Presentation on theme: "Automatic enrollment and state health reform Stan Dorn Senior Research Associate Urban Institute 202.261.5561 State Coverage Initiatives."— Presentation transcript:
Automatic enrollment and state health reform Stan Dorn Senior Research Associate Urban Institute 202.261.5561 email@example.com State Coverage Initiatives Program AcademyHealth Denver Colorado August 3, 2007
Urban Institute2 Overview 1.Enrollment models 2.Data issues 3.Applying auto-enrollment to state coverage reforms
Urban Institute4 If you build it, will they come?
Urban Institute5 Why enrollment matters Necessary to accomplish the goal of coverage expansion Cost offsets with eligible but un-enrolled: when they get sick, they will use services, and the state will pay Standard enrollment growth curve creates political vulnerability – for example, see next slide
Urban Institute6 PRESS RELEASE The Maine Heritage Policy Center Muskie Survey Shows Dirigo’s Failure and High Cost to Taxpayers Taxpayers are spending $15 million a year to reach 1,800 uninsured Mainers. Portland, ME - The Maine Heritage Policy Center today cited the DirigoChoice Member Survey: A Snapshot of the Program’s Early Adopters, a report prepared by the Muskie School of Public Service, as definitive proof of the failure of the DirigoChoice health insurance product. The survey reveals that only 1,800 or 22.4% of DirigoChoice enrollees were uninsured and that the state is spending nearly $8.00 for every $1.00 of savings to the health care system attributed to providing coverage to those previously uninsured individuals. “DirigoChoice is a costly failure,” said Tarren Bragdon, director of health reform initiatives for the Maine Heritage Policy Center. “It is not significantly covering the uninsured and it is costing the Maine taxpayers millions of dollars a year. Maine taxpayers are paying $15 million a year to cover 1,800 previously uninsured people.” At one year, Mass. healthcare plan falls short By Sally C. Pipes | May 15, 2007 “So one year in, we have a plan that, even if no more concessions to liberal advocates are made, falls 20 percent short of its stated goal.”
Urban Institute8 Traditional public benefits model Government’s role Provide program information – “outreach” Process applications Individual must Apply Provide individual information showing eligibility Complete the application process
Urban Institute9 Implications of traditional model Denies coverage to eligible people who: Do not apply Do not complete the process It takes several years for a new program to reach many of its targeted beneficiaries High ongoing administrative costs for state BUT: Familiarity means less risk, culture shock, uncertainty, mid-course adjustment after initial stumbles Permits covert caseload controls that lower cost with less risk of successful opposition – Procedural barriers “prevent waste, fraud and abuse” Reduced outreach may never come to public attention
Urban Institute10 A different model: Auto-enrollment Mechanisms Default enrollment Data-driven enrollment Proactively facilitated enrollment Promise – lessening the historic tension between safeguarding program integrity and simplifying application procedures. More eligible people get covered A smaller percentage of ineligible people get covered Operational administrative costs drop (after infrastructure development) Happened with WIC and NSLP
Urban Institute11 Basic principle: Newton’s First Law of Motion “An object at rest tends to stay at rest…”
Urban Institute12 Examples of auto-enrollment 1. SCHIP vs. Medicare Part D 2. Retirement savings 3. Medicare Part B 4. Community-based, proactive facilitation of child health enrollment 5. Retention of health coverage in Louisiana 6. Massachusetts CommCare
Urban Institute13 Example #1: SCHIP vs. Low-Income Subsidies (LIS) for Medicare Part D Source: Selden, et al., 2004 (MEPS data). Effective 10/1/97 Food stamps, after 2 years: 31% take-up
Urban Institute14 Data-driven enrollment – Medicare Part D, low-income subsidies (LIS) Without application, automatically enrolled in drug plan, with LIS, if received Medicaid or SSI the prior year Can apply to SSA
Urban Institute15 Example # 1, continued Total enrollment: 74% Source: CMS enrollment data. Calculations by Urban Institute.
Urban Institute17 Example #3: