1. John Cai, MD, PhD
Director, Medical Informatics, Celgene
Real World Evidence & Market Access Summit 2015
Philadelphia, PA

2. This presentation represents the speakers’ personal
views of pathway analytics. The content does not
constitute any positions of Celgene or any other
organizations.
Disclosure

3. Presentation Outline
• Real-World Evidence (RWE)
• Real-World Data (RWD)
• Real-World Big Data (RWBD)
• Case study: Patient-level treatment pathways
using RWBD
• Summary

4. Market access requires Real-World
Evidence (RWE)
In real world, RWE is not at the foot of the hierarchy of evidence, but the fourth hurdle

5. • Cost effectiveness – Payer's willingness to pay
• Clinical effectiveness (long term efficacy and safety) – Physicians to
prescribe, patient to adhere
• Comparative effectiveness, patient reported outcomes – Physicians to
prescribe, patient to adhere
To Innovate To Approve To Pay for To Prescribe To Adhere
Industry FDA Physician Patient
Health Plan
IDS
Government
Healthcare decision making requires RWE
from RWD

6. Real-World Evidence (RWE) evaluates safety,
effectiveness and outcomes of various treatments using
Real-World Data (RWD)
What is RWE?
Acknowledgement: definitions from IMS Health
Real-World Evidence (RWE) as capability – data, tools,
processes, organization – underpinning several functions
to drive business intelligence

7. RWD: "Data used for decision-making that are not collected
in conventional randomized controlled trials (RCTs)”
What is RWD?

8. RWE in the “hierarchy of evidence”
RWE based on
RWD from
observational
studies

9. 3% cancer patients enroll in clinical trials
Few stories are told with RCTs
RCT data doesn’t provide a full patient journey!

10. RCT Evidence
Individual Patient
Benefit / Outcomes
Evidence-based Medicine
Precision Medicine
Acknowledgement: Caroline Robinson, PhD, Genentech
Patients are individuals and need Precision
Medicine

11. 1. When RCT is not possible:
– Don’t have the resources and luxury of time for RCTs or
when RCT is ethical
– Not every question require a trial for satisfaction
2. Precision Medicine requires RWE from RWD
– From “average” patients in RCTs to individual patients
undergoing routine clinical care
3. Because we now have lots of RWD— Big Data!
– EMR adoption
– Mobile/wearable technology
– Advanced analytics
Why RWE vs. RCT?
100
1,000
10,000
100,000
1,000,000
10,000,000
Phase 1 Phase 2 Phase 3 Phase 4 5 yrs 10 yrs
Typical RCT Data
Real World Data
#patients

12. Real-World Big Data (RWBD)
• Not RCT data and broader than observational data, RWBD is health
data collected from actual practice by healthcare providers or in day-
to-day situations by patients or caregivers
Real World
population
Observational
study
population
Clinical
Trial
population
Real-World Big Data

13. Real-World Big Data in the Evidence
Hierarchy
RWE from Real-World Big Data

14. Real-world Big Data vs. observational
studies
Observational Studies
1. Medical/epidemiological science
2. Driven by causal inference, etiologic
research, elucidating Nature
3. Evidence supposes a hypothesis
4. N=small or N=some; selected variables
5. Primary use of data collected following
study protocols
6. Structured or curated data; errors
minimized
7. Statistical analysis
Real-world Big Data
1. IT/Informatics science
2. Driven by/toward correlations,
associations, and patterns
3. Largely ‘theory-free’
4. N=large; all features
5. Secondary use of data
6. Structured and unstructured data;
errors embraced
6. Machine learning / data mining
Prediction
• “Personalized Medicine” or “Precision Medicine” will eventually benefit from Real-
World Big Data Analytics

15. • Longitudinal insurance claims
• Integrated EMR/EHR
• Large patient registry
• PHR/Patient forum/social media
• Medical device/mobile apps/wearables
Example of Real-world Big Data

16. Pharma
CER
Proactive
Pharmacovigilance
Trial Design
& recruitment
Precision
Medicine
Cost
Effectiveness
Drug Repurposing
/ new Indications
Payer/
PBM
Real World Big Data
?
?
Potential use of RWBD in Pharma

17. Case Study: Treatment Pathways
Based on Real-world Big Data
Analytics

18. Patient Journey is Complex
Real-world treatment pathways
can be messy
• Nature of healthcare
• Rationales unknown
• Physicians not following
clinical practice guidelines
• Patients not adherent to
medications
• Missing data
Treatment pathways are difficult to reconstruct
using healthcare data:
• Technical hurdles - need to repeatedly query
and merge across large # tables
• Conceptual hurdles of secondary use
• Claims and EMR for transaction
• EMR with MU for patient care

19. 19
• Use business rules to translate data to events of interest
- Example: ndMM patient cohort
• One inpatient diagnosis or two outpatient diagnoses (two separate dates)
– list of ICD9 codes
• One or more MM-specific treatments
– list of drugs and procedures
• First diagnosis: “index date”
• At least 6 or 12 months continuous coverage before index date
• At least 12 or 24 months continuous coverage after index date
• What is a therapy line?
• What is a drug switch, discontinuation, add-on, combo, “drug holiday”?
• Addresses some parts of the conceptual challenge
• Creates new problems
- How sensitive are our results to the rule definitions?
Typical solutions

20. Technical solution: Hadoop and
MapReduce
• Hadoop: an open source software project
- Hadoop Distributed File System (HDFS)
- MapReduce: compute paradigm for parallel computing
- A whole ecosystem of additional products/services/tools
• History:
- 2003 Google file system paper
- 2004 Google Map Reduce paper
- Adopted by Yahoo, donated to the open source community in 2009
• The gist of it:
- Distributed file system, “cheap” storage on computer clusters
- Compute paradigm that abstracts the parallelism by breaking down
operations to “map” and “reduce”
- Hadoop framework takes care of everything else

21. Map Reduce in a nutshell
Mappers work on data,
“emit” key-value pairs
Reducer works on all
values (data) for the
same key
Shuffle-Sort:
intermediary data
sorted and distributed
by key

22. 22
Building patient timelines using MapReduce
followed by visual analytics
Shuffle-Sort:
“Hadoop magic”
Mapper Reducer

23. Treatment Pathways
most patients started w/
corticosteroid, suggesting they got
their 1st diagnosis during a flare.
many patients started w/ aminosalicylate or
immunosuppressant, suggesting these were
mild cases

24. Individual Patient Time Lines
Pathway: xyz
•This is a severe case: starting with a flare and followed by another flare 2 yrs later. Should’ve this
patient been managed more aggressively after the 1st flare?

25. Further Analysis
• Cost of care analysis, comparing across different pathways
• Healthcare resource utilization analysis, comparing across
different pathways
• Comparison to Clinical Practice Guidelines - ongoing
• Physician specialty analysis, integrated with treatment
pathways - ongoing
• Patterns of care analysis: predictive modeling combining
patient similarity measures and clustering - planned
• Outcomes of care/CER: incorporating clinical outcomes using
integrated claims/EMR data – planned
• Future use cases: find “hard-to-find” patients

26. Storytelling by Pathways 2.0
• Patient Story
– Patient preference and non-adherence
– Tolerability and affordability
– Patient reported outcomes (PRO)
• Physician Story
– Diagnosis, referral, and treatment patterns
– non-compliance to or lack of guidelines
• Payer Story
– Payers pathways and drug formularies
To Innovate To Approve To Pay for To Prescribe To Adhere
Industry FDA Physician Patient
Health Plan
IDS
Government
Only longitudinal and
integrated data (i.e.
RWBD) can tell the full
story!