Clinical Research Informatics at the University of Michigan

1. Clinical Research Informatics at the University of Michigan Daniel Clauw M.D. Professor of Medicine, Division of Rheumatology Assistant Dean for Clinical and Translational Research Director, Center for the Advancement of Clinical Research

2. Clinical Research Informatics • Current State of Clinical Research Informatics Effort in CACR • Engage Web Portal • BioDBx • Honest Broker

3. Bench BedsidePractice Translational Research • Unique window of opportunity to capitalize on: • our basic science strengths, • our health services strengths, and • co-ownership of health system, research enterprise and managed care organization

4. Translational Research • Can differentiate us (UMHS, University) from our competitors • Clinical (grow innovative clinical care programs around clinical and translational research strengths, document effectiveness of innovative treatment programs to third parties) • Research (Increased federal funding, industry partnerships, IP, philanthropic donations, prestige of health system) • Education (improved experiences of students at all levels, increased national reputation, increased “value” of UM to state government) • Represents an opportunity to truly link the three portions of the UMHS enterprise, and the University • All portions of the enterprise should co-invest in this effort

5. Major Investment in Informatics • This will determine success or failure of academic medical centers, re: the “haves” and “have nots” • The “haves” will be the first to develop linkages among activities, establish importance of interrelationships, think of the life-cycle of academic medical knowledge from bench to bedside to practice, and back • The research informatics of today are the clinical informatics of tomorrow • Metaphorically, goal is a Regional Translational Research Center, although the true benefits of creating these linkages so far exceed the value of the NIH grant that this should not be over-emphasized

6. Overall Bioinformatics Effort at Michigan Central Administration with (recruited) Faculty Lead CACR-led Clinical Research Informatics MCBI-led “Bioinformatics”

7. WEB PORTAL “Engage” • Bulletin Board - info • matching patients to ongoing studies • Consent to use clinical data for research • Registry of patients, subjects • Recruitment for specific studies • HONEST BROKER • Integrate clinical to basic • Protect PHI, ensure security • Output of data for statistical analyses • BioDBx • Capture of research data • Web-based, facilitated research • Convert clinical care encounters into • clinical research • Phenotype patients, using self-report • or structured encounters Bedside Bench E-Research system of unified peer review and regulatory approvals to ensure appropriate access to research participants • BIOINFORMATICS (MCBI) • Collect / analyze genomic, proteomic, • microarray, functional imaging data • Databases to house huge amounts of data E-Research system of unified peer review and regulatory approvals to ensure appropriate access to research participants Clinical data or sample warehouses

8. Clinical Research Informatics • Introductions • Current State of Clinical Research Informatics Effort in CACR • Engage Web Portal • BioDBx • Honest Broker • Velos Opportunity • How Much is Needed • Next Steps

9. Clinical Research Informatics • Introductions • Current State of Clinical Research Informatics Effort in CACR • Engage Web Portal • BioDBx • Honest Broker • Velos Opportunity • How Much is Needed • Next Steps

10. History of BioDBx • Originally developed by Steve Gruber / Joe Bonner for their own research • Independently chosen by both CACR and Cancer Center as clinical research software program of choice • In 2003, Gruber turned over further development of BioDBx to CACR

11. BioDBx • Flexible system to manage all types of data required for clinical research • Administrative suite to manage studies • Case report forms • Clinical data • Laboratory data • Images • Analysis

13. Successes Business model Adopted by most large clinical research groups Serves several hundred studies Has been key to getting several large grants (e.g. BAA) and will be key to future successes Failures Business model Poor management of existing users Unfulfilled promises BioDBx

14. Clinical Research Informatics • Introductions • Current State of Clinical Research Informatics Effort in CACR • Engage Web Portal • BioDBx • Honest Broker • Velos Opportunity • How Much is Needed • Next Steps

15. Honest Broker • Features • HIPAA-compliant storage of sensitive data • HIPAA-compliant routing of data between clinical care and research systems • Benefits • Supports research while reducing institutional exposure • Furthers scientific goals with little or no risk to human subjects

16. Honest Broker • Challenges • Provide enterprise-wide Plug-and-Play integration of clinical care and research data • Go beyond contract requirements to provide a best-of-breed end-to-end solution for interconnecting UMHS’ many clinical care networks with research systems