Skip to Main Content


Clinical Research Informatics

Clinical Research Informatics

Clinical Research Informatics utilizes the accumulating "big data" derived from clinical and translational activities to advance health quality, healthcare delivery, and performance. The clinical data warehouses (EPIC, VA Corporate Data Warehouse) are critical components.

The data warehouse at YNHHS combines data feeds from several clinical, research, administrative, and finanacial sources and provides access to currently unexplored information. Access to this knowledge helps to assure that care delivery is value-driven.

  • Database design and implementation: Requirements gathering will define breadth and depth of information that will be stored. A star schema data architecture with central fact tables accompanied by dimensional tables will provide for the most efficient querying and analysis.
  • Data acquisition will require extraction from production transaction systems, filtering of inaccurate and unnecessary data, transformation to standard codes, and pre-calculation of frequently required information (e.g., total days spent, patient ages, total costs, etc).
  • Data access control and query management with appropriate governance, security, and user-interface services will require particular attention.
    • Vetting and prioritizing report requests will require ongoing attention to policies and procedures.
    • Protected health identifiers can be stored in a separate secure database with restricted access while a de-identified version of the warehouse can be made available for research and quality improvement.
    • A simple front-end will facilitate use (e.g., Query-By-Example).
  • Knowledge discovery in the warehouse will apply data mining tools to identify patterns and relationships in large volumes of data.
  • Datamarts and registries will be created to define subpopulations of the warehouse. For example those patients with a particular disease or with repeat admission or adverse outcome can be selected to populate a persistent dataset. These subpopulations will become the targets for safety and quality improvement activities as well as research.
  • Assessing health and behavioral outcomes in these subpopulations will lead to a better understanding, policies, and programs to reduce health inequities and disparities that affect population subgroups due to social determinants, such as race, socio-economic status, gender, and geography.
  • Neuroinformatics: The National Human Brain Project (HBP) and the National Neuroscience Information Framework (NIF).
  • Phenotyping activities such as with Million Veteran Program.
  • A multitude of clinical informatics research activities may be undertaken making use of advanced EHR technologies. Depending on the interests of informatics faculty hired, this research might focus on such topics as natural language processing, personal health records, privacy and security, human-computer interaction studies, mobile (wireless) health, telemedicine, and qualitative (sociotechnical) studies of EHR use, among others.