Metrics for Data-Driven Decision Making and Demonstrating Value

Medicaid Small Group Convening

January 15, 2015

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1. Why Monitor a Core Set of Metrics?

2. State Use of Process and Outcome Metrics

3. Using Metrics to Drive Messaging

4. Discussion/ Next Steps

Agenda

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Why Monitor a Core Set of Metrics?

• Provide decision makers with retrospective data

► Were projections accurate?

• Provide decision makers with information about cost and utilization trends

► Trend data can act as an early warning system

► Trend data can inform ongoing discussion and decision regarding benefits

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Why Monitor a Core Set of Metrics? (continued)

• Report progress to policy makers, the media and public

► Information will be needed to support upcoming policy debate

• Build and maintain analytic infrastructure to respond to ad hoc requests for data

► Assure consistency in reporting► Avoid duplication of data collection and

analysis ► Reduce analyst burden

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Potential Trends and Metrics to Monitor

• Enrollment operations and performance► Call Center

• Volume

• Wait time

• Handle time

• Abandonment

► Applications (by channel)

► Renewals

► Determined eligible

► Processing time

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Potential Trends and Metrics to Monitor

• Pharmaceutical► PMPM► Members vs. users► Number of prescriptions► Rebates by quarter► Top five prescriptions by volume and

expense

• Medical► Members vs. users► Top five diagnosis (and bottom five?)► Top five office visits

• Dental

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Potential Trends and Metrics to Monitor

• Membership► Members - by age, income, geography, gender, etc.

► Terminations by type

• Member Services► Appeals received

► Appeals resolved

► Appeals expedited

• Members – Who is not using services?► Characteristics of members not using services

► Age, income, geography, gender, etc.

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Potential Trends and Metrics to Monitor

• Financial► PMPM by eligibility category

► Top five services by cost

• Plan performance ► Quality measures (e.g. NCQA star rating)

► Timeliness of claims

► Complaints

• Provider network analysis

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Examples of Categories of Measurement

• Monthly

• Percent change since previous month

• Year-to-date

• 12-monthly average trend

• By eligibility category

• Fee-For-Service vs. Managed Care

• By region or county

• By Managed Care Organization

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Factors to Consider When Selecting Data

Is the data;

• Easy to access?

• Easy to use?

• Timely?

• Useful?

• Of high quality?

• Comprehensive (are there data gaps)?

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Prioritizing Measures: Some Considerations

• Mix of broad-based and specific measures in areas of interest

• Mix of process and “outcome” measures

• Leverage existing reporting

• Monitor a consistent set of measures over time

• Identify sub-populations of interest (Eligibility category? Demographics?)

• Collaborate within/outside the agency when possible (using common definitions, etc.)

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Approaches to Producing Analytics

• In house, dedicated staff► Need separate, ongoing resource stream

► Need sufficient training, hardware/software

► Staff can feel isolated if unit is too small

• Contract with university or consultant► Lose some control

► Data sharing agreements can be challenging/time consuming

► Gain technical expertise/skills

► Data will be leveraged beyond internal use

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State Data and Analytic Capacity

56%31%

9%

4%

In House

In House and UnderContract

Under Contract

No Capacity

Source: NAMD. 2nd Annual Operations Survey. February 2014

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An Evaluation and Monitoring Framework can Prepare States for Tough Conversations

• Ongoing monitoring of metrics

• Responding to ad hoc requests

Example questions:

• Do the needs of the Medicaid expansion population match the benefits provided through the ABP?

• How does enrollment and/or utilization compare to what we expected?

► Budget implications of the woodwork effect, Medicaid expansion

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1. Why Monitor a Core Set of Metrics?

2. State Use of Process and Outcome Metrics

3. Using Metrics to Drive Messaging

4. Discussion/ Next Steps

Agenda

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Survey of SGC States on Evaluation and Monitoring

Survey administered December 19 to January 5 on:

• Process and Outcomes Metrics► What process or outcome metrics is your state

using to track the experience of Medicaid populations?

► Has your state used metrics to inform or drive messaging around the effects of coverage gains in your state?

