©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |1

Once We Understand, Change Results.

Patient (Persona) Analytics A Catalyst to Driving Engagement and Compliance

for Risk based Outcomes Contracting

October 5, 2016

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MARKET DRIVERS

DRUG PRICING PRESSURE

MARKET DRIVERS

COMPLEXITY OF HEALTHCARE

VALUE-BASED CARE

MARKET STRATEGIES

NEW BUSINESS MODELS

PAYVIDERS

OUTCOMES BASED CONTRACTING

COLLABORATION

RISK & DATA SHARING FOR BETTER

PATIENT OUTCOMES

PATIENT ENGAGEMENT &

CONSUMERISM

INSIGHTS & TECHNOLOGY AS

A KEY ENABLER

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Potential Headwinds For Life Sciences

HEALTH PLANS WILL REQUIRE OUTCOMES BASED RISK STRATEGY

SPECIALTY DRUG COST IMPACT ON OVERALL HEALTH CARE SPEND

IMPACT OF MEDICATION COMPLIANCE ON TOTAL COST OF CARE

IMPACT ON THE GROWTH OF MANAGED MEDICARE AND MEDICAID

DISPARATE AND SILOED DATA

PATIENT BEHAVIOR IS CHANGING- CONSUMER AS THE NEW MONEY MANAGER

PAYER“ ARE REQUIRING MORE RI“K ON DRUG IMPACT AND EVIDENCE OF PATIENT “UCCE““

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360o ANALYSIS MATTERS

Singular View

SCIO

Comprehensive view

Vs.

IMPACT FOCUSMeasure and predict areas for

greatest impact and

identification of untreated

patients.

RESOURCE FOCUSFocus resources on contracting

payer collaboration and

commercial operation

resources

3600 FOCUSIncorporate multiple data

sources to determine

behaviors leading to

compliance, engagement and

risk reduction.

SCIO believes in bringing insights to light through a comprehensive lens

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Patient

Approach to Value: Patient Persona’s

PROVIDER

360° ANALYSIS

Prescriptive

Analytics

Predictive

Analytics

OU

TC

OM

ES

Da

ta F

low

Dynamic Risk Management of

Patient Populations

Provider Performance

Management

Treatment Pathways

Individualized for each Persona

Patient Migration Towards

Greater Compliance

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72%

14%

4%1%

$200K+

$100K - $200K

$50K - $100K

Less than $50K

Estimated Income

Patient Persona example: High Utilizers

23%

58%

12%7%

65+ 55-64

35-54 16-34

Age Group

Description

High-risk adults, mostly non- college level education, blue collar employees with average income. They happen to be high utilizers of healthcare services given that they are higher risk and the average chronic conditions is greater than one.

Intervention: High risk with high utilization, so need to education HCP to ensure steerage

Demographic Attributes

% Above Poverty Level 68%

% Blue Collar Employed 36%

% Single Family Dwelling 33%

% Married 48%

% Household with children 60%

Utilization Attributes

IP Utilization 1.45

ER Utilization 0.84

# Average Chronic Conditions 2.05

Paid Amount PMPM ER $95

Paid Amount PMPM IP $247

Gender Education

School College

Individuals with

Income Level

> $50K

Median Age

43

$146K

72%

HCP Engagement

AverageSpending

Median Home Value

Socio-Economic Score

62

Spending Pattern

60 100

Low Risk Median Risk

Prospective Score

0.71

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Healthy &

Affluent

Balanced

AdultsHigh Utilizers Quality Driven Cost Conscious

Chronic older

Adults

High Cost Baby

Boomers

Example of Personas – Clinical Attributes

No.of chronic conditions

ER Paid PMPM

IP Paid PMPM

ER Utilization

IP Utilization

0.540.70 0.71

0.86 0.82

1.021.13

Median Risk Prospective

Score

0.6 0.7 0.8 1.2 1.2 1.3 1.6

0.09 0.05 0.10 0.04 0.07 0.08 0.09

0.25 0.22 0.34 0.23 0.18 0.21 0.23

$75 $73 $147 $54 $75 $118 $248

$10 $9 $14 $9 $7 $10 $11

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THE OPIOID CHALLENGE

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• Market Potential is up to 6x the current number of patients on Opioid Dependence therapy. 80% of which are non compliant.

