1. REAL-WORLD BIG DATA ANALYTICS
FOR UNDERSTANDING TREATMENT
PATHWAYS
John Cai
Director, Medical Informatics
June 11, 2015

2. Big Data for Pharma
Decision
making

3. The Fourth Hurdle requires Real
World Evidence from RWD
Cost-effectiveness (or CER) has became
the “fourth hurdle” to market access

4. Real World Big Data
Complexity
Variety
Unstructured
data types
e.g. clinical
notes
Volume
Massive data
sets, e.g.
longitudinal
claims/EMR
Velocity
Fast, real-time
data collection
and
transmission
e.g. HIE,
wearables

5. Volume: Real World Population and
Real World Data
• Real World Evidence (RWE) evaluates safety, effectiveness and outcomes using real
world data (RWD).
• Not RCT data and broader than observational data, RWD is health data collected from
actual practice by healthcare providers or in day-to-day situations by patients or
caregivers
Real World
Population
Randomized
Clinical Trial
Population
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 Pharma Data
Real World Data
#patients
Observational
Study Population

6. Variety: Major Real World Data
Types and Sources
• Claims (from payers or data vendors): Truven (MarketScan), IMS (PharMetrics),
United Health Group (Optum), Wellpoint, Aetna, Humana, CMS, ...
• EMR/EHR (from Healthcare providers or EMR vendors):
 Nation-wide: VA, DoD, GE Centricity, Allscripts, Cerner, Humedica, Flatiron, etc…
 Regional: Kaiser, Regenstrief, Partners, Mayo, Intermountain, Geisinger, ...
 Academic: Harvard, Univ of Utah, Vanderbilt, Cincinnati Children's Hospital, ...
• Surveys and registries: NCHS (NHANES, NHIS, NAMCS , NHAMCS, NSAS,
NHDS, NNHS, NNAS, etc.), SEER registries, MEPS, ACC registries, ...
• PBM/Pharmacy Databases: Medco, Wallgreens, CVS, Walmart, …
• Lab databases: Quest, Labcorp, …
• PHRs: patient portals, MS HealthVault™, Indivo X, CMS PHR Pilots, …
• Patient forums/social media: Patientslikeme, inspire.com, smartpatients.com…
• Monitoring/wearables: medical device data, Apple ResearchKit, …

7. Pharma
CER
EBM
Proactive
Pharmacovigilance
Trial Design
& Interpretation
PHC
Cost
Effectiveness
Drug Repositioning
/New Indications
Patient
recruitment
Velocity: Real World Data
Transmission to Pharma
Payer/
PBM
Real World Data
?
?

8. Complexity, Variability, Veracity
• Patient journeys are complex
• Real-world treatment
pathways can be messy
• Physicians not following
clinical practice
guidelines
• Patients not adherence
to medications
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 for transaction
• EMR for patient care

9. 9
• 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

10. Potential 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

11. Map Reduce in a Nutshell
Mappers work on data,
“emit” key-value pairs
 We write Mappers and Reducers
 Hadoop takes care of everything else
Reducer works on all
values (data) for the
same key
Shuffle-Sort:
intermediary data
sorted and distributed
by key

12. 12
• Load data into HDFS
- “Transactional” data (claims, interactions)
• Reconstructing a patient’s timeline is a textbook MapReduce
exercise:
- Mapper:
 Read a piece of data. Example: claim
 Figure out who it relates to. Example: patient ID
 Return key-value pairs:
Key: patient ID
Value: the full piece of information (claim)
- Reducer:
 Gets as an input a key and the set of all values (claims) associated with
that key (patient ID)
 Organize the values (claims) to produce a basic patient history
Building Patient Timelines using
Hadoop and MapReduce

13. 13
Building Patient Timelines using
MapReduce Followed by Visual Analytics
Shuffle-Sort:
“Hadoop magic”
Mapper Reducer

14. Treatment Cost Trends
14
 Cost analysis of PsA and PsO treatments
 Biologics treatment costs have been high and going up
 Presented to AMCP and ISPOR 2015 as posters

15. Co-medication Usage

16. Treatment Pathways

17. Patient timelines - “individual story”

18. Future Directions
 Cost of care analysis, comparing across different
pathways
 Healthcare resource utilization analysis,
comparing across different pathways
 Patterns of care analysis: predictive modeling
combining patient similarity measures and
clustering
 Comparison to Clinical Practice Guidelines
(Compliance and Adherence)
 Outcomes of care/CER: incorporating clinical
outcomes using integrated claims/EMR data

19. Some Learning Points
 Some Hadoop functionality perfectly suited for
patient timeline analysis
 Mapreduce for creating patient timelines
 Once patient timelines are created, everything else scales
linearly
 Map(reduce) for calculating patient metrics and
complex events
 Mapreduce for analyzing treatment pathways
 Cheap scalable storage capacity and compute
power
 Scalability allows robust analysis

20. Healthcare Decision Making Requires
Real-world Big Data Analytics
 Efficacy and Safety from RCT settings – FDA to approve
 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

21. Forthcoming
Thank You!
Leveraging Hadoop MapReduce in Building Patient Timelines and Analyzing
Health Resource Utilization
Special Issue on Big Data in Pharmacoeconomics
Saar Golde, Ph.D., Knowledgent Group and NYU
Zhaohui “John” Cai, M.D. Ph.D., Celgene Corporation