This document summarizes a discussion between Christy Gilchrist from Wellspan Health and Todd Tullis from goBalto on using site intelligence and predictive analytics to improve clinical trial feasibility assessments, site selection, startup, and performance evaluation. Some key points discussed include: 1) Using data analysis of electronic health records and epidemiological models to better predict patient enrollment expectations and feasibility at sites. 2) Measuring site and sponsor responsiveness to startup tasks in real-time to facilitate faster resolution of issues. 3) Evaluating site performance against enrollment goals, compliance goals, and business goals to help sites improve for future trials. 4) Sharing post-study performance data with sites to build

1. Christy Gilchrist Todd Tullis
Briefing on
Director of Research Sr. Director, Product Management
Site selection & startup Wellspan Health goBalto, Inc.

2. What is site intelligence?
• Our capability to analyze data, measure
performance, and more confidently predict
results
• For CROs, sponsors, and sites

3. If you could wave your
CSI magic wand…
What would be included?
What would that mean to you?

4. Haven’t we asked these
feasibility questions before?
Which sites do we think will
enroll at a sufficient rate?
Can we trust these
new sites? Is our investigator
database current?
This change won’t be
an issue for sites, right? What are the ‘initiation
wrinkles’ for this site again?

5. Haven’t we answered these feasibility questions before?
(like, last week?)
Do we have the right Will we get another chance
patients for this trial? if we say “no”?
Is this study good Doesn’t the sponsor
for our business? have our CVs on file?
What’s my plan for What do I need to do
IRB approval? to get initiated?
Have we been selected? When will we be ready to enroll?

6. Site example:
strategic research program planning
• Find your niche
• Tell the world
• Contacting you must be easy

7. Sponsor/CRO example:
a predictive approach to site selection
• Objective and subjective measures
• Everything is relative
• Finding historical data can be tough
• No substitute for building site rapport

8. Site example:
EHR analysis for enrollment expectations
• Screening/flagging potential patients
• Epidemiology models for patients, advertising
• Statistical modeling for success
• Contract specifically for patient feasibility

9. Sponsor/CRO example:
measure & share metrics during startup
• Site and sponsor “responsiveness” to startup tasks
• Real-time intelligence facilitates faster resolution
• Transparency to metrics can drive performance

10. Site example:
evaluating our performance vs. plan
• Enrollment goals met – target numbers and date?
• Compliance goals met?
• Business goals met – plan budget vs. actual cost?
• How can we do better next time?

11. Sponsor/CRO example:
post-study utility of CSI data
• An opportunity to build better relationships
• “They never offered to share the data they were
keeping on me”
• Allow for rationalization of outlier metrics
• Should performance data be posted to registries?
• Rafael Dal-re et. al, 27 Dec 2011

12. Takeaways
for sponsors/CROs: for sites:
• Measure what you • You are selling usable
think is important data, and plenty of it!
• Data can motivate • Preparation pays off
• Sites care about • Leverage the data that
your evaluations you collect

13. Thank you!
Christy Gilchrist Todd Tullis
cgilchrist@wellspan.org ttullis@gobalto.com