Building a Performance-Based Value Model to Drive Population Health Strategies

Becker's Hospital Review 4th Annual Health IT + Revenue Cycle ConferenceSeptember 20, 2018

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Agenda

• Industry Overview• Preparing for Risk• Optimizing Your Value-Based Contracts• Building Analytics Value Model for Population

Health

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Two Conflicting Healthcare Paradigms

1. Continued pressure to bring down healthcare costs and reduce reimbursement (volume-based model)

2. Shift to accountable care and value-based reimbursement tied to performance of services

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The New Realities of Reimbursement

• Commercial reimbursement for FFS is either staying flat or decreasingo PCPs are experiencing flat reimbursement or a slight increase from 2% to 3% o Specialist are experiencing decreases from 5% to 10%

• Pressure on hospitals to accept reimbursement at Medicare rates• Payers are following the lead of Medicare and wanting providers to assume

“risk” • Payers will provide incentives for utilization management unless providers can

show evidence of clinical care management through quality outcomes

• Providers taking steps to prepare for “risk” will be best positioned for risk

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Attributes that Drive Success of Value-Based Contracts

Scale and geographic reach

Provider engagement

Network management

Financial decision support

Analytic capabilities

Value-Based Contract Products

Patients

Building the information-management platform from data to information to knowledge is the foundation of a good value-based contract

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Population Health ManagementRisk Stratification

Risk

-Co

st5%

15 - 30%

65 - 80%

Risk-Rising PatientsChronic, aging, chronic condition – unmanaged

Low-Risk PatientsHealthy or chronic condition – managed

High-Risk PatientsOne complex illness, multiple comorbidities

Population

Current Focus

ACOs/CINs Opportunity to ExpandMarket Services &Share

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Data Will Drive Contract Performance

• Obtaining “usable” data is the top issue for ACOs

• Access to historical claims data is key• Consistent data format, level of detail for

“in-area, out-of-network” claims, compatibility with ACO’s analytic systems

• Recommend attaching specified claim file layout as part of contract

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Types of Data That Drive Results

There are two types of data that ACOs must be able to manage effectively to respond to the changes in the market. High-performing organizations are shifting their focus from retrospective to transactional data to help them make better and quicker decisions.

Retrospective Data

• Reactive• Gives a look at past

performance• Provides a robust

understanding of the business

Transactional Data

• Proactive• Action-oriented data

resulting from analysis

• Real-time data that can be used for decision-making

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Optimizing the Value-based Contract

• Transition to a “high-performance” data analytics organization

• Optimize provider participation• Quantify value from a value-based contract

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Data Management and Analytics Organization

Data Analytics Services: • Centralized function • Builds and maintains the organization’s data repository• Maintains data integrity within the organization’s data

repository• Determines analytics tools• Manages system-level analytic resources, reporting

requests, and overall data management

Centralize Business Analysts:• Support organization-wide reporting requests• Support ad hoc reporting requests

Departmental Business Analytics:• Provides direct reporting support to

departments and stakeholders • Report visualization tools can be customized to

the department’s reporting and analytics needs

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Multi-Generation Plan to Achieving Analytics Excellence

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Governance/ Definitions

Reporting/ Visualization

Data Needs

Governance/ Definitions

Data Enhancement

1. Analytics Organization

2. Advance Analytics and Reporting

Real-Time Information

3. Real Time Analytics and Applications

Advanced Reporting

Additional Technology/ToolsCustomer Service

Alerts for Action

Predictive

Supporting Applications

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High-Performance Data Analytics Requires Cultural Change Management

Proof of Concept Based on Actionable Insights

Identify Problem Statements (What are we solving for?)

Information Required to Solve Problems (Report)

Data Elements Needed to Produce the Report

Systems and Sources of Data

Data

Information

Insights

Action

System Platforms

Information Request

Leveraging a “problem-back” approach will create actionable information

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How Should Executives Optimize Provider Performance?

• What are the most effective speed-to-market care-management programs for high-opportunity population segments?

• What is the care team doing today to engage people to enroll and participate in programs that have proved to be effective?

• How do we ensure care managers are aware of the impact and value of their work on improving member outcomes and reducing costs?

• What are the redundancies or inefficiencies among different care-management programs across the continuum of care?

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How to Value Quality

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ROI Calculator Utilization Analyzer Predictive Modeler

Provides a quantitative framework for performance monitoring, investment, and financial decision-making.

