Is Your Data & Analytics Strategy Enabling You to Successfully Take on Risk Contracts?

| 1 ©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. ©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved.

Is Your Data & Analytics Strategy Enabling You to Successfully Take on Risk Contracts?

David Hom

Chief Evangelist

SCIO Health Analytics

Teresa Rueckert, RN, MSN

Clinical Solution Consultant

SCIO Health Analytics

| 2 ©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved.

THESIS: USE OF DATA TO CREATE AN ENHANCED

VIEW OF THE PATIENT/MEMBER FOR ENGAGEMENT

DATA MART

CLAIMS RX ELIGIBILITY SOCIAL

ECONOMIC LAB EMR FITNESS DEVICES

DATA MART USES CASES

What is The Total Cost of

Care By Patient and Provider

What Risk Will A Member

Have Without Claims Data

What Lab Data Will Enhance

The Prediction of

Financial Risk

With Claims, Data What is

The Financial Risk of The

Member

Ability to Predict Which

Members Will Be Compliant

What New Data Can Be Used

To Understand Compliance

What Data in The EMR

Enhances The Prediction of

Risk

Data Intuitiveness To Reduce

False Positives

How Often Should The Data

Be Updated

1

2

3

4

5

6

7

8

9


PROFILES AS AN ANALYTIC FOUNDATION

CLAIM, PATIENT

& PROVIDER PROFILES

Behaviors & Attitudes

Demographic & Attribution

Clinical Factors

Cost & Quality

Utilization

Risk


OBJECTIVE OF PATIENT PERSONAS: PREDICT

LIKELIHOOD FOR RISK WITH LITTLE DATA

PROVIDER

Integrate Multiple Data Sources:

Health de-identified medical claims/RX data, ZIP level

demographics, call center and outreach data , lab values, EMR

key notes, economic area data, fitness data, other data values

PREDICTIVE

ANALYTICS

PRESCRIPTIVE

ANALYTICS

PATIENT

Clinical and Financial

Risk Management of

Patient Populations

Provider Performance

Management for

Outcomes

Treatment Pathways

Individualized for each

Persona

Patient Ability to

Complete the Service

Eligibility Files

Which Capture

Demographic Data

Zip Code Data - 9 Digit

Zip Code and Household

Level Data Files


Healthy &

Affluent

Balanced

Adults High Utilizers

Quality

Driven

Cost

Conscious

Chronic older

Adults

High Cost

Baby Boomers

EXAMPLE OF PERSONAS – CLINICAL

No.of chronic

conditions

ER Paid PMPM

IP Paid PMPM

ER Utilization

IP Utilization

0.54 0.70 0.71

0.86 0.82

1.02 1.13

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


APPLYING PERSONA ANALYTICS

Consumer Types

• Healthy and Affluent

• Balanced Adults

• High Utilizers

• Quality Driven

• Cost Conscious

• Chronic Older Adults

• High Cost Baby Boomers

Viewing membership this

way helps organizations

develop, evaluate, and

market care management

programs to the most

appropriate members.


12%

68%

19%

1%

65+ 55-64

35-54 16-34

Age Group

27%

43%

22%

8%

$200k+

$100k - $200k

$50k - $100k

Less than $50k

Estimated Income

BALANCED ADULTS

Description

Well managed and balanced middle age adults. Mostly female, predominately with college level education

and white collar employees with medium income. They are a healthy population with low utilization and

PMPM cost which is aligned to average number of chronic conditions.

Intervention: Low risk and well managed individuals, so no need to spend high value resources.

Demographic Attributes

% Above Poverty Level 94%

% Blue Collar Employed 14%

% Single Family Dwelling 73%

% Household with children 30%

Clinical Attributes

IP Utilization 0.05

ER Utilization 0.22

# Average Chronic Conditions 0.69

Paid Amount PMPM ER $9

Paid Amount PMPM IP $72

Gender Education

School College

Individuals

with

Income

Level

> $50K

Median Age

48

$285K

73%

Frequent

Spending

Median Home Value

Socio-Economic Score

90

70 100

Spending

Pattern

Low Risk

Median Risk

Prospective

Score

56


LEVERAGE NON-CLINICAL DATA TO IDENTIFY

HIGH-RISK

CommunityArea

NameLowBirthWeight

PrenatalCareBeginning

inFirstTrimester

TeenBirth

Rate

Below

Poverty

Level

NoHigh

School

Diploma

PerCapita

IncomeUnemployment

AvalonPark 19.7 74.5 63.9 16.7 13.3 $23,495 16.6

WashingtonHeights 19.6 75.4 65 15.7 15.6 $19,709 18.3

WashingtonPark 17.7 74.9 82.6 39.1 28.3 $13,087 23.2

EastGarfieldPark 17.5 73.2 93.2 39.7 26.2 $13,596 16.4

Woodlawn 17.4 73.3 51.1 28.3 17.9 $18,928 17.3

FullerPark 17.1 71.4 69.2 55.5 33.7 $9,016 40

WestGarfieldPark 17 71.4 114.9 40.3 26.2 $10,951 25.2

WestEnglewood 16.1 63.6 116.9 32.3 30.3 $10,559 34.7

Austin 15.4 72.9 81.8 27 25 $15,920 21

Chatham 15.4 72.5 68.2 25.3 13.7 $20,320 19

NorthLawndale 15.3 75.8 108.9 38.6 30.4 $12,548 18.5

Riverdale 15.3 74.1 64.5 61.4 24.6 $8,535 26.4

LincolnPark 6.6 75.7 2.1 11.8 4.3 $71,403 4.5

JeffersonPark 6.6 82.9 28.6 6.4 13.5 $27,280 9

EastSide 6.4 73.3 53 18.7 35.5 $15,347 14.5

LakeView 6.3 79.1 15.8 10.5 2.9 $58,227 4.7

HydePark 5.9 80.3 7.6 18.2 5.3 $39,243 6.9

Loop 5.3 78.2 1.3 11.1 3.4 $67,699 4.2

NorwoodPark 4.9 89.4 3.4 5.9 13.5 $31,659 7.3

Beverly 4.9 84.8 11.9 5.2 5.1 $40,107 7.8

WestElsdon 4.6 81.2 45.5 9.8 39.6 $16,938 13.5

LowerWestSide 4.5 80.8 49 27.2 44.3 $15,467 13

O'Hare 3.5 82 15.9 9.5 11 $29,402 4.7


NEW DATA SETS ENHANCEMENTS

CLEARER VIEW OF THE PATIENT FINANCIAL RISK BUT FOCUS ON:

• Avoidable IP and ER visits

• Extended length of stay impact

• Readmissions

• Excessive testing and specialist care

OUTCOMES

• Improved prediction at the patient level

• Recommended prescriptive outputs by patient

Claims Data Rx Data Community

Data CDC Data Lab Data EMR Data

Eligibility

Data

Zip Code

Data


IMPACTABLE POPULATION ANALYSIS

Population

100%

Impactability Prospective Risk

Moderate Impactability

12% of Members

Low Impactability

75% of Members

High Impactability

12% of Members

High

Low

Opportu

nity

Goal

Close Gaps and

Steerage to

Managed

Networks

Close Gaps

and Steerage

to Managed

Networks

Manage High

Costs and

Risk Factors

Manage

High Costs

High Risk

10%

Moderate Risk

1.5%

Low Risk

0.5%

High Risk

8%

Moderate Risk

3%

Low Risk

1%

High Risk

13.5%

Moderate Risk

27%

Low Risk

34.5%

High Cost

1% of Members


VALUE-BASED CONTRACTING CONTINUUM

Measurement Systems

• PMPM

• Hospital events/1000

• HEDIS compliance metrics

• Readmissions

• Comorbidities

Finalize Pay for Outcomes Metrics

Provider Scorecards

• PCP and Specialist Attribution

• HEDIS metrics

• CMS quality metrics

Member ID

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

Difference Impactability

Score # Hospitalization

# ER

Visits

In Patient

(PMPM)

ER

(PMPM)

Out

Patient

(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

Member ID Risk Score Impactability Score Gap1 Gap2 Gap3

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

000000010331 0.83 1.51 Lipid Panel Spirometry RX

000000010043 0.81 1.64 Consider Pulmonary Rehabilitation AST Test Physical Therapy

Identify What Gap Should be Prioritized at the Member Level


ANALYTIC INSIGHTS


ANALYTIC INSIGHTS

High-Level View of Greatest Impact

By Cost %

Risk

Bucket

Impactability Bucket

High Medium Mild Low

High 14.6% 6.3% 9.2% 48.5%

Medium 0.4% 0.4% 0.3% 13.0%

Mild 0.0% 0.1% 0.1% 5.8%

Low 0.0% 0.0% 1.5%

Potential

Savings

Risk

Bucket

Impactability Bucket

High Medium Mild Low

High $1,363,086 $69,264 $16,162 $136

Medium $80,984 $37,529 $5,558 $81

Mild $20,040 $12,580 $2,865 $67

Low $746 $417 $0

2015

Risk Score

2015

Risk Score Where to Find High Opportunity Members

Identify highly impactable

populations who will deliver

greatest savings


Identify specific gaps with the highest savings

potential / impactablity

ANALYTIC INSIGHTS

Drill Down to Member/Patient Level

DRILLDOWN

DRILLDOWN


Viewing the relationship

between cost, condition and

impactability to help in

planning programs

ANALYTIC INSIGHTS

B

Y

T

O

T

A

L

C

O

S

T

Condition Level Analysis


Understand where to focus

resources and outreach

based on identifying gaps

with greatest potential

savings

ANALYTIC INSIGHTS

T

O

P

1

0

G

A

P

S

O

P

E

N

DRILLDOWN

Gap Analysis


CASE STUDIES

Stratifying The Opioid Patient

&

Identifying The Untreated Patient


OPIOID USE & IMPACT ON TOTAL COST OF CARE

Sample Population Profile 2014–2015

• Average risk for general patient population is 1.00

• Average risk for Opioid patient population is double to 2.00

• This forecasts that patients to spend 100% more in the next 12 months

• Average risk for this comorbid patient population is significantly higher @ 3.5!

• This forecasts that patients will spend 350% more in the next 12 months


DIABETES

Compliance Summary for High Value Services


SUMMARY

The New World of Analytics

Eligibility

Data

Zip Code

Data

RISK

Health Care

Services

CLAIMS DATA

EMR Data

Lab Values

Medical Data

Rx Data

Impactabilty

Model

Surgical Models

• Internet of Things (IOT)

• Wearables

• Weather patterns

• Gross Domestic Product

(GDP)

• Provider clinical data from

devices

• etc.,

NEW DATA SETS

Intevenability

Models of

Do–ability

Propensity to

Consume

Is Your Data & Analytics Strategy Enabling You to Successfully Take on Risk Contracts?

Healthcare

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