AW uses state-of-the art big data technologies, expert analytical methodologies, and deep healthcare industry expertise to mine massive claims databases to derive targeted insights for Patient Journey Analysis, Physician Targeting, Outcome Prediction, and more.

1. Analytical Wizards
Claims Data Navigator
Targeted Insights Mined from Real World
Data

2. Patient Journey – Disease-specific patient cohort selection
to address targeted business needs
• Individual brands can be displayed, rather than drug
classes
• Versions for medical diagnoses can be created to
understand disease progression and comorbidities
• Patient cohorts are custom-developed to derive
focused insights for specific business challenges
2© 2017 AnalyticalWizards INC. All rights reserved.

3. • Analyses can be performed for any
variable captured in the data
• Patients within any node along any
strand can be profiled cross-sectionally
or longitudinally
• Elapsed time between medical events can be
determined
• Events can include diagnoses, tests, and/or
treatments
3© 2017 AnalyticalWizards INC. All rights reserved.

4. Claims Data Navigator Applications
Macro Level
Patient Journey
Mapping
Physician
Targeting
Market Sizing
and Forecasting
Patient Profiling
and Targeting
Inform Clinical
Trial Design
Advanced
Modeling -
Outcome
Prediction
4
4© 2017 AnalyticalWizards INC. All rights reserved.

5. Macro-Level Patient Journey Mapping
Patient
demographics
Disease
progression
over time
Comorbid
conditions
Treating
physicians
Treatment by
line of therapy
• Switching /
Adjunct therapy
Medical tests
Managed
Care / IDN
affiliation
5© 2017 AnalyticalWizards INC. All rights reserved.

6. Target Physicians based on their real-world
treatment behavior
Identify Brand
Loyalists
Predict potential
Brand Loyalists
Identify Competitive
Loyalists and
potential defectors
Advanced modeling
to uncover factors
influencing brand
loyalty
• Physician demographics
• Patient characteristics
• Geographic variations
• MCO/IDN Influences
6© 2017 AnalyticalWizards INC. All rights reserved.

7. Market Sizing and Forecasting
Real World
Evidence-based
market sizing
using identifiable
patient cohorts
Demographic
and treatment
trends shaping
likely future
prescribing levels
Analog
Identification
7© 2017 AnalyticalWizards INC. All rights reserved.

8. Advanced Modeling - Outcome Prediction
Prediction of Positive and
Negative Outcomes
Characteristics
that determine
brand choice /
switching
Identification of
key variables
• Patient Demographics
• Treatments
• Timing
8© 2017 AnalyticalWizards INC. All rights reserved.

9. Patient Profiling and Targeting
Understand patient
treatment behavior
Identify unmet needs
and under-served
patient segments
Extract medical history
for targeted patient
cohort
Differentiate between
patients adhering to
your brand vs
competitor’s brand
9© 2017 AnalyticalWizards INC. All rights reserved.

10. Inform Clinical Trial Design
Design Test-
Control
analysis
based on
real world
data
Develop new
trial designs
Align study
constraints
to real world
clinical
pathways
Identify good
and bad
outcomes
from
comparative
studies
Identify
points of
intervention
in treatment
10© 2017 AnalyticalWizards INC. All rights reserved.

11. Pharma Data Assets Used by AW
Type of Data
• Sales
• Prescription data
• Claims data
• APLD Datasets
• Promotional data (Calls,
Details, Vouchers,
Samples
• Formulary Data
• Internal Data
• EMR Data
• Specialty Pharmacy Data
• A&P (TV, Print, Digital,
Copay, OOH)
Providers of Data
• IMS Health (Midas,
Plantrak, Exponent, DDD,
NSP, HCOS
• SHA (WK)
• Fingertip
• Medimedia
• Cegedim
• Epocrates
• Optum, Truven
• Media Agencies/Others –
MediaVest, FCB,
AccessMed, McKesson
Type of Aggregation
• Patient level
• Physician level
• Account level
• Payer level
• Sales Territory/Regional
level
• Brick level
Levels of Data
• National
• Sub National
• Physician / Customer level
• Channel Level
• DMA level
11© 2017 AnalyticalWizards INC. All rights reserved.

