New Applications and Understanding of the Patient Journey Through Real World AnalyticsPrepared by: Sandy D. Balkin, Ph.D.

Presented: September 21, 2017

Presentation Objectives

Review of traditional patient journey

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Introduce what has changed in approach, data, and analytics

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Review some new use cases for patient journey

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Redefine patient journey definition

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Conclusions and Questions

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Introduction

• In era of patient-centricity, pharma companies are exploring various sources of data (big and small) describing the patient experience to help inform decisions around research and commercialization

• Patient journeys have long be used to inform sales and marketing strategies, however, the renaissance of claims data availability and modern database platforms has dramatically altered both how they are constructed and the questions they can answer

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Patient Journey Analytics Front and Center

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Traditional View of Patient Journey• The patient journey is a description of the typical patient’s experience of a condition from early

awareness through cure, partial resolution or death, which illuminates decisions faced and emotions encountered

• While individual patients have unique courses, understanding similarities in patient journeys for a single disease can help inform many stakeholders key treatment decision points about what it is like to live with a condition

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Traditional Use Case

Pharma marketing interested in understanding patients’ experiences as background to understand why they receive or prefer certain medications over others and to determine key promotional

opportunities

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(Qual) Where are the potential leverage points that can be used to influence usage of our drug along the treatment journey?

(Quant) What is the size of these leverage points?

HCP/PCP consult/Dx

SpecialtyConsult/Dx

Initial symptoms

Try OTC, Avoidance

Acceptance, develops coping

strategies

Worsens or recurs

Worsens or recurs

Initial Treatment (OTC or Rx)

Ongoing Treatment /

Management (OTC +- Rx)

(Referral)

(Not diagnosed and very satisfied with OTC)

Traditional Patient Journey Mapping

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Evaluation Criteria Chart Pulls Patient Interviews

Number of Patients Analyzed:

Surveys and interviews are always of small sample sizes

~300 ~30

Validity:

Patient memory over time and definition of a “typical” or “last patient” very variable; with EMR the HCP often has to get IT staff involved in the chart pulls

Challenging Very Challenging

Projection of the Results:

Projecting the results of chart pulls is a challenge as high decile physicians are often sparsely underrepresented in on-line panels

Difficult Near Impossible

Accuracy of the Data Collected:

Subject to same variability of any survey, panel or interview based research

Reliant on HCP’s ability to define typical

patient

Reliant on recall of a non-professional

Patient recall or HCP Chart Reviews

Robust construction required many different patients to be

gathered and analyzed

Typically created from scratch

What Has Changed to Facilitate New Use Cases?

• Patient level data has become more available, consistent and available:• Longitudinal Medical Claims (Open / Closed)

• Electronic Medical Records

• Social Media

• Genomics

• Computational architecture allows for efficient and inexpensive data staging and access

• Machine Learning algorithms to identify features and relationships that are not readily apparent

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Anonymous Patient Level Data

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Closed Claims Data Sources• Typically derived from health plan provided data

• Patient centric data:• We know when the patient was and was not part of the database

(i.e., when the patient enrolled and when he/she ended her enrollment)

• Tends to capture complete information (all claims) for the patients while they were part of the databases regardless of setting (outpatient services, outpatient pharmacy, inpatient)

• Typically puts limitations on ability to associate physicians, payers, and/or geographies with the claims

• Suppliers• Truven – Leverages information from many health plans

• Optum – Part of United Healthcare Group (tends to have better lab data than Truven)

• Pros/Cons• Ability to know when patients are part of database makes closed

databases better than open databases for HEOR type studies

• Lack of ability to associate physicians, payers and/or geographies with the data makes them less suitable when trying to inform targeting of physicians and/or prayer

Open Claims Data Sources• Typically captures data during claims processing (e.g., the

switch) and can even capture rejected and reversed claims

• Physician/providers and/or pharmacy centric• Picks up all the information from a practice if the claims processing

flows through a contracted supplier of the data

• Tends to capture either nearly all or only a limited amount of information for any specific physician and/or pharmacy

• Will have missing information for patients if some of the patient care is through claims that are to going through contracted suppliers

• Hard to “know what we don’t know” about patients

• Suppliers• IMS Lifelink

• Symphony Health Solutions

• Decision Resourced Group

• Pros/Cons• Qualification of patients for HEOR type studies can be challenging as

it is hard to know what information might be missing

• Ability to link the data to specific physicians and/or payers makes them useful for targeting purposes

Sanofi’s Computational Platform is a “Game Changer”• Standard model for commercial analytics is a

client-server arrangement running SAS against a datamart or EDW

• Cloud-based Redshift + Spark + R combination allows enormous jobs to run very quickly, efficiently and inexpensively without requiring investment in large computational infrastructure

• Evaluation of RxDataScience’s Kx based platform

New Data Elements Allow for Commercial Value Analytics Focusing on the Patient

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Contra-Indications

Misdiagnoses

No treatment prescribed

No drug treatment prescribed

Patient cannot get drug treatment

Patient cannot get prescribed drug treatment

Patient does not have adequate insurance coverage

Patient does not fill drug therapy

Patient does not take drug as prescribed

Patient does not stay on therapy

Physician-Level Variables Patient-Level Variables

Background for Examples

Key Issue• Indication: as an adjunct to diet and

maximally tolerated statin therapy for the treatment of adults with heterozygous familial hypercholesterolemia or clinical atherosclerotic cardiovascular disease who require additional lowering of LDL-C

• Cost: $13,000+ annually. Concern remains about the cost to be borne by patients, insurance, and the public

Amgen Press Release on PCSK-9 Rejection Rates

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Could an expanded patient journey analytics

be leveraged?

Quantitative Patient Journey

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Leveraging patient claims data, we can now track patient usage within a

therapeutic class using a large sample size and high precision from

beginning to end

Source: RxDataScience

New Use Case #1: Quantitative Patient Journey and Patient Source

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Specify starting point for journey and map where they go

Source: RxDataScience

Specify ending point for journey and see where they start

New Use Case #2:Feature Identification of Treated Patients

Optimal - Less than 100 mg/dLNear Optimal, above optimal - 100-

129 mg/dLBorderline High - 130-159 High - 160 -189 mg/dL Very High - Greater than 189 mg/dL

No Indication 1.00 1.07 1.34 2.24 5.54

Pure Hypercholesterolemia 1.49 1.68 2.16 3.28 7.80

Clinical_ASCVD 2.24 2.85 4.61 6.04 11.35

0.0

2.0

4.0

6.0

8.0

10.0

12.0

Od

ds

Ra

tio

Odds Ratios for Combinations of Presence of indication and various LDL levels

AHA/ACC Guidelines Includes PCSK9s as

preferred agents for patients not at goal on high

dose statins

PCSK9s part of AHA/ACC third line recommendation

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• RX History• Diagnosis history• Lab value history (LDL and Trigs)

• Analysis suggests LDL levels in conjunction with RX history most important in obtaining insurance coveragage

New Use Case #3:Predictive Patient Finder using Look-A-Like Modeling

• Develop predictive model using a training dataset in order to identify patients who share key characteristics with patients who obtained PCSK-9 reimbursement successfully, but have not attempted to do so as of yet

• Score all patients in claims database to assess probability of being able to successfully obtain reimbursement for a PCSK-9 prescription

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Claims data diagnosis codes used

to classify known patients

Predictive models used to find

unknown patients and unidentified

treatment pathways

New Use Case #4: Impact of concomitant therapies on persistency• Sanofi published a paper suggesting patient

compliance critical to determining if high intensity statin therapy can truly be effective

• Use survival analysis to visualize and measure impact of patient persistency for patients concomitantly taking an anti-depressant

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NEW USE CASE #5: Analyzing Access as Part of the Patient Journey

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• Analysis of claims data suggests low approval rate and high out of pocket for PCSK-9 therapies• Data can be used to show prior therapies before successfully obtaining reimbursement for a PCSK-9• Data can be used to show what therapy is switched to when a PCSK-9 is rejected

Data provide by Symphony Health

New Use Case #6: Quantifying the Complete Ecosystem of the Patient Journey

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Critical to understand where patients will be getting medical advice…especially those that are outside span of control and may require mitigation

Next Gen Patient Journey Redefined

Focus on Complete Ecosystem of Patients, Payers and Providers in a world field with personal and non-personal HCP promotion, healthcare

networks, social networks, lawyers, outcomes studies, value based contracting, clinical practice guidelines and patient engagement

Thank YouSandy D. Balkin, Ph.D.

Sanofi US

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Special thanks to:• RxDataScience• Analytical Wizards• Symphony Health• Sanofi’s Advanced Analytics CoE