The Risk Adjustment Treadmill: Keeping Pace in an Ever-Changing Market

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The Risk Adjustment Treadmill: Keeping Pace in an

Ever-Changing Market

14 July 2016


CMS’ REVISED PAYMENT MODEL


The updated model includes the following changes:

What’s Changing – CMS-HCC Risk Adjustment Model

Community model segment split into six separate model segments

Community risk scores will depend on the dual status in the payment month

Two additional model segments: Long-Term Institutional and New Enrollee

Long Term Institutional risk scores will include a Medicaid factor based on

Medicaid status in the payment year

Data years used to recalibrate the model (10/11 13/14)

Disease interactions











Source: RTI International analysis of 2011-2012 Medicare 100% data. https://www.cms.gov/Medicare/Health-

Plans/MedicareAdvtgSpecRateStats/Downloads/RiskAdj2017ProposedChanges.pdf












• CMS will continue to calculate the 2017 risk score by blending two data

sources with an increased emphasis on EDPS.

• For PACE organizations, CMS will continue the same method of calculating

risk scores as used for the 2016 payment year (no blending, additive only)

What’s Changing - Encounter Data as a Diagnosis Source

90%

10%CY 2016

RAPS and FFS EDPS and FFS

75%

25%

CY 2017



Transition Timeframe

90%

10%CY 2016


75%

25%

CY 2017


0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

PY 2020*

PY 2019*

PY 2018*

PY 2017

PY 2016

EDPS/RAPS Blend

EDPS and FFS RAPS and FFS

January 2017March 2017


By now, you should be well underway with implementing business process changes to ensure

you can support the revised, blended, model:

Process Changes to Support the New Model

Completed programming to support the EDPS filtering

logic

Implemented a process for identifying

and mitigating Dx differences

between EDPS and RAPS data

Developed processes to

ensure all diagnoses without a

claim make their way

through as an encounter

Working rejects from both sources

Trained team members to understand the blended model and

differences in data sources


What Have We Learned So Far?

This was never an easy process and it becomes more complex every year

We are seeing varying levels of understanding and varying levels of readiness across the industry

Many thought the disparity between the two data sources would be minimal

We most frequently see degradation in EDPS scores as opposed to RAPS scores


DATA SHARING FOR COLLABORATION


Payer/Provider Collaboration and Data Sharing

Did I get all the data I

need?

Did I get paid

correctly?

Did CMS get all of the information?

Consistent and comprehensive data supports quality of care improvements

This means:• Provider should have a view of both the CMS risk scores based on RAPS data and EDPS data.• There should be a process in place for investigation and issue resolution• Need a feedback loop to allow collaboration for issue resolution and process improvements

• Integrated systems will have an easier time at this• Contracted networks and plans will have to work harder to create this collaboration



• Are exception reports being shared (gaps and outliers)?

• Are member health profiles being shared?

• Are outcomes reports being shared (risk scores, HCC weights)?

• Are variance reports being shared (RAPS vs EDPS)?

Agree on Data and Format

• What happens when one party finds a discrepancy?

• Is there a reconciliation process?

Create Two-Way Communication

Revenue and Care Continuity under this new model requires a robust and consistent data set for both EDPS and RAPS.

Alignment is dependent on sharing the right data in the right format and good communication



• Are exception reports being shared (gaps and outliers)?

• Are member health profiles being shared?

• Are outcomes reports being shared (risk scores, HCC weights)?

• Are variance reports being shared (RAPS vs EDPS)?

Agree on Data and Format

• What happens when one party finds a discrepancy?

• Is there a reconciliation process?

Create Two-Way Communication

Revenue and Care Continuity under this new model requires a robust and consistent data set for both EDPS and RAPS.

Alignment is dependent on sharing the right data in the right format and good communication

As an industry, we have 8 months to improve our processes before 25% EDPS weighting.


Understanding How/Where Your Data Flows


Understanding How/Where Your Data Flows

Claims Diagnosis

Limitations

Claims Entry

Protocol

Encounter Claims Entry

EDI Vendor Oversight

Feedback Reports

Rejected Claims

Claim Volume


RETROSPECTIVE AND PROSPECTIVE

TACTICS


Effective Programs Work Retrospectively & Prospectively


Balancing Retrospective and Prospective Tactics

Retrospective Prospective


Integrate Risk Adjustment & Quality Improvement Activities to

Gain Efficiencies

Accurate Medicare

Advantage Risk ScoresEnsure That CMS

Reimbursement is

Commensurate With the

Expected Costs

Improved Quality

Measures Ensure that Conditions are

Identified and Treated

Each Year

Revenue

Quality of Care


MEASURING RESULTS


Measuring Risk Adjustment Program Effectiveness

An important part of a well-run risk adjustment program is measuring results.

Set Year-End and Interim GoalsSet a year end goal and break it into quarterly milestones.

Create a Balanced ScorecardYour risk adjustment dashboard should reflect all aspects of the program - RAF results, initiative return on investment, provider engagement/satisfaction, and auditing results.

Watch Monthly Results and Trends Over TimeImportant trends might include quarterly RAF scores, RAF trends by provider group/PCP/geography, year over year adjustments and program costs.


Measuring Risk Adjustment Program Effectiveness

Measure ROIBe sure to note the impact of initiatives on year over year trends.

Celebrate SuccessA successful risk adjustment program takes a village! Recognize key contributors to the initiatives.

Special RecognitionRecognize providers that have demonstrated the most significant improvement in care gap closures.

Treat Measurement Like an Advertising CampaignCommunicating progress is important for on-going engagement.


SCIO HEALTH ANALYTICS’ POINT OF VIEW


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:

• Suspecting: 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








PMPM Revenue



$760

$784

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

.98 RAF

.95 RAF


$24








PMPM Revenue



$760

$784

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

.98 RAF

.95 RAF


$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


HAT Report

Healthcare

The Risk Adjustment Treadmill: Keeping Pace in an Ever-Changing Market