Life sciences and pharma companies are evolving their strategies to utilize Real World Data (RWD) to demonstrate value of pharmaceutical and medical device innovations. Technology advancements at the point of care and improvements in data collection strategies have led to a significant increase in the availability of RWD in healthcare Real World Evidence (RWE) can provide actionable patient insights and accelerates time to market of new medical products in order to gain competitive advantage With the emergence of wearable technologies, Internet of Things (IOT), Cognitive Computing, Genomics, Blockchain, etc., future RWE data sources will become more diverse and extensive. This document introduces the concept of Real World Evidence studies in healthcare, describes the various data sources for performing real world analytics and illustrates the role of RWE in better patient care. It then summarizes challenges faced while performing RWE analytics with respect to regulatory compliance, data accessibility and sharing, analysis reporting, costs etc.

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Accelerating Patient Care with Real World
Evidence
26 August, 2017 | Author : Shweta Joshi | Healthcare Business Analyst
CitiusTech Thought
Leadership

2. 2
Objectives
 Life sciences and pharma companies are evolving their strategies to utilize Real
World Data (RWD) to demonstrate value of pharmaceutical and medical device
innovations
 Technology advancements at the point of care and improvements in data collection
strategies have led to a significant increase in the availability of RWD in healthcare
 Real World Evidence (RWE) can provide actionable patient insights and
accelerates time to market of new medical products in order to gain competitive
advantage
 With the emergence of wearable technologies, Internet of Things (IOT), Cognitive
Computing, genomics, Blockchain, etc., future RWE data sources will become
more diverse and extensive
 This document introduces the concept of Real World Evidence studies in
healthcare, describes the various data sources for performing real world analytics
and illustrates the role of RWE in better patient care. It then summarizes
challenges faced while performing RWE analytics with respect to regulatory
compliance, data accessibility and sharing, analysis reporting, costs etc.

3. 3
Agenda
 Introduction
 Value and Role
 Data Categories, Data Format and Standards
 Types of RWE Studies
 Challenges & Opportunities to Leverage RWE
 Leveraging Technology Solutions for RWE
 Latest Healthcare Trends in RWE
 References

4. 4
Introduction
 A Real world evidence (RWE) study is a scientific study of Real world data (RWD) using
appropriate statistical and/or commercial analytics
 RWD encompasses patient healthcare data that is collected outside of clinical trial
environment
 Sources of RWD includes:
• Administrative claims data from insurers and government health programs
• Clinical data from medical records
• Surveys
• Patient registries
• Molecular and laboratory results data
• Social media and mobile technologies
• Health “apps” and monitoring devices capturing health and lifestyle information
• Linked data sources (e.g. claims linked to electronic health records)
 RWE-based approaches puts forth a better understanding of patients experience throughout
the treatment journey

5. 5
Value and Role of RWE
RWE study is gaining more importance with:
 Increasing awareness of the fact that the real world-effectiveness of a treatment or a
medical product may differ from its efficacy/safety profile as shown in clinical studies
 Knowledge of patient characteristics in real life which differ from patients included in
clinical studies who fulfill stringent inclusion/exclusion criteria
 The ability to provide deeper insights into the patient journey, treatment pathways,
understanding of disease patterns and treatment effectiveness
Pharma companies and life sciences organizations are increasingly using RWE to:
 Differentiate their products in a saturated and competitive environment
 Provide information on relevant health outcomes, including cost of care
 Prove value to a price-sensitive healthcare marketplace
RWE play an important role in:
 Launching strategy and market access of drugs, medical devices or medical products
 Demonstrating product value of drugs, medical devices or medical products

