Leveraging analytics in a value based reimbursement world

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SCIO Health Analytics®

Leveraging Analytics in a Value-

Based Reimbursement World

October 17, 2016


MARKET DRIVERS


Provider Consolidation and Shifting Care

Traditional Fee for Service

Patient PCP

Hospital

Lab

Specialist

SNF



Traditional Fee for Service

Patient PCP

Hospital

Lab

Specialist

SNF

RegulationsTechnology Needs

PenaltiesComplex Patients



New Types of Organizations: ACOs, IDNs, CINs, PCMHs, and Others

Hospital

Primary Care Provider

Aftercare Provider

Clinic

PayviderSNF

Testing Center

Provider Groups

Home Care

Patient


How Plans Have Thought about Providers So Far

Analytics for Value Based Care

• New types of analytics are important under Value Based Care:

– Quality: How is the provider doing at closing gaps?

– Care Pathways/Bundles: Ensure providers render integrated care

– Enhanced Disease Management: Wellness Programs, Incentive Optimization

– Referral Patterns: Are providers referring to most efficient other providers (specialists, facilities, etc.)

Historic Analytics

Key Components of legacy thinking

• Unit Cost – Metrics focus on how a provider compares to others on various services

– Strategies to Control increases include : Contracting with OON providers, Payment Methodologies, Medical Management (Step Therapy, Lower Levels of Care)

• Utilization – Focus on items increasing faster than average and how to manage

– Medical Management, Pre-Certification, Formularies, Member Cost Shares


Impact of These Shifts

Customers

Consumers become money managers

Increased focus on wellness & prevention

Aging Populations with Chronic Conditions

Providers

New reimbursement models that shift risk

& favor value over volume

Changing organizational structures

(Hospitals employing physicians)

New Services (Personalized Medicine)

Shift care to lower intensity settings:

outpatient, nursing facilities and home

Changes create new risks for payment

errors, abuse, and fraud

Efficiency Quality Satisfaction

Employers Members


MANAGING COST & CARE ACROSS THE

CONTINUUM


Thriving in a Value-Based Reimbursement World

• Understanding How Individuals Vary in Impactability and Population Health Needs

Identifying Highest Risk Individuals (Illness and Cost)

• Utilizing Assessment and Targeting Analytics in Managing Finite Resources

Leveraging the Right Resources to Optimize Clinical

and Financial Results

• Engaging Providers in Managing and Optimizing Network Performance

Delivering Care in the Optimal Site of Service and Intensity of

Service

• Establishing Data Sharing and Transparency Collaborating with Key Stakeholders: Payers, Providers, & Patients

Challenge Analytic Needs


Using Analytics to Better Understand

1. Identify Outliers

2. Engage

3. Measure Results


The Foundation of Actionable Analytics: Robust Profiles

Behaviors & Attitudes

Demographic & Attribution

Clinical Factors

Cost & Quality

Utilization

Risk


Example of Personas – Member / Patient Analysis

HEALTHY & AFFLUENT

BALANCEDADULTS

HIGH UTILIZERSQUALITYDRIVEN

COSTCONSCIOUS

CHRONIC OLDER ADULTS

HIGH COST BABY

BOOMERS

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


POPULATION HEALTH ANALYTICS:

