Longitudinal analysis of anonymized patient level data (APLD) is a powerful tool for assessing patient experience on a granular level that will lead to better treatment outcomes and increased life sciences market penetration.

1. Leveraging Anonymized Patient Level
Data to Detect Hidden Market Potential
By collecting and analyzing aggregate patient data across the life
sciences and healthcare value chains, pharma companies can derive
and apply deeper insights to deliver better patient experiences
and outcomes, and discover new market segments for incremental
revenue generation.
Executive Summary
As the blockbuster drug era comes to a close,
pharmaceuticals companies face a host of new
challenges as a result of shifting industry sands.
These include:
• Market: Decision-making in primary care has
shifted. Accountable care organizations (ACOs)
and payers are limiting access to physicians
based on price, especially in genericized
markets, and also to new drugs to curb the
explosion in care costs. These rules even apply
to specialty medicines, where selling costs are
significantly lower due to the fewer number of
physicians to be targeted (as the number of
physicians prescribing specialty medicines is
relatively lower).
• Regulatory: The introduction of the Affordable
Care Act (ACA) has forced the healthcare
industry to shift to a value-driven model;
pharmaceuticals companies and healthcare
providers are all marching toward producing
efficient and effective health outcomes.1
•
outcomes and is believed to cost the healthcare
system up to $290 billion annually.2
As a result,
it is imperative for healthcare providers and
physicians to better ensure patient adherence
to treatment therapies and regimens that
improve outcomes and reduce avoidable
healthcare expenses.
• Data: Given the above challenges, there is a
greater need for a more accurate and consistent
view of the market. For instance, companies
need to understand patient behavior as well
as the potential within the untreated segment
as treatment outcomes become the primary
driver for differentiation. Patient benefit needs
to be a primary focus, as well as the related
cost-effectiveness, which requires robust and
accurate data acquisition.
Regulations such as the ACA and Health Informa-
tion Technology for Economic and Clinical Health
(HITECH) can introduce the healthcare triple aim
of better health outcomes at an improved cost and
patient experience.3
For this reason, the industry
needs to deliver a better patient experience. To
get there, industry players need to understand the
patient medical journey and explore avenues for
Therapy adherence: Patients’ nonadherence
to treatment can be detrimental to health
cognizant 20-20 insights | october 2015
• Cognizant 20-20 Insights

2. 2cognizant 20-20 insights
enhancing how patients experience healthcare.
This requires fact-based business decisions to
be made using patient-derived insights across
various phases of the pharma value chain (i.e.,
from drug discovery and prescription, through
the treatment continuum).
While traditional data sources provide informa-
tion on physician activity, they do not allow the
user to peer into the treatment regimens used
for individual patients. Moreover, they offer
only a limited ability to track patient behavior.
Anonymized patient level data (APLD), on the
other hand, provides insight not only into the
patient journey but into patient behavior. This
data also offers insights into physician prescribing
behavior and the effectiveness of the treatment.4
Pharmaceuticals companies realize that their
therapies work for a subsegment of people, but
not all. Rather than target everybody with the
same medicine, the approach is to identify the
right target patient for the right therapy. This
white paper lays out a strategy to help pharma
companies analyze APLD data and enable
business users to better understand customer
segments and target their products to address
the needs of those segments.
Approach to Conducting
Longitudinal Analysis
A patient-centric approach is beneficial to all
stakeholders in the life sciences and healthcare
industries. We apply APLD in client engagements
alongside traditional data sources to provide
metrics that offer information at the patient
level, which is more granular and thus yields
more accurate insights into patient and physician
behavior. This can then be used for strategic
decisions that create greater value for physicians,
patients and payers.
Essentially, APLD is healthcare-utilization data
that can be linked to individual patients, longitudi-
nally. This is data that tracks a patient’s healthcare
utilization over time. It provides patient informa-
tion on their interactions with each physician
and reveals those patients who were diagnosed
with what diseases, and which medication was
prescribed and used, etc. This data is captured
through similar sources as standard prescription
data. For example, patient-level data is collected
from various components of the healthcare
system (e.g., pharmacy, hospitals/clinics, payers
and physicians) and compiled as a longitudinal
database (see Figure 1).
Patient Data Creation Based on Various Moments
of Truth Across Patient Lifecycle
Patient sees doctor.
APLD Data
Providers
Patient hospitalized. Then visits the
pharmacy…
Where the RX
gets validated
based on insurance.
Then the RX gets
processed by the PBM…
And enters the
payer database.
■ IMS
■ Symphony Health
Solutions
■ Truven Health
Analytics
■ I3 Analytics
Figure 1

3. cognizant 20-20 insights 3
Available through various data providers like IMS,
SDI and SHS (again, see Figure 1), APLD data,
along with internal data dictionaries, is accessible
to industry players through a secured virtual
private network. To maintain privacy and prevent
data leakage, this database can be accessed
through a remote server. This data pertains to a
particular therapeutic area that is filtered for the
disease of interest. The information extracted for
population subsets can then be grouped into two
broad categories:
• Care pathway analysis: The treatments/
regimens patients have undergone.
• Diagnosis Information: Diseases for which
patients have been diagnosed.
This information, when refined using defined
business rules, can yield many useful metrics. The
business rules are defined according to a deep
understanding of the therapeutic area, disease
and its treatments by subject matter experts
(SMEs)/consultants. Some of the important APLD-
derived metrics are depicted in Figure 2.
Critical Success Factors
Key advantages of APLD vs. traditional databases
include:
• Greater granularity: Links physicians and
patients with the drugs, diagnoses and
procedures used to measure the effectiveness
of treatment, options for combination therapy,
co-morbidities, drug effectiveness, etc.
• Deeper insights: Helps identify patient and
physician behavior.
• More revealing: Has the ability to derive more
insights about the market trends as it contains
patient-level transactions on every treatment
date (i.e., patient birth year, patient gender,
associated physicians and their specialties,
region, site of treatment, drugs used, insurance
payer associated, etc.).
Applying APLD Data
Integrating APLD with other datasets and applying disease-specific business rules to
generate KPIs from a 3P (physicians, patients and payers) perspective.
Drug
Identification
APLD Claims
Vendors
Specialty
Pharma Data
EMR Data
Promotional
Data
Calls Data
Sales Data
External
Sources
Remote
Server
Client Internal
Systems
Secure
VPN
Syndicated
Therapeutic
Area Patient
Population
Population
Subset for a
Particular
Disease
Diagnosis
Information
Therapy Identification
(Surgery, Radiation,
Chemo, Biological,
Hormone, Alternative)
Care Pathway
Analysis
Identify Patient
Population by
Physician Specialty
and Line of Therapy
Patient-Centric
Metrics
Brand-Centric
Metrics
Physician-Centric
Metrics
Payer-Centric
Metrics
• Claim Approval Rates
• Claims Payer Mix
• Co-pay Card Analysis
• Sales-Based Deciding
• New vs. Continuing
• Zip Level Penetration
• Physician Uptake
• NRx vs. NBRx
• New Patient Share
• Source of Business
• Utilization by Disease
• Treatment Adherence
• Mono Vs Combo
• Avgerage Dose per kg
• Percent of High dose
HCO
Affiliations Data
$
Figure 2

4. Quick Take
Situation:
A leading global biotech company sought to
measure the efficacy of its patient support program
covering a particular therapy. The objective of the
support program was to drive patient belief in one
full year of treatment, awareness of support and
financial resources, and awareness of opportunities
to reinitiate paused treatment by sending periodic
reminders to patients about their next visit to the
clinic, next refill date, etc. Although program enroll-
ment was voluntary, the company’s sales force
encouraged doctors to enroll their patients.
Solution:
• Among the patients who opted into the support
program, APLD identified patients with stable
treatment history and minimum gaps in
treatment.
• Patient-level treatment parameters as in line of
therapy, duration of therapy, dosage pattern,
site of care, age, gender and payer were
calculated by processing APLD data.
• Enrolled patients were matched to non-enrolled
patients on several parameters (primary tumor,
line of therapy, age, gender, payer, site of
care, etc.).
• Enrolled and non-enrolled patients were
compared to measure the program’s impact on
duration of therapy, compliance and number of
drug infusions.
Analysis Outcomes:
Analysis clearly demonstrated that patients who
had enrolled in the program had significantly
longer duration and received one to two additional
brand infusions, on average, than patients not in
the program. Importantly, these patients were
more adherent toward treatment.
Analysis also identified areas for improvement in
subsequent campaigns — reduced cost per lead,
improved qualification rates and further drive
persistence.
Measuring Efficacy of Patient Care Programs
4cognizant 20-20 insights
Campaign Results
Flowchart for identifying analysis
universe of qualified patients
Duration of therapy for groups of patients
25.6 21.722.1 19.119.3 16.4
Total Duration Compliant Duration
■ Non-CARES
■ Proactive
■ Enrolled
▲ Non-CARES
Proactive (n = 9)
Enrolled (n = 27)
0%
20%
40%
60%
80%
100%
1 2 3 4 5 6 7 8 9 10 11 12 >12
ProportionReceivingInfusion
Number of Brand Infusions
Proportion of patients receiving a
number of brand infusions
X
X
= =
Average Cost per Lead
(CPL) = $2,538
ROI estimation approach
Total CARE
Program
Enrollees
Enrollees
with valid
patient IDs Valid patients
with stable
claims*
Not Brand-A Patients
Brand-A Patients with no
Brand-A claims
post CARES enrollment
Qualified Enrollees
Percentage of Enrollees
that Are Qualified *
Number of CARES
Enrollees
Total Impact = $4.2M
Total Cost = $3.5M ***
Incremental Net Rev
per CARES Qualified **
358
106
4
2,301
1,948
468
2,301
$4,925
ROI ≈ 1.2:1
A
85%
62%
24%
76%
23%
1%
* Source: Estimate based on the percentage of patients receiving Brand-A after enrolling in CARES. (52% yields 1:1 ROI).
** Source: Duration analysis using CARES vs. non-CARES patients based on claims data.
*** Source: All CARES costs from the Patient Marketing Team.
Figure 3

