UMB ICTR

UMMS and the UMB have jointly founded a new Clinical Research Data Sub-core to centralize and streamline the provision of clinical and translational data.

Included in this partnership is the launching of TriNetX which allows researchers to freely pre-review aggregate EPIC data using a query tool to define a cohort prior to initiating the effort of IRB submission/approval process and requesting data. Go here for more information about TriNetX https://www.umaryland.edu/ictr/investigator-resources/ictr-informatics-core-services/trinetx/ 

Please note, the IRB-approved protocol and letter of determination must be provided prior to requesting a data pull. 

In addition, please complete this form and upload in the Informatics section of the ICTR Request application EPIC Data Request_Additional Elements

• Access to Medicare and IQVIA data.  Researchers interested in Medicare and IQVIA data may be eligible for ICTR Voucher Support to help defray the cost (see ... below).  For full description, see PHSR Community Database Description of Databases and PHSR Community Database Fees. 
  • Medicare Chronic Conditions Warehouse (CCW) 5% Sample
  • Medicare Current Beneficiary Survey (MCBS)
  • IQVIA (IMS Health Pharmetrics Plus) 10% Sample
• Assistance with securing Memorandums of Understanding or Data Use Agreements.

• Patient-centered PHR/patient portal implementation
• Web-based research and usability testing
• Hospital information systems evaluation and implementation

• Use of digital technology in support of health, such as mobile apps
• Use of eHealth/mHealth/patient portal interventions to promote and manage the health of adults age 50 and older

• The REDCap team can help build a database for your project in .  Please be prepared to provide your current, IRB-approved research surveys, data entry /case report forms, and your project's data collection schedule.    Data collected are stored and maintained locally on campus in a firewalled server managed by the UMB. REDCap provides automated export procedures for seamless data downloads to Excel, PDF, and common statistical packages (SPSS, SAS, Stata, R) as well as a built-in project calendar, a scheduling module, ad hoc reporting tools, and advanced features, such as auto-validation, branching logic, file uploading, calculated fields, and survey stop actions.  REDCap can be set up for multi-center studies as well. 

More information about REDCap can be found here Data Management here REDCap.

The University of Maryland Baltimore County (UMBC) provides Artificial Intelligence, Machine Learning, & Cybersecurity Services to the UMB ICTR Informatics Core.  The new UMBC services will be an exciting addition for those researchers exploring the use of machine learning (ML) and Artificial Intelligence (AI) to identify patterns in data and make decisions with minimal human intervention.   For researchers interested in developing apps and devices that may improve delivery and exchange of health information, UMBC's Cybersecurity expertise will help the researcher protect those devices, systems, and data from attack.  See below for a list of UMBC Core services available to UMB researchers.

UMBC - ICTR Core Capabilities

• Securing medical devices
• Securing smart systems, e.g., smart surgery rooms
• Protecting learned/predictive models from attacks
• Deep learning and artificial neural networks
• Natural language processing
• Graph analytics
• Time series analytics
• Data visualization augmented reality and virtual reality

UMBC - ICTR Core Example of Services:

• Consult to uncover possible cybersecurity risks associated with devices and/or systems.
• Consult on ways to protect devices and/or systems from attackers, either at design time or after deployment.
• Consult on ways to apply artificial intelligence and machine learning to solve specific problems given existing data, including processing pipelines, specific algorithms, and evaluation methodology
• Advise on what additional data could be collected or obtained to potentially improve the utility of AI/ML for specific use cases.
• Construct simple proof-of-concept AI/ML systems to understand what level of performance might be achieved with more time, data, or resources
• Advise on the best visual representations to explore complex datasets and to communicate results to others.