FINDING MEMBERS YOU CAN IMPACT


Precise Care: Higher Quality and Lower Costs

TOTAL POPULATION

ACTIONABLE MEMBERS


View Members Through the Lens of Impactability

Population

100%

Impactability Prospective Risk

Moderate Impactability

12% of Members

Low Impactability

75% of Members

High Impactability

12% of Members

High

Low

Op

po

rtun

ity

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


Allocate Resources Towards Impactable Conditions

Diabetes, $326,515,914

COPD, $56,262,780

Seizures, $20,743,606

Obesity, $53,401,848

Rheumatoid Arthritis, $47,819,586

Inflammatory Bowel, $13,258,925

Back Pain, $303,270,098

Depression, $158,480,248

Hyperlipidemia, $326,077,730

Asthma, $119,587,531 CHF, $69,839,071

Maternity, $69,955,753

CKD, $48,898,470

Parkinson Disease, $1,602,486

CAD, $112,816,271

Hypertension, $497,076,823

-10

-8

-6

-4

-2

0

2

4

6

8

10

-10.0 -8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0 10.0

Condition Intervention Summary

Harder to Impact

More Complex Interventions

Less Complex Interventions

Easier to Impact

High Volume Silent Diseases

High Volume Symptomatic Diseases

Maternity

Symptomatic Chronic Low Numbers

Diabetes, $497,076,823

Hypertension,

$326,515,914


Allocate Resources Towards Impactable Conditions

Diabetes, $326,515,914

COPD, $56,262,780

Seizures, $20,743,606

Obesity, $53,401,848

Rheumatoid Arthritis, $47,819,586

Inflammatory Bowel, $13,258,925

Back Pain, $303,270,098

Depression, $158,480,248

Hyperlipidemia, $326,077,730

Asthma, $119,587,531 CHF, $69,839,071

Maternity, $69,955,753

CKD, $48,898,470

Parkinson Disease, $1,602,486

CAD, $112,816,271

Hypertension, $497,076,823

-10

-8

-6

-4

-2

0

2

4

6

8

10

-10.0 -8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0 10.0

Condition Intervention Summary

Harder to Impact

More Complex Interventions

Less Complex Interventions

Easier to Impact

High Volume Silent Diseases

High Volume Symptomatic Diseases

Maternity

Symptomatic Chronic Low Numbers

Diabetes, $497,076,823

Hypertension,

$326,515,914

Optimal Interventions for Diabetes

1 Eye Exam

2 HbA1c

3 Lipid Test

4 Medication Regimen Compliance


Prioritize Care Gap Closure at an Individual Level

Member ID 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

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

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


Understanding Patient Risk with Analytics

1WHERE ARE HIGH RISK

PATIENTS?2

WHAT ARE THE PATIENT PERSONA TRENDS,

AND WHAT IS DRIVING THE RESULT?

3WHAT GAPS IN CARE ARE DRIVING

PATIENT COSTS?4

WHAT ARE MY QUALITY MEASURE GAPS?

WHERE DO WE AGREE ON RISK?


ANALYTICS TO STANDARDIZE CARE AROUND TOP

PERFORMERS & WATCH FOR ABERRANCIES


Network Analytics – The Need

Focus resources on areas to improve risk-

adjusted utilization against benchmarks in

key areas

Insight into performance on quality

measures

Drive improved efficiency (both

utilization & unit cost)

Demonstrate opportunities to

improve network leakage (based on hypothesis of why)


Case Study

Evaluation of Provider Quality and Efficiency – Advanced Analysis

EFFICIENCY RESULTS QUALITY RESULTS

OVERALL RESULTS

Provider Analytics: Attribution, Performance

Physician ID

Total Members

ObservedCost/

Member

Expected Cost/

Member

Efficiency(Observed/ Expected)

00001 53 $11,939 $10,450 1.14

00002 46 $7,987 $9,052 0.88

00003 42 $7,460 $9,925 0.75

00004 39 $14,218 $12,150 1.17

Physician ID

Total Members

ObservedGap

Expected Gap

Quality (Observed/ Expected)

00001 53 42% 47% 0.89

00002 46 47% 45% 1.04

00003 42 45% 48% 0.94

00004 39 45% 50% 0.90

Physician ID

Total Members

Efficiency(Observed/ Expected)

Quality (Observed/ Expected)

00001 53 1.14 0.89

00002 46 0.88 1.04

00003 42 0.75 0.94

00004 39 1.17 0.90

Best Combination of

Efficiency & Quality


The Squeeze is On: Increase in Outpatient Care

Chronic Condition Management

RAC Audits

Readmission Penalties

Inpatient Care

OutpatientCare


The Outpatient Challenge

Various Forces are Driving Treatment to Lower Levels of Care

Target

• Good/Better Outcomes for Patients

• Typically Lower Cost

• Increased patient satisfaction

COMBINE TO DRIVE EXCESSIVE OUTPATIENT UTILIZATION

External Forces

Coordinated Care Management,

Re-Admission Penalties

RAC Short Stay Audits.