5. cognizant 20-20 insights 5
Looking Forward: APLD Benefits
and Outcomes
• APLD helps pharmaceuticals companies in
tactical and strategic decision-making by:
Providing valuable physician behavior in-
sights to develop comprehensive and proac-
tive messaging strategies for improved phy-
sician segmentation and targeting.
Validating the insights obtained from prima-
ry market research by replicating the busi-
ness KPIs using APLD as secondary data.
Delivering a quantitative estimate of the rev-
enue potential in chronic disease markets
with the ability to link disease diagnosis and
treatment data.
Leveraging the granularity of APLD data to
improve the accuracy of the brand’s sales
forecast based on derived persistence and
compliance rates.
Enabling effective and focused targeting
across patient and physician segments,
resulting in improved ROI in promotional
activities.
• Physicians can infer the treatment outcomes of
various therapies at the aggregate population
and patient levels so that they can personal-
ize treatment approaches to different types of
patients.
• Patients will benefit through improved
treatment outcomes, better experiences and
better engagement.
• ACOs will be able to measure effectiveness of
treatment to differentiate drug potential to
optimize formulary and treatment recommen-
dations for physicians.
Footnotes
1 http://www.fiercebigdata.com/story/obamacare-spurs-growth-data-analytics/2013-09-10
2 http://www.todaysgeriatricmedicine.com/archive/0115p12.shtml
3 https://www.digitalnewsasia.com/insights/four-top-trends-in-healthcare-data-analysis
4 http://www.pm360online.com/new-ways-to-evaluate-physicians/

6. About Cognizant
Cognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business
process outsourcing services, dedicated to helping the world’s leading companies build stronger busi-
nesses. Headquartered in Teaneck, New Jersey (U.S.), Cognizant combines a passion for client satisfac-
tion, technology innovation, deep industry and business process expertise, and a global, collaborative
workforce that embodies the future of work. With over 100 development and delivery centers worldwide
and approximately 218,000 employees as of June 30, 2015, Cognizant is a member of the NASDAQ-100,
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fastest growing companies in the world. Visit us online at www.cognizant.com or follow us on Twitter: Cognizant.
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About the Authors
Srikanth Kumar Konduri is an Engagement Manager within the Cognizant Analytics Practice, specializ-
ing in life sciences and healthcare analytics. He has more than five years of analytics experience working
with global pharmaceuticals, healthcare and high-technology companies, on assignments spanning
sales force effectiveness, audit reporting, promotion efficacy measurements, campaign management
and patient data analytics. Srikanth holds an M.B.A. in finance from the Institute of Management
Technology, Ghaziabad, and a bachelor’s degree in electrical and electronics engineering from JNT
University, Hyderabad. He can be reached at Srikanth.Konduri@cognizant.com.
Reemanshu Sharma is an Associate within the Cognizant Analytics Practice, focused on life sciences and
healthcare. She has more than five years of experience in data analytics and customer insight, where she
has gained extensive experience involving sales management, market research and patient analytics.
Reemanshu holds an M.B.A. in finance from ICFAI Business School, Hyderabad, and a bachelor’s degree
in electronics and communication engineering from UPT University, Lucknow. She can be reached at
Reemanshu.Sharma@cognizant.com.
Yuvaraj Natarajan is an Associate within the Cognizant Analytics Practice, focused on life sciences
and healthcare. He has more than five years of experience in data analytics and audit reporting,
working across the areas of sales force effectiveness, physician affiliation management and patient
claims analytics. Yuvaraj holds an M.B.A. in marketing from Bangalore Management Academy and a
bachelor’s degree in mechanical engineering from Anna University, Chennai. He can be reached at
Yuvaraj.Natarajan@cognizant.com.