integrating analytics for value-based healthcare

Integrating Analytics for Value-Based Healthcare

Joshua McHale

Maury DePalo

22

Today’s Topic …

Population Health Analytics

• Current Climate and Challenges - Program Objectives

• Driving Needs for Focused Analytics - to Improve Clinical and Financial Performance

• Mobilizing by Process Implementation & Improvement

Data Integration Challenges

• Enterprise Data Architecture

• Primary Components – Data Sourcing, Transformation, Delivery

• End-User Data Navigation Model – Integrated Data Repository

Business Intelligence and Data Exploration

• Empowering the End-User Experience – Examples

• Action-Oriented Performance Measures

Moving Forward

• Risk and Alternative Quality Contracts

• Reducing Clinical Variation

• Mobilizing for Value-Based Care

Q & A

33

Population Health Analytics

Current Business Climate & Challenges

• Relentless Pressures on Quality Outcomes Costs

• Need for Innovation in Care Delivery, Coordination, Risk Sharing, Cost Control

• Massachusetts Health Policy Commission – Phase 2 of Community Health Acceleration, Revitalization, and Transformation investment program (CHART-2)

• Enhance delivery of efficient, effective care at community hospitals

• Promote care coordination, integration, delivery transformation

• Advance EHR adoption information exchange among providers

• Increase adoption of alternative payment models accountable care organizations

• Enhance patient safety coordination between hospitals and community-based providers

• Leveraging resources of community partners

• Focus on Pressing Healthcare Needs in Local Communities

• Targeted At-Risk Populations Behavioral Health; Diabetes

• High Utilizers > 4 Inpatient Admissions; > 10 Emergency Room Visits

• Improve Coordination and Access to Care

• Improve engagement of high-risk diabetes patients incorporate into care management programs development of disease management registries

44

Population Health Analytics

Driving Needs for Focused Analytics to Improve Clinical & Financial Performance

• Identify & Characterize Key Targeted Patient Populations

• At Risk Populations, Preventive Care, Patient Experience

• Identify Targets for Intervention & Improvement Programs

• Patient, Diagnosis

• Provider, Service Utilization, Care Coordination

• Track Quality and Financial Performance Metrics Against Baselines & Targets

• Individual and Aggregate Measures – Drill-Down on Quality & Financial Performance

• Individual Patients – Individual Providers

• Aggregate Providers – Aggregate Care Teams – Aggregate Practice Groups / Locations

• Track Attributed Populations Defined Under Risk Performance Contracts

• Track Response to Programs – Defined Quality Measures

• Track Assignment Consistency – Care Plan Compliance

• Disseminate Standards of Care Across Care Teams & Settings

• Track Variation in Outcomes, Utilization, Costs

55

Population Health Analytics

Integrating Care Planning – Execution on Focused Patient Cohorts

•Examine Performance Contracts – Patient Mix, Service Mix

•Establish Baseline Measures and Set Patient Goals

•Begin Care Plan Activities

•Monitor Adherence according to Care Plan & Schedule

•Track Quality and Financial Metrics

•Measure Results of Individual Patients & Evaluate Impact of Program on Overall Population

•Adjust Accordingly & Schedule Follow-ups

•Design Care Plans & Interventions – Activities, Observations & Measures

•Assign Patients to Tailored Care Plans Consistent with Goals for Overall Population

• Identify Partner Providers for Outreach and Coordinating Care

• Identify Patients Targeted for Intervention - Define Cohorts

• Stratify Patients Based on Clinical or Financial Risk or Operational Resource Demands

•Target Specific InterventionsIdentify

& Stratify

Design &

Assign

Execute &

Monitor

Evaluate &

Adjust

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Achieving Value-Based Accountable Care

Pursuing a Staged Implementation – Success Factors at Each Stage

Patient Panel

Definition

Targeted

Populations &

Outcomes

Baseline

Expenditures

& Costs

Accountability

Models

Financial

Reconciliation

Population

Health

Management

Identify Unique Patients

Assemble Records of Clinical Care

Define Bundles

Identify Unique Providers

Align Patients & Providers

Measure / Manage Care Delivery

Measure / Manage Care Relationships

Patient Panel Analytics

Defined Patients, Beneficiaries or Members

Segmentation

Outcomes: Clinical, Operational, Financial

Identify ACO Parties & Roles

Performance Targets & Metrics

Targeted Care Plans

EBM Guidelines for Required Care for Patient Needs

Historical Baselines

Align Patient with Provider Entity

Align Provider with ACO Entity

Calculate Service Fees & Savings Targets

Hierarchical Segmentation & Aggregation

Anticipated Services, Charges & Costs

Collaborative Care Delivery Models

Transitions in Care

Communications, Handoffs, Follow-ups

Contracts, Roles, Responsibilities

Shared Metrics, Benefits & Risks

Retrospective Payments

Shared Savings & Costs

Value Realization

Allocated Gains (Losses)

Billing & Payment Distribution

Compliance & Adherence Targets

Patient Stratification

Comparative Outcomes & Quality Metrics

Prospective & Bundled Payment Models

Predictive Risk Modeling

Performance Optimization

Market Share & Competitive Analytics

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Population Health Analytics

Health Systems Need to Know …

How do we manage patient cohorts more systematically? How do we better

integrate and focus our care delivery across these populations & care settings?