• Enrollment, Expenses, and Utilization Demand Cost

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Examples of Process and Outcomes Metrics that States are Tracking

• Eligibility and Enrollment (IL, NY, VA) ► Accounts created, applications by source► Enrollment in traditional Medicaid (to track woodwork)

• Patient Experience (CO, OR, WV)► CAHPS

► Access to care

► Health plan satisfaction

• Quality and Coordination of Care (CO, MD, MI, NM, OR)► NQF, HEDIS measures

► Health Risk Assessment and Comprehensive Needs Assessment

► Follow-up after hospitalization for mental illness

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Examples of Process and Outcomes Metrics that States are Tracking (continued)

• Structural Measures (MI, OR)► Network Adequacy

► HRA completion rates

► Compliance with cost-sharing requirements

• Utilization and Cost (IL, MI, NM)► Primary care, behavioral health care, prescription

drugs

► Segmented by age, gender, race/ethnicity, income, delivery system (Managed Care or FFS)

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1. Why Monitor a Core Set of Metrics?

2. State Use of Process and Outcome Metrics

3. Using Metrics to Drive Messaging

4. Discussion/ Next Steps

Agenda

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How States are Using Metrics for Messaging: Select Examples

• Coverage gains and effects on insurance rates

► National news outlets listing Colorado as fifth in the nation in reducing the uninsured, a function of both the Medicaid expansion and the success of Connect for Health Colorado.

• Reductions in uncompensated care

► Hospitals in West Virginia featured in news articles on reductions in uncompensated care in emergency departments

• Quality of coverage► Oregon is informally using CCO performance data to show

how newly-eligible are experiencing care

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Messaging from Maryland

Blog on Governor’s website by DHMH Secretary Sharfstein, May 30, 2014:

“…We know that more than 330,000 Marylanders have enrolled in coverage since January 1 (exceeding our goal of 260,000)….for the first quarter of 2014, hospitals saw 62% fewer charity cases and 24% fewer self-pay cases, compared to the first quarter of 2013.Maryland’s unique system of setting hospital rates means that less uncompensated care translates directly into lower hospital bills. Officials on the rate setting commission said they plan to take 1% off of their planned rate adjustment in July…”

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A Look at the Healthy Michigan Plan Evaluation and Monitoring Framework

Metrics and reporting were mandated in PA 107, the law that authorized Medicaid expansion in Michigan. Specific metrics include:

► HRA completion rates

► Findings from HRA data

► Primary Care visits (scheduling and attendance)

► Healthy behaviors (acceptance and incentives)

► Cost sharing compliance

► Impact on hospital uncompensated care

► Impact on commercial insurance rates

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Healthy Michigan Plan Progress Report

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Healthy Michigan Plan Progress Report (cont.)

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Colorado Report on Implementation of the Accountable Care Collaborative

• Request for information from the state’s Joint Budget Committee

• Report requested (due November 1, 2014) to inform the Joint Budget Committee on:

► How many Medicaid clients are enrolled in the program

► The current administrative fees and costs for the program

► Performance results with an emphasis on the fiscal impact

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Reporting ACC Enrollment (June 2014)

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Reporting Costs for All Adults (Fiscal Year 2013-2014)

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Measuring Key Performance Indicators

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Updating Key Performance Indicators

KPIs for FY 2013–14: • Decrease in emergency room (ER) visits • Decrease in high cost imaging • Decrease in 30 day, all-cause hospital readmissions • Increase in well-child visits (all children)

KPIs for FY 2014–15: • Decrease in emergency room (ER) visits • Well-child visits (ages 3-9)• Post-Partum Care

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1. Why Monitor a Core Set of Metrics?

2. State Use of Process and Outcome Metrics

3. Using Metrics to Drive Messaging

4. Discussion/ Next Steps

Agenda

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Discussion Questions

• Do you have a staff member dedicated to running analytics?

► How many (FTEs)?► What are their backgrounds? Education? Training?

• What metrics do you find the most actionable?

• In what situations do you use data? (Or wish you had data to use?)

• What do you not have that you wish you had?

• Do you have tools to visualize the data? Is this automatic?

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Contact Information

Shannon McMahon – smcmahon@chcs.org

Chris Heiss – cheiss@chcs.org

Elizabeth Lukanen – elukanen@umn.edu

Julie Sonier – jsonier@umn.edu