6. 6
Data Categories & Source
Data relevant to RWE comes from:
 Claims data derived from insurance reimbursements
 Clinical setting data derived from medical records & patient care
 Pharmacy data derived from prescription orders and fulfillments
 Patient-reported data
Claims Clinical Setting Pharmacy Patient Reported
Medical Claims
Prescription Drug
Claims
EHR'
Pathology Labs Data
Genomic Data
Point of Sale Data
Prescription Fill
Data
Patient Reported
Outcomes
Social Media
State Medicaid
Insurance
Companies
CMS
Providers
Clinical Labs
Genetic Test
Companies
Pharmacies
Prescription
Benefit Manager
Patients
Patient
Communities
Patient Networks
Claims Databases
Analytics Companies
FDA Sentinel
Analytics Companies
Genomics Databases
PCORnet
Patient Advocacy
Organizations
Types
of data
Primary
Users
Secondary
Users

7. 7
Data Categories & Source
Commercial data providers:
Commercial Data Providers Real World Evidence Data Categories
EHR
Claims
Data
Health
surveys Registries
Social
Media
mHealth apps &
devices data
DaVita
FlatIron
Truven Health
Optum
IMS Health
Premier
Millman Inc.
DataBay Resources
WebMD Health
CMS
Medicare.gov
Data.gov (DHHS)
Health Verity
National Cancer Institute
Validic
Center for Disease Control and
Prevention (CDC)
represents YesNOTE:

8. 8
Data Format and Standards
 RWE studies use healthcare data from multiple sources and heterogeneous
systems
 Sources could include commercial data vendors, data partners like
Hospitals/ IDNs/ HIEs/ Clinical Networks etc.
 Each data source follow different data standards and data structure. e.g.
SAS, .CSV, HL7 etc.
 Common data model (CDM) is necessary to harmonize and collect
healthcare data from various sources for concurrent RWE studies
 Currently Observational Medical Outcomes Partnership (OMOP) is widely
used CDM for RWE studies
 Other frequently used CDM are PCORnet and FDA Sentinel

9. 9
Types of RWE Studies
RWE Studies Description
Risk assessment
Analyzing RWD to evaluate the probability of an adverse event
occurring.
Treatment Adherence
Determines the degree to which a patient correctly follows medical
advice.
Treatment pattern Examines treatment pattern for a disease indication.
Post-marketing Safety
Profile
Aims to monitor safety profile of a medicinal product.
Incidence rate
Determines the number of new disease cases at risk in a population
in a given time period.
Quality of life
Assessment of individual's satisfaction with life in terms of health
status, measured by factors such as physical, psychological, and
emotional functioning.
Hospital Admission
Analyzes hospital readmission cases to identify if an association
exists between treatment administered and readmission rates.
Comparative Effectiveness
Compares outcomes of various treatments to determine the most
effective one.

10. 10
Challenges
 Keeping track of regulations to respect
patient privacy and patient consent
 An in-depth and updated understanding of
the rapidly changing regulatory
environment, contractual mechanisms and
data privacy requirements is necessary for
successful RWE studies
 Lack of clarity from FDA for use of RWD to
support more effective drug development
and post-marketing commitments
 Policy on data sharing with external
organizations
 Challenges in securing patient consent
for data use
 Access through data holder or third party
 Need for reliable standards and data
formats to capture patient data from
disparate sources. For example, HL7 for
EHR, CMS-1500 for medical claims etc.
 Absence of consistent data standards to
promote replicable real world analysis.
For example, OMOP is used widely but is
still not a universal standard like HL7
 Lack of IT expertise in big data
 Efforts for data maintenance &
sustainability
Regulatory Compliance Data Access & Sharing

11. 11
Challenges
 Lack of individuals with expertise on
healthcare domain and advance analytics
 Difficulties in keeping up with rapidly
evolving BI tools and technologies
 To ensure adherence to RWE data
protection and data privacy there are no
set rules on publication of studies
 Tackle challenge on study findings from
competitors or external organizations
 Unclear value proposition/ROI
 Costs associated with
• Data handling and maintenance
• Storing data in standard format and
standard healthcare vocabulary
• Governing data access rights and
data security
Analysis & Reporting Costs

