Clinical Research Informatics Postdoctoral (CRISP) Fellowship

The Clinical Research Informatics Postdoctoral (CRISP) Fellowship provides 1 or 2 years of tailored training for clinician investigators who seek to improve healthcare through the science of clinical research informatics. It is funded by a training grant (TL1-TR001871) from the National Center for Advancing Translational Science (NCATS), in conjunction with the UCSF Clinical and Translational Science Institute (CTSI) and the Department of Epidemiology and Biostatistics.

CRISP fellows obtain advanced didactic training in the methods of clinical research informatics and participate in regular work-in-progress and career development sessions. Fellows receive a stipend commensurate with their PGY/postdoctoral fellow status plus tuition assistance for didactic training. Applicant departments are responsible for covering approximately 25% of the total fellowship costs (see "Frequently asked questions" at the bottom of this page). 

What is clinical research informatics?

Clinical Research Informatics, one of the five informatics specialties defined by the American Medical Informatics Society, involves using data science methods, analytics, and clinical practice observation to generate and disseminate knowledge related to health and healthcare delivery.

Careers in clinical research informatics may involve:

  • Diagram, venn diagramDescription automatically generatedSelection, implementation, development, and maintenance of a technology ecosystem to support clinical research activities and regulatory needs
  • Optimization of electronic health record (EHR) systems and data to support research administration, participant recruitment and consenting, data capture, intervention implementation, and other activities related to clinical research execution
  • Management and workflow of EHR data repositories, registries, marts, and warehouses, along with simplifying the process of leveraging these standardized repositories via reporting and analytics
  • Leveraging EHR data for population health analytics and scientific inquiry
  • Use of implementation science methods and translation of research into evidence-based practice