Internal Strategies

Converting Buildings

Buying Ancillary Providers

Moving Care from Office to OP

As Inpatient services are shrinking, Hospitals are Providing more Services in

Outpatient Settings. They are also adjusting to Balance Revenue lost from

reduced inpatient stays


• “Improved” efficiency

• Unclear if this was a net improvement for the system as a whole

• Things not included in bonus might get more expensive

• Possible incentive to misrepresent the risk of their population

Unexpected Consequences of Value-Based Programs

Before Incentive

Par Doctor

Facility 1

Facility 2

$$$

$

After Incentive

Par Doctor

Facility 1

Facility 2

$$$

$

Concerns:


PAYER-PROVIDER COLLABORATION:

WORKING TOGETHER TOWARDS COMMON GOALS


Payer, Provider, and Patient Engagement

Am I compensated

correctly?

Do I have all the data I

need?

Am I receiving the best care?

Transparent

Data Sharing Supports

Care & Revenue Optimization


Analytics Driving Engagement Outreach


Small Improvements Carry Significant Revenue Implications

Calculation: $800 average pmpm payment x RAF

A 45,000 member health plan purchases Medicare risk adjustment

and HEDIS/Star/P4P monitoring analytics

Within 90 days their systems are online to support new suspecting and provider collaboration programs:

• Identification: A prioritized list of all patients that need to be seen by 12/31 to ensure care gaps are closed

and revenue streams remain constant

• Provider Collaboration: Each morning physicians receive patient-specific pre-populated forms containing

previously diagnosed conditions and any outstanding Stars measure assessments needed for that member.

PMPM Revenue

In two months, the health plan increases their average Medicare RAF score from .95 to .98 and sees significant

improvement on a number of clinical Star measures

$760

$784

$0 $100 $200 $300 $400 $500 $600 $700 $800 $900

.98 RAF

.95 RAF

Pre-Solution Revenue Post-Solution Revenue

$24


Small Improvements Carry Significant Revenue Implications

Calculation: $800 average pmpm payment x RAF

A 45,000 member health plan purchases Medicare risk adjustment

and HEDIS/Star/P4P monitoring analytics

Within 90 days their systems are online to support new suspecting and provider collaboration programs:

• Identification: A prioritized list of all patients that need to be seen by 12/31 to ensure care gaps are closed

and revenue streams remain constant

• Provider Collaboration: Each morning physicians receive patient-specific pre-populated forms containing

previously diagnosed conditions and any outstanding Stars measure assessments needed for that member.

PMPM Revenue

In two months, the health plan increases their average Medicare RAF score from .95 to .98 and sees significant

improvement on a number of clinical Star measures

$760

$784

$0 $100 $200 $300 $400 $500 $600 $700 $800 $900

.98 RAF

.95 RAF

Pre-Solution Revenue Post-Solution Revenue

$24

Provider

Reimbursement for many providers is based on % of revenue/premium

At 35% of premium, this example generates an additional provider revenue of

$4,500,000

Health Plan

An increase of just 0.03 to the RAF score generated an additional $24/member/month.

For a 45,000 member plan, this equates to an annual revenue increase of

$12,960,000


Our Vision for Analytics

Use new types of data and analytics to build robust views and models based

on enhanced member & provider profiles in order to:

• Better identify patterns of behavior that merit review• Prioritize limited resources• Enhance provider collaboration to improve shared results• Improve performance overall

Goal

Healthcare

Leveraging analytics in a value based reimbursement world