Population Health

Management

Do we understand our charges, payments and costs? Are we reconciling these with

our care plans and our accountability models? Financial Reconciliation

How do we implement & measure accountability across our ACO partnerships?

Where and by whom are value and costs introduced into our delivery processes?Accountability Models

What are our baseline expenditures & care delivery costs on these targets, with this

payer? How do these align with our contract terms across payer types?

Baseline Expenditures

& Costs

What are our current targets for patients & outcomes? What quality / results are

we seeing? Are they consistent? Where do we see under- or over-performance?

Targeted Populations

& Outcomes

Who are our patients? What treatments are they receiving? What other providers

are they seeing? At what locations? With what frequency?

Patient Panel

Definition

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Data Integration Challenges

We Need Visibility Into …

- Demographics- History

- Reported Outcomes- Location

- Specialty- Relationships

- Location- Care Team

- Structure- Locations

- Legal Entity- Contracts-Care Mgmt

Teams

- Inpatient- Outpatient- Pharmacy

- Beneficiary History- Payers

- Charges, Payments & Adjustments

- Costs- Margin

- Risk Contracts

- Diagnosis- Chronic Conditions

- Labs & Results- Procedures &

Medications- Quality

- Appts- Scheduling

- Utilization & Throughput

- DRG - Location

99

Enterprise Data Architecture

Data

Inte

gra

tion

& T

ran

sfo

rmatio

n

Patient Panel Analytics

Targeted Populations

& Outcomes

Baseline Expenditures

& Costs

Accountability Models

Financial Reconciliation

Population Health

Management

Dashboards &

Analytic ViewsContract Measures

Performance

Summary

Baseline Expenditure

Provider Profile

EMR

Billing

Provider Master

Health

System

Payers

Claims

Data

Access –

Navig

atio

n &

Secu

rity

Reports

Patient

Capture Integration and Transformation Consumption

Extensible Data

Architecture

Standard Data Models

MPI

Coding

Members

Provider

Claim

Reference

Other

Master

Data

Encounter

Location

1010

Data Integration Challenges

Data From Multiple Source Systems of Record and Points of Origin

• Differing Formats and Semantics

• Inconsistent Taxonomies

• Differing Data Granularities

Technical Challenges

• Timing and Granularity Differences and Conflicts

• Access to data stored in the cloud

• Positioning for Big Data Opportunities

End-User Experience

• Consistent but Responsive (Variable, Tailorable) Experience

• Power User vs. Ease of Use

• Education on Source, Meaning and Veracity of Data Elements

Data Governance

• Lack of consistent Enterprise-wide definitions

• Different groups use similar terminology for different data and meanings

Evolving Needs for Focused Analytics – Driving Clinical and Financial Performance

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End-User Experience – Navigating Complex Data Spaces

Patient

Organization

ProviderLocation

Contracts

Payer

Claims

Payments

Encounter

Charges

Costs

Diagnosis

Treatments

Chronic

Condition

Disease

Group

Procedures

Medications

Margin

Events

Data Navigation Model …

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End-User Experience – Empowering Analytics

Navigating Multi-Dimensional Data Spaces

Intuitive & Flexible Navigation of Multi-Source Data Spaces

• Data Integrated from Numerous Sources – Network Data Model

• High-Performance Interactive UI – Free Navigation Across Subject Areas

1313

End-User Experience – Empowering Analytics

Empowering the End-User with Context-Informed Search …

1414

Moving Forward – Risk and Population Focused Quality Contracts

0

20

40

60

80

100

120

140

160

Year 1 Year 2 Year 3 Year 4 Year 5

PER

FOR

MA

NC

E A

GA

INST

BA

SELI

NE

CONTRACT PERFORMANCE YEAR

Performance

CPI

Efficiency

Baseline

Value Axis

Patient Count ...

{ Contract Performance } - { Aggregate Population } - { Segment by Quality Measures, Cost Components }

Segmentation

Population Segments, Quality Measures, Cost Components, ...

Savings Elements

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Risk and Population Focused Quality Contracts

Population Composition

0

20

40

60

80

100

120

140

160

180

2008-Q2 2008-Q4 2009-Q2 2009-Q4 2010-Q2

PA

TIEN

T C

OU

NT

DATE OF OBSERVATION

Diabetes

PreDiabetes

Normal

Value Axis

Patient Count ...

{ Population Composition } - { Diabetes } - { Disease Severity Cohort }

Color Axis

Disease Severity ...

Disease Severity

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0

2000

4000

6000

8000

10000

12000

14000

160004

.8

5.0

5.2

5.4

5.6

5.8

6.0

6.2

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6.6

6.8

7.0

7.2

7.4

7.6

7.8

8.0

8.2

8.4

8.6

8.8

9.0

9.2

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9.6

9.8

10

.0

10

.2

10

.4

10

.6

10

.8

11

.0

PA

TIEN

T C

OU

NT

HB A1C - MOST RECENT OBSERVATION PER PATIENT

Poor

Good

Excellent

{ Population Composition - Distribution Analysis } – { Diabetes } – { Disease Severity Cohort }

Adherence toCare Plan

Value Axis Color Axis

Patient Count ... Adherence to Care Plan ...

Category Axis

Hb A1c - Most Recent Observed Value

Risk and Population Focused Quality Contracts

Population Composition – Distribution Analysis

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Risk and Population Focused Quality Contracts

Population Composition – Distribution Analysis

0

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PA

TIEN

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OU

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NUMBER OF MONTHS ENROLLED ON INTERVENTION CARE PLAN

UnfavorableNo ChangeFavorable

{ Population Composition – Distribution Analysis } - { Diabetes } - { Time on Care Plan Cohort }

Response toCare Plan

Value Axis Color Axis

Category Axis

Patient Count ... Response to Care Plan ...

Number of Months Enrolled on Intervention Care Plan

1818

Reducing Clinical Variation

Procedure / Treatment:

• Current Treatment

• Prior Treatments

• Response

• Adverse Events

Disease Condition:

• Diagnosis

• Complications / Comorbidities

• Demographic / Socio-Econ

Operations / Utilization:

• Service Utilization

• Resource Utilization

• Fac / Lab / Mat / Equip

• Care Planning / Mgmt,

Pathways

Financial:

• Revenue & Cost

• Profitability

• Payers & Contracts

Quality:

• Clinical Outcomes

• Patient Satisfaction

• Process Adherence

Measures:

• HbA1c, Mortality, QOL, Patient

Satisfaction, HAC, Infection

• LOS, Resource Util, Service Util

• Reimbursement, Costs of Care

• # Encounters, Care Settings, Duration

Between Encounters

• Care Plan Adherence

1919

Reducing Clinical Variation

0

50

100

150

200

250

300

1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8

Tota

l Nu

mb

er o

f C

ases

Avg LOS per Case

Rotator Cuff Repair: Distribution of Avg LOS by Surgeon

Hockensmith Hunnicutt Sexton Roderick Endicott

2020

Reducing Clinical Variation

0

50

100

150

200

250

300

1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8

Tota

l Nu

mb

er o

f C

ases

Avg LOS per Case

Rotator Cuff Repair: Distribution of Avg LOS by Surgeon

Hockensmith Hunnicutt Sexton Roderick Endicott

0

20

40

60

80

100

120

140

160

1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8

Tota

l Nu

mb

er o

f C

ases

Avg LOS per Case

Rotator Cuff Repair: Distribution of Avg LOS by Surgeon

Hockensmith Hunnicutt Sexton Roderick Endicott

2121

Reducing Clinical Variation

2222

Mobilizing for Value-Based Care

Workflows Integrate Data Capture, Care Delivery, Communications & Shared Metrics

Physician Office

Other Care Settings

Integrated

Database & HIE

Patient Registries

& Analytics,

Financial & Quality

Measures

Workflow

Triggers, Alerts &

Escalation

Patient Registration, Scheduling

Call Center

Patient Home

Web Access Assessment & Stratification

Individualized Care Plan

Discharge Progress

Review

Labs

EMRs

Quality

Performance

Improvement

Phone

Outreach

Workflow Mgmt

Data Sharing

Patient Engagement

Care Plans

Practice Mgmt

Cost

of Care

2323

Achieving Value-Based Accountable Care

A Business Intelligence & Change Management Platform

Patient Panel

Definition

Targeted

Populations &

Outcomes

Baseline

Expenditures

& Costs

Accountability

Models

Financial

Reconciliation

Population

Health

Management

Integrated

Data Platform

Changes to Processes

& Operations

Changing Business

Models

Population &

Practice Models

2424

Integrating Analytics for Value-Based Healthcare

Accountable Care Analytics

Clinical

Strategic

Planning

IT

Practice

Mgmt

Marketing

Finance

Revenue Cycle

Costs, Margin

Payer Mix

Stratification

Outcomes

Quality &

Safety

Growth

Market Share

Competition

Architecture

Data Quality

Tools, Applications

Security, Governance

Patient Satisfaction

Panel Management

Continuum of Care

Outreach

Physician Liaison

Relationship Mgmt

Service Improvement

Integrating Analytics for Clinical, Operational and Financial Improvement

2525

Questions?

Thank You!

2626

Dave Hegarty

Healthcare Business Development

Phone: 781-213-9864

Email: dhegarty@edgewater.com

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