Todd Berner's presentation on how to define effective and efficient real world trials

1. How to Design
Effective
and
Efficient
Real World Trials
Todd Berner MD
Executive Medical Director, Urology
Astellas Scientific & Medical Affairs, Inc.
October 23, 2014

2. Muller RW, ECRI Institute Conference 11.28.2012

3. Providers
Assuming
Risk:
• Nationwide, about 120 provider-
sponsored health plans are owned by
hospitals or health systems or are in the
process of applying for a license to own
health plans
• Few provider-owned health plans
participate in exchanges
• About 15% of hospitals had PPOs, 13%
HMOs and 5% fee-for-service products
in 2011, with percentages relatively flat
over a decade, according to AHA’s latest
data
Health Plan Week August 19, 2013 Volume 23 Issue 28
There are limitations for smaller provider
organizations in taking full risk and
becoming an insurance plan, “because
you need large numbers in terms of how
capitated rates are set.”

4. Transforming Care Delivery
Transformation
of Care
Primary
Care
Snyder R, ECRI Institute Annual Conference Nov 28,2012

5. Listening to the Patient Voice in
Research

6. PCORI Determinants of Success in
Involving Patient’s in Research
 Are we producing high-quality, timely, useful,
trustworthy information?
 Is the information we produce being used?
 Are other (funders of) researchers following our
lead?

7. Efficacy
7
Real World Evidence
Effectiveness

8. Value Information= Valued Information
• Generate valued information regarding cost-
effectiveness useful to payers and/or policy makers
• Real World Evidence Development
• Follows from clinical findings, real-world
observations
– Retrospective data studies
– Models of care
– Prospective observations
– Measures of Patient Experience(PROs)
“Value is in the eyes
of the Beholder”

9. Value Evidence Generation
Effectiveness
Efficacy
Adherence
Individual
Patient
Characteristics
Tolerability
Access

10. Hypothesis
Question
Methodology
Data
Generation

11. Using Evidence to Inform
Development of Clinical Trials
• Retrospective Claims and Medical Records Database Analysis
• Analysis of treatment patterns and utilization
• Analysis of prevalence and population trends
• Burden of Illness
• Adherence and persistence assessments
• Economic and resource utilization
• Effectiveness studies and outcomes analysis
• Pharmacoepidemiology
• Prospective Research
• Patient Reported Outcomes
• Treatment patterns and resource utilization
• Quality of Life
• Productivity

12. RWE within the product lifecycle
Pre-launch Product Launch 1 yr post-launch 2+ yrs post-launch
Market
Shaping
• Burden of
Illness
• Gaps in
Care
• RCT
on-label
communication
• Establish
place in
treatment
• Analysis of
clinical trial
subpopulations
• Economic
evaluations
• Budget impact
• CE using RCT data
• PRO Measures of
Patient
Experience
RWD generation
• Adherence/
persistence
• Healthcare
Resource
Utilization
• Clinical Outcomes
• Patient
Satisfaction

13. Real World Evidence:
Efficacy vs. Effectiveness
Example-
 Conditions with significant patient subjective symptom
outcome measures (OAB, IBD, migraine)
• RCT data
Extremely high placebo response rates
Difficult to show efficacy for drug compared to placebo
It is essentially all non-pharmacologic therapy compared to
non-pharmacologic therapy + drug
• Real World data
All of the behavioral, non-pharmacologic intervention
associated with the RCT moves over to the drug side of the
ledger
This becomes a comparison of activated, engaged Rx
recipients vs. those with just an Rx

14. Inception Point:
The ‘Representativeness’ Issue
Robert M. Califf, MD
Considerations in Designing Acute
Decompensated Heart Failure Clinical Trials

15. Pragmatic Clinical Trials
Brass EP Clin Pharmacol Ther 2010

16. How much could we save if
RWE replaced just a single clinical trial?
Cutting Edge Survey
Phase IIIB/IV Clinical Trials 2011

17. Capturing Stakeholder Voices
Uncovering the true needs
• In this changing landscape, they may not have a
clear idea
• Use therapeutic expertise to generate ideas
• Build on those ideas they do have
• But this might not be enough
• May need to actually work together to figure it out
• Collaborative Research
The
“We don’t have
the bandwidth ”
Challenge

18. Complexities of Defining Quality
 Measurement Challenges
• Disparate data sources needed
• What data should be reported that will be
useful to stakeholders?
 to achieve behavior change
 for patient choice
 for payer market decisions
• Criteria for Outcome Measures
 Risk adjustment
 Capture few unintended consequences
The Joint Commission
National Patient Safety Goals 2012

