John Cai, MD, PhD Director, Medical Informatics, Celgene

Real World Evidence & Market Access Summit 2015

Philadelphia, PA

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

Disclosure

Presentation Outline

• Real-World Evidence (RWE)

• Real-World Data (RWD)

• Real-World Big Data (RWBD)

• Case study: Patient-level treatment pathways using RWBD

• Summary

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

• 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

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

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

What is RWD?

RWE in the “hierarchy of evidence”

RWE based on RWD from observational studies

3% cancer patients enroll in clinical trials

Few stories are told with RCTs

RCT data doesn’t provide a full patient journey!

RCT Evidence

Individual Patient Benefit / Outcomes

Evidence-based Medicine

Precision Medicine

Acknowledgement: Caroline Robinson, PhD, Genentech

Patients are individuals and need Precision Medicine

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

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

Real-World Big Data in the Evidence Hierarchy

RWE from Real-World Big Data

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

• 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

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

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

Analytics

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

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• 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

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

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

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Building patient timelines using MapReduce followed by visual analytics

Shuffle-Sort: “Hadoop magic”

Mapper Reducer

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

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?

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

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!