Multi-Institutional Data Sharing

The INSIGHT Clinical Research Network, which is funded by the Patient Centered Outcomes Research Institute (PCORI), integrates EHR data from six health systems in NYC to support the goal of “creating an accessible, sustainable, scalable clinical research network to facilitate regional and national people-centered research.” 

Using data from the INSIGHT network, you can track patients across NYC-area health care providers, addressing the data quality issues posed by health care fragmentation to provide a holistic portrait of utilization and outcomes. The INSIGHT network has supported, to date, over 44 projects, including several projects initiated at Weill Cornell Medicine.

The National COVID Cohort Collaborative (N3C) is an NIH-led open science community that aims to acquire, harmonize, and make available patient-level COVID-19 data from participating sites to enable innovative machine learning and statistical analysis. Using data from N3C, WCM researchers can collaborate with researchers from other institutions to analyze retrospective data, conduct characterization studies, build predictive models, and carry out population level estimation. Access to the data is provisioned by the N3C team and is provided through a secure web enclave.

Visit the N3C website for more information. 

The All of Us Researcher Workbench is a cloud-based platform where registered users can access Registered and Controlled Tier data. The workbench offers powerful tools to support data analysis and collaboration. The national All of Us Research program provides $300 in initial credits for each registered Workbench user which can be used for data access and computational resources. The platform also offers a range of resources, including training materials, documentation, and a support network to help users maximize the potential of the data.

To learn more, visit the All of Us Researcher Workbench.

The NCATS Accrual to Clinical Trials (ACT) Network is an NIH-led effort to allow investigators to query EHR data from nearly all CTSA hubs nationwide to determine counts of eligible patients for clinical trials. 

Visit the CTSC website for more information. 

Researching COVID to Enhance Recovery (RECOVER) is a national initiative funded by the NIH to improve our prediction, detection, treatment, and prevention of the long-term health effects of COVID-19, often referred to as the post-acute sequelae of COVID-19 (PASC).

The RECOVER initiative pools real world data (RWD) from participating PCORnet sites and utilizes advanced machine learning and artificial intelligence methods to better understand the long-term health effects of COVID-19 after the acute infection period. This is achieved through the development of condition specific computable phenotypes which will have the capacity to detect patients that are potentially affected by PASC and potentially identify patients that are susceptible to PASC. This project also focuses on health services research (HSR) which investigates other confounding factors that can play a role in clinical outcomes and treatment such as racial/ethnic disparities, environmental factors, and analyzing how different variants of COVID-19 have affected individuals. This projects overarching goals are described below: 

• What are the clinical spectrum of and biology underlying recovery from acute SARS-CoV-2 infection over time?  
• For those patients who do not fully recover, what is the incidence/prevalence, natural history, clinical spectrum, and underlying biology of this condition? Are there distinct phenotypes of patients who have prolonged symptoms or other sequalae? 
• Does SARS-CoV-2 infection initiate or promote the pathogenesis of conditions or findings that evolve over time to cause organ dysfunction or increase the risk of developing other disorders? 

For more information on this initiative please visit recovercovid.org 

TriNetX is a global research data network with data from thousands of health care providers in 24 countries across the globe for hundreds of millions of patients. Using TriNetX, clinical trial sponsors can query de-identified electronic health record data from participating sites based on structured inclusion and exclusion criteria to determine whether or not each site has patients who may be eligible for a trial. 

Using TriNetX, users at individual sites can also query data from their own site using the same interface. To learn more about TriNetX, email arch-support@med.cornell.edu or visit the link on the sidebar.

To request an account and access de-identified patient information, complete the Cohort Discovery Request form.