12. 12
Addressing RWE Challenges
 Steps to tackle existing challenges includes:
• Open data initiatives e.g. OpenFDA
• Clinical trial data access expansion e.g. Yale’s YODA project, PhRMA-EFPIA etc.
• Maturation of state All Payer Claims Databases
• Evolution of the FDA Sentinel Program
• Potential FDA regulatory changes to allow RWE based product labeling
 Initiatives to develop consensus on practices that will enable transparent,
scientifically robust and replicable RWE are on rise
 For the successful implementation of RWE, pharma companies need a partner who
can help them overcome technical and regulatory challenges associated with RWE
 A framework to build integrated RWE analytics that can process RWD from multiple
sources in compliance with latest regulations is the need of the hour

13. 13
Opportunities to leverage RWE
Clinical Trials
To improve clinical trial design for a more
targeted/personalized therapies
Health Economics and
Outcomes Research
(HEOR)
To fully understand product value in healthcare and its
potential in real-world clinical practice
Drug Safety
To identify safety events/signals in real world and provide
insights to potential factors of risk
Epidemiology
To gain valuable clinical insights by following the complete
continuum of a patient’s care
Market Access
To gather evidence of clinical effectiveness and cost
effectiveness of medical product
Sales Operation
To measure sales and marketing effectiveness and develop
precise sales plans as well as sales forecasts

14. 14
Leveraging Technology Solutions for RWE
 Collection of RWE data requires health IT systems to capture medical and patient data from
electronic health records (EHRs), claims/registry data, health sensors for real time data
capture, lab/biomarker data, social media, clinical outcomes studies etc.
 RWE studies requires Health IT infrastructure complying with all Health Insurance Portability
and Accountability Act (HIPAA) and patient-confidentiality standards
 RWE uses patient data for a purpose other than primary care and hence the information
needs to be de- identified in order to protect their privacy. This has lead to increase in
demand for de-identification/anonymization software and services
 Software to link de- identified clinical data to claims are necessary to implement
comprehensive RWE analytics
 Data Adaptors are required to consume RWE data from multiple sources and transform it
into standardized format
 Life sciences and pharma companies are rapidly adopting cloud to support RWE. Hence
demand for health IT vendors providing HIPAA compliant infrastructure on cloud is bound
to increase.
 In the near future, advanced RWE studies would be implemented by leveraging machine
learning, Natural Language Processing (NLP), cognitive computing and genomic analysis

15. 15
Latest Healthcare Trends in RWE
 21st Century Cures Act has led to many life sciences companies investing in RWE and
data strategies
 There has been a greater adoption of cloud based platform and big data technologies
such as Hadoop, Spark, MapReduce, Impala, Hive, etc. for RWE analytics
 RWE is set to revolutionize healthcare through remote monitoring, disease
management and early detection
 Advance RWE tools would help clinicians use real time patient data for improved
patient care
 RWE studies to improve clinical trials enrollment and optimize clinical trials
 New standards have emerged beyond Randomized Control Trials (RCTs) to address
requirements for trials on basis of RWE
 Payers and regulators will relay more on RWE to identify cost-efficient yet effective
and high-quality therapies
 The healthcare industry is making significant investments in advanced analytics to
process unstructured notes and genomics data, to derive meaningful insights

16. 16
References
 Real World Evidence: A New Era for Health Care Innovation
http://www.nehi.net/publications/66-real-world-evidence-a-new-era-for-health-care-
innovation/view
 Real-world study planning: A systematic approach
http://www.ingress-health.com/why-ingress/whitepapers/
 Getting real with real-world evidence
https://www2.deloitte.com/us/en/pages/life-sciences-and-health-care/articles/real-world-
evidence-benchmarking-survey.html
 Case studies on real world evidence impacting payer decisions
http://imsbrogancapabilities.com/en/health-access/real-world-evidence.html
 How can pharma companies take advantage of the real-world data opportunity in
healthcare? IT insights, October 2011

17. 17
Thank You
Authors:
Shweta Joshi
Healthcare Business Analyst
thoughtleaders@citiustech.com
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