12. AW has the cutting edge tools for Rapid Intelligent
Automated Data Selection and Cleaning
•Faster than SAS
•Impossible to do on laptops
(which have insufficient
computing power for processing
TBs of data)
•Approximately 1 day turnaround
•Queries run in seconds (other
platforms will take hours or days
to run the same query)
•Vectorized data analysis – Data
treated as Matrices rather than
rows, making processing much
faster than SAS
•R is open-source, much lower
cost than proprietary packages
like SAS
•Faster machine-learning
capabilities
12© 2017 AnalyticalWizards INC. All rights reserved.
Google
BigQuery
Cloud-based
technology
Cloud-based R Studio
Automated querying

13. Intelligent Big Data Processing
~ 6.8 Billion
Records
>100 Million Patients
~ 68k Diagnosis
Raw Claims Data
Medical
Diagnosis
Lab
Results
Medical
Procedures
Drugs
Limit the Scope
- Identify known diagnosis,
associated drugs and medical
procedures
- Filter for the relevant patient
cohorts
- Keep the most appropriate
features from patient history
Build Disease Progressions
13© 2017 AnalyticalWizards INC. All rights reserved.

14. Steps / Process
1. Identify Data
Sources for Analysis
• Sales
• Prescription data
• Claims data
• APLD Datasets
• Promotional data (Calls, Details,
Vouchers, Samples
• Formulary Data
• Internal Data
• EMR Data
• Specialty Pharmacy Data
• A&P (TV, Print, Digital, Copay, OOH
2. Fast Data Transfer to
Cloud Processing &
Analysis Environment
• Raw files transferred from
client infrastructure to cloud
using proprietary Replicant™
fast file transfer
• Files quality-checked for
readability completeness, etc.
• Files loaded into Google
BigQuery RDBMS
3. Data Selection for
Cleaning and
Preliminary Analysis
• Client market definitions
• Desk research
• Iterative Data mining
4. Final Analyses and
Model Building
• Descriptive Statistics
• Advanced Model Building
14© 2017 AnalyticalWizards INC. All rights reserved.

15. Rapid Intelligent Automated Data Selection and
Cleaning
1. Research Disease
Category (client definition
and/or desk research)
• Identify related Drugs (NDC Rxs)
• Identify relevant medical procedures
(JCODEs)
• Identify relevant patient diagnoses
•Some patients are diagnosed but not
treated, don’t continue journey
2. Extract patient cohort for
study (SQL)
• Criteria based on Drugs taken, Medical
procedures, and/or Diagnosis codes
previously identified
• Patient Cohort definition refined via
iterative analysis
•Data analysis surfaces additional patient
characteristics and treatments
3. Define a relevant study
period (6 months to 3+ years)
to identify complete patient
records
• Remove patients with partial/intermittent
coverage
• Final dataset contains complete patient
records
15© 2017 AnalyticalWizards INC. All rights reserved.

16. Claims Data Analysis Refinement and Discovery
1. Clean Patient
Cohort
•Refinement and
discovery of relevant
patient characteristics
and treatment
2. Examine ICD9/10 codes to Discover Most
Common Diagnoses for Patient Cohort
• Identify new JCODEs, ICD9/10, etc. based on what the data
reveals
• Use data to identify comorbidities
•JCODEs
•ICD9/10
•DRG
•CPT4
•NDC (other drugs)
• Some patients may not have ICD9 Dx, but are considered
relevant due to Rxs or medical procedures
•Lab Results
3. Build Patient Profiles
with Timelines for Disease
Progression
•Based on patient characteristics,
medical history, and comorbidities
•Develop predictive models for
disease diagnosis and/or disease
progression
16© 2017 AnalyticalWizards INC. All rights reserved.

17. Contact
Eric Levin, Senior Director
elevin@analyticalwizards.com
609-509-7277
Ram Sharma, Managing Director
rsharma@analyticalwizards.com
973-994-2270