based on the OHDSI OMOP/CDM open-source data model. The company has also been developing an extensive technical and content management infrastructure as well as the necessary metadata model and CDM extension in support of its business objectives.
Our client has several distributed teams operating on three continents collaboratively working with each other and the clients.
Engagement Scope and Details
Our client engaged Clinovera to capitalize on our extensive experience in Healthcare and Life Sciences, as well as for our experience with OMOP (Observational Medical Outcomes Partnership) CDM expertise.
We brought in a multi-disciplinary team to work closely with client leadership and management using agile processes.
As part of this engagement, we worked with a wide range of data-focused operational and infrastructure tasks, including:
- Rapidly evolving a multi-tenant cloud-based interactive platform for data collection that supports workflows and logic for clinicians, patients, and researchers;
- Building an integration pipeline with Healthcare institutions that utilizes intelligent patient matching, data profiling, and data quality functions;
- Acquisition, transformation, and mapping of patient data from different EHR systems and legacy data sources to OMOP CDM;
- Data modeling for numerous disease-specific observational entities of high complexity in support of specific diseases and workflows;
- Developing clinical content management tools and infrastructure for the collaboration of researchers and clinicians.
Technical Implementation Highlights
- Cloud-based multi-tenant solution hosted in Microsoft Azure;
- Technology stack: Single page architecture, Angular JS, Java Bootstrap, microservices, Microsoft SQL Server Database, Forgerock;
- Integration pipeline is based on Intersystems HealthConnect infrastructure;
- Intelligent patient matching using open-source machine learning software and algorithms;
- Persistent storage in OMOP CDM and extensions; shadow storage for UI is in JSON;
- Data conversion from external sources in CCDA, FHIR, HL7, proprietary interfaces and data formats to OMOP CDM and JSON.
About OHDSI OMOP CDM
OMOP is an open-source Common Data Model (CDM), a standard for representation of observational data used for conducting Real World Evidence (RWE) research. It is supported and maintained by the OHDSI international community.
OMOP CDM is highly extensible and can accommodate extensions and custom entities outside of the standard model. Traditionally, OMOP CDM is not used as an operational data store, but with the right extensions and indexing we have been able to adopt this CDM to the needs of the customer.
About the Client Our client, an innovative company that has developed a platform for rare disease registries, is building registries and supporting related research in multiple countries. Each registry aggregates data from multiple sources including EHR and other systems.
The internal operational patient data representation is based on the OHDSI OMOP/CDM open-source data model. The company has also been developing an extensive technical and content management infrastructure as well as the necessary metadata model and CDM extension in support of its business objectives.
Our client has several distributed teams operating on three continents collaboratively working with each other and the clients.
Engagement Scope and Details
Our client engaged Clinovera to capitalize on our extensive experience in Healthcare and Life Sciences, as well as for our experience with OMOP (Observational Medical Outcomes Partnership) CDM expertise.
We brought in a multi-disciplinary team to work closely with client leadership and management using agile processes.
As part of this engagement, we worked with a wide range of data-focused operational and infrastructure tasks, including:
Rapidly evolving a multi-tenant cloud-based interactive platform for data collection that supports workflows and logic for clinicians, patients, and researchers
Building an integration pipeline with Healthcare institutions that utilizes intelligent patient matching, data profiling, and data quality functions
Acquisition, transformation and mapping of patient data from different EHR systems and legacy data sources to OMOP CDM
Data modeling for numerous disease-specific observational entities of high complexity in support of specific diseases and workflows
Developing clinical content management tools and infrastructure for collaboration of researchers and clinicians.
Clinovera’s Expertise in OMOP/CDM
Clinovera has a team of analysts, medical informaticians and engineers with deep expertise in OMOP CDM who have been mapping and extracting observational data from a variety of data sources, modeling complex observational entities, and developing tools and applications around OMOP CDM for research and analytics.