19. Quality Metrics
Benefit from a focused clinical set of proven,
evidenced-based process and outcome
measures that are most likely to drive quality
outcomes and have utility for patients
Tie to the power of payment policies
Appreciate that clinical measures not credible to
clinicians don’t get measured well, nor do they
result in quality improvement
Many measures may not even contain the
information needed to make QI changes
The Joint Commission
National Patient Safety Goals 2012

20. Evidenced Based Solutions
Measurement is not the endpoint
 Need to find ways to improve on performance,
using tested proven solutions that are
sustainable because they are imbedded into
practice & are widely accepted
Bring process improvement into solutions
Solutions must be customizable to setting
Improvements must be measurable to know if
they work and at what cost
The Joint Commission
National Patient Safety Goals 2012

21. Performance Improvement Research Collaborative:
Blueprint
Phase I Phase II Phase III Phase IV
Research:
Burden of Illness
Predictive Model
Project Phases
Recruit Health Pilot
Sites
Design Change
Package
Implement
Change Package
in Pilots
Collect Data
Disseminate Best
Practices
Learned
Analyze Align Implement Spread
Partnering to deliver improved care at a reduced cost.

22. Mapping the Patient Journey
Baker GR, Schwartz F
Healthcare Quarterly, 8(3) May
2005.doi:10.12927/hcq.2005.17194

23.  Patients newly initiating therapy
Index event captured by HCP Investigator
 Patient then engages with Registry and provides ePRO data
throughout the course of 12 month observation period,
independent of whether they:
 Remain on treatment
Switch or add on additional treatments
Change or consult with multiple providers
Naïve sites; Weighted PCP representation;
 Linkage to multipayer/allpayer claims data of meaningful
scale is still lacking
The Patient Journey-
An Observational Registry

24. Phase 1
• Descriptive analysis (baseline characteristics, treatment
patterns (adherence (primary and secondary),
discontinuation, and switchers) and healthcare resource
use.
• Identify risk factors associated with treatment patterns
Phase 2
• Survey patients newly initiated on therapy who
subsequently Persist, Switch, or Discontinue
Phase 3
• Implement Quality Improvement
intervention using results from Phase II in
Health System Patient population with the
condition
Primary Nonadherence within a
‘Closed’ Healthcare System

25. Performance Improvement within a Health System with
Significant ‘excursions’ of Patient Care Outside the System
• Assess treatment approaches, Explore several measures:
 Compare the number of visits during which condition was:
 Appears in the problem list
 Listed as a diagnosis
 A drug was prescribed for the condition
 Examine appropriateness of referral patterns:
 PCP should try at least one drug for this condition– but only one – before
referring to a specialist
 Referrals made without trying any drug or after prescribing more than one
will be classified as potentially inappropriate or suboptimal
 Subgroup analysis of referrals from the Health System’s primary care
clinic network vs others
 Examine the use of diagnostic testing and imaging
 Examine the use and documentation of validated symptom assessment tools.
 Compare presentation of treatment options to patients among those that we
can identify via the EHR

26. EMR Based Condition Specific Prompts and
HCP Clinical Decision Support
National Quality Strategy--The Future of Quality Measurement
ONC, AHRQ, CMS Presentation. September 14, 2012

27. EMR Based Condition Specific Prompts and
HCP Clinical Decision Support
• Clinical Decision Support (CDS)
 Detect potential safety and quality problems and help prevent them
 Detect inappropriate utilization of services, medications, and
supplies
 Foster the greater use of evidence-based medicine principles and
guidelines
 Organize, optimize and help operationalize the details of a plan of
care
 Help gather and present data needed to execute this plan
 Ensure that the best clinical knowledge and recommendations are
utilized to improve health management decisions by clinicians and
patients
Osheroff JA, Pifer EA, Teich JM, et al.
Improving Outcomes with Clinical Decision Support: An Implementers' Guide
Chicago: HIMSS; 2005.

28. What Does Good Look Like?
Toward Continued Excellence
• More patient-centered care
• More predictable outcomes
• Low tolerance for unwarranted deviations
from the norm
• Collective Mindfulness
• Greater efficiencies, less waste
• More appropriate/evidence based care
that meets shared expectations
• Evolving “Learning” Systems
The Joint Commission
National Patient Safety Goals 2012