• Investments required to build and expand programs

• Organizational impact on revenue and expenses providing sensitivity analysis

• Forecast the pace of change and its impact on the organization’s financial performance

Enables an organization to evaluate revenue and expense implications related to:

• Care management intervention

• Clinical performance programs

• Care settings across network providers

• Shifts in utilization

Provides opportunities to move into prospective outcome forecasting and risk analysis. Allows organizations to:

• Anticipate outcomes-based population and program variables

• Provides statistical analysis to identify dependent and independent variables affecting program outcomes

• Allows for prospective risk contracting and expanded care-management reconciliation with payers

Value ModelCharacterized and defined by the

following

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Building the Value Model’s ROI Calculator

Translate large amounts of structured and/or unstructured data into actionable insights

Identify important relationships between practice patterns and outcomes

Identify specific levers (expense and revenue) for improving quality and reducing cost

Move beyond understanding trends to understanding why trends are occurring

Predict the likelihood of favorable and unfavorable outcomes

Understand and stratify risk

Benefits of advanced data mining, machine learning, and predictive analytics

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Value Model with a Macro and Micro Perspective

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Uses advanced statistical and regression analysis to identify marginal impact of care-management programs and implications of populations

Economic Component Statistical Cost-of-Care Component

Focuses on macroeconomic outcomes relating to enterprise-wide revenue impacts, expense management, and shifts in medical spend

The statistical model produces estimates of cost-of-care reductions that can be fed into the economic model for bottom-line impact estimation

Increased shared savings opportunities

Care management effect on place-of-service utilization

Expenditures for program initiatives supporting care-management programs

Identify ROI, net savings, and project future financial impact

Supports transitions from shared savings to full-risk capitation

Uses cost levers as dependent variable to anticipate performance output

Produces average cost change per program input (e.g., care coordinator FTE)

Incorporate a marginal productivity curve (output vs. costs) to identify point of diminishing return

Approached a variety of ways:• Aggregate PMPM• Patient-level PMPM• Program-specific

cost impacts

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Understand Both “Good” and “Bad” Utilization• Commitment to improving quality, reducing avoidable utilization, and costs

• Reductions in medical spend are a byproduct of the reduction in avoidable utilization such as emergency department (ED) and acute inpatient utilization for individuals with chronic conditions

• Reductions in medical spend are offset by increases in “good” utilization, such as increased primary care physician (PCP) visits, wellness screens, and pharmacy costs associated with increased medication management

“Bad” Utilization• ED

• Inpatient Acute (Includes M/S, NICU, and ICU)

• Inpatient Sub-Acute

• Dialysis

• Specialists

• Diagnostic Radiology

• Hospice

• Outpatient Surgery

• Other Surgery

• (Elective)

“Good” Utilization• Pharmacy medication adherence• Office Visits (PCP)• Skilled Nursing• Home Health• Laboratory• Preventive Care• Palliative Care• Mental Health

Condition-Dependent Utilization• Physical Therapy• DME

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Building the infrastructure,

processes, timing, and measurement

tools for making the transition from FFS

to FFV.

Developing an effective, efficient, care-management

delivery infrastructure to

identify and drive profitability under risk arrangements

Redesigning care processes to reduce errors and support transitions of care

Developing the analytics necessary

to support population health

management, including

understanding the predictors of

admission risk

Evaluating potential and expected returns from

investments made within population

health infrastructure

Identifying readmission risk,

reducing avoidable readmissions, and avoiding penalties

from CMS

Improving physician engagement,

improving quality, and eliminating referral leakage

The Journey to High Performance

High-performance organizations are building and using value models as a decision-making tool to measure performance outcomes, predict risk, and evaluate return on investment (ROI).

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Data Source/Elements to Support Model Build

1. Accurate PMPM cost data for attributed and potentially attributed populations with specific population

2. Member counts for both attributed and potentially attributed members represented in PMPMs3. All data to be available with a dimension of time, ideally by month, or at least quarter

4. Demographic information and average risk variables

5. Availability of measures that accurately capture program activity/treatment effects, including any relevant ramp-up periodso Will need clinical guidanceo Need to identify sources of program intervention variables by time period (ideally by month)

6. Ability to match members between the data sources

7. Sufficient richness of data to construct matched cohorts and/or similar episodes from control (non-attributed) population

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• Healthcare is going through a transformation• Changes in healthcare delivery and bending of the

cost curve will make us more accountable• Adoption and integration of analytics is a big driver of

change• New financial models will align incentives and modify

behaviors• Continue to manage cultural change as a transition

point to risk• Aligned objectives will prepare you for accountable

care

In Summary

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Daniel J. MarinoManaging Partner708.602.0308dmarino@luminahp.com