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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
1
Introduce what has changed in approach, data, and analytics
2
Review some new use cases for patient journey
3
Redefine patient journey definition
4
Conclusions and Questions
5
2
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
3
Patient Journey Analytics Front and Center
4
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
5
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
6
(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
8
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
12
Could an expanded patient journey analytics
be leveraged?
Quantitative Patient Journey
13
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
15
• 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
16
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
17
NEW USE CASE #5: Analyzing Access as Part of the Patient Journey
18
• 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
19
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
21
Special thanks to:• RxDataScience• Analytical Wizards• Symphony Health• Sanofi’s Advanced Analytics CoE