Patients are stratified by compliance and prospective risk

(10) Highest prospective risk patients (e.g. decile 10) consume 75% of healthcare spend

Each year prospective risks shifts patients between each decile group (e.g. patients health improves resulting in lower

risk while other patients become more sick)

Existing and Untreated Patients by Opioid Dependence Compliance and Risk

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Example of Prospective Risk by CBSA (Core Based Statistical Area)

Top 5 CBSAs

% of US OpioidDependence Rx

New York 4.7%

Philadelphia 3.7%

Pittsburgh 3.6%

Boston 2.6%

Detroit 2.3%

High=top 20% Middle=middle 30% Bottom=bottom 50%

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UNDIAGNOSED DIABETES PATIENT CHALLENGE

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Patients are stratified by compliance and prospective risk

(10) Highest prospective risk patients (e.g. decile 10) consume 75% of healthcare spend

Each year prospective risks shifts patients between each decile group (e.g. patients health improves resulting in lower

risk while other patients become more sick)

Patients by Diabetes Groups, Compliance and Risk

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Patients at Risk for Diabetes by Risk Category

-

20

40

60

80

100

120

140

10 9 8 7 6 5 4 3 2 1

Hospitalizationsper1000

RiskDecile

Hospitalizationsper1000

-

20

40

60

80

100

120

140

160

180

200

10 9 8 7 6 5 4 3 2 1

ERVisitsper1000

RiskDecile

ERVisitsper1000

10 – High Risk…..1 – Low Risk

Undiagnosed Diabetic Patients

Prospective Risk: 9ER/1000: 15IP/1000: 24Impactability: 7Avg. #CC: 2.67

Rank across all Counties

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Identify Gap Prioritization at the Patient Level

Patient Risk Score Impactability Score Gap1 Gap2 Gap3

000000010506 0.89 1.68 Diabetes - Consider Foot Exam HbA1c Less Than 7 Target

000000010331 0.83 1.51 Lipid Panel Spirometry

000000010043 0.81 1.64 Consider Pulmonary Rehabilitation AST Test Physical Therapy

000000010154 0.73 1.39 Lipid Panel Spirometry Alpha-Glucosidase

000000010539 0.73 1.04Diabetes and Macroalbuminuria - Consider

Adding an ACE Inhibitor or ARB

Diabetics 50 years and Older - Consider

Screening for Peripheral Arterial Disease

Patient

In Last 12 Months Cost Incurred in Last 12 Months Probability

of ER

Admission

Predicted

Probability of ER

Admit IF all the

gaps are closed

DifferenceImpactability

Score#

Hospitalization

# ER

Visits

InPatient

(PMPM)

ER

(PMPM)

OutPatient

(PMPM)

Professional

(PMPM)

Pharmacy

(PMPM)

000000010506 1 1 $2,999 $302 $209 $201 $130 93% 22% 71% 1.68

000000010331 0 0 $237 $158 $147 90% 27% 64% 1.51

000000010043 0 2 $287 $231 $225 $133 91% 22% 69% 1.64

000000010154 0 0 $231 $178 $103 74% 16% 58% 1.39

000000010539 0 0 $340 $181 $96 70% 27% 44% 1.04

000000010507 0 0 $333 $208 $134 73% 24% 49% 1.15

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Utilize Benchmarking to Identify Patterns of

Potential Utilization and Waste

Drill into High Risk Patients to

Uncover Impactability Score

Measure Impact of Non Compliance on HEDIS

Star Ratings for Managed Medicare and

Medicaid Plans Using Specific Products

Identify Geographies at Highest Risk Across

Commercial, Managed Medicare and Medicaid

Ar ed With A “i gle “heet of Music Part er with Payers on Risk Based Outcomes

Ide tify the Geographic Hot “pots a d Draft Care Plan Messages for HCPS and Associated Health Plans

Understand the Compliant /

Non-compliant Patient Persona

Build Capabilities to Measure

Outcomes and Pay for Value

Evaluate Impact on Hospital Readmissions

and Bundled Payments

Track Results with Providers and

Health Plan Care Managers

Track Utilization of Medication on Impact

to Other High Cost Chronic Conditions

Which Comorbidity Increases

Patient Prospective Risk

Summary: Persona Analytics Enabled Response to Headwinds