STATUS: Active Project
In order to achieve a sustainable data network infrastructure, promote interoperability, and foster the creation of a Learning Health System (LHS), there is a need to map and transform data across various Common Data Models (CDMs) and leverage open-source standards. By mapping various CDM data elements and leveraging existing PCORTF investments, it is feasible to reuse the data, methods, and other resources from each network thereby providing PCOR researchers with access to larger and more diverse types of observational data.
PROJECT PURPOSE & GOALS
This is a collaborative project between the Food and Drug Administration (FDA), National Cancer Institute (NCI), National Institutes of Health/National Center for Advancing Translational Sciences (NIH/NCATS), Office of the National Coordinator for Health (ONC), and the National Library of Medicine (NLM).
The goal of this project is to build data infrastructure for conducting patient-centered outcomes research (PCOR) using observational data derived from the delivery of health care in routine clinical settings. The sources of these data may include, but are not limited to insurance billing claims, electronic health records (EHRs), and patient registries. The Common Data Model (CDM) organizes data into a standard structure, which may differ across networks. This project intends to harmonize several existing CDMs in order to support research and analyses across multiple data networks. The aim is to advance the utility of data and its interoperability across networks to facilitate PCOR. The enhanced data infrastructure created through this project will have the capacity to support evidence generation on patient-centered outcomes that can inform regulatory and clinical decision making within federal programs.
The project objectives are to:
Develop common data architecture as the intermediary between four CDMs within four networks i.e., Sentinel, PCORNET, i2b2 and OHDSI.
Develop a flexible data model that can be used to create outbound data in multiple formats for multiple purposes.
Test the common data architecture by using it to study factors associated with the safety and effectiveness of newly approved oncology drugs that boost patients’ immune response to cancer. These drugs, known broadly as immune checkpoint inhibitors, are gaining approvals in a number of different indications, but it is unclear what the safety of these drugs may be in routine clinical care and how effectiveness may vary in different patient subpopulations, in combination with other effective agents for comorbid, such as those which treat autoimmune disorders. In this 2-year project, the team will focus on three agents in the programmed cell death protein 1 (PD1)/ programmed death-ligand 1 (PDL1) class of oncology drugs with a focus on patients who have both cancer and an autoimmune condition. In order to validate this specific use case, the statistical tests and methods in the Sentinel and OHDSI libraries will be applied to the mapped CDMs.
Establish methods and develop processes, policies, and governance for ongoing curation, maintenance, and sustainability of the common data architecture, building upon existing resources, standards, and tools. Example of existing resources include but are not limited to the Data Access Framework (DAF) developed by ONC to interface to various Common Data Models (CDMs) and the NIH Common Data Element Repository to register the harmonized, standardized data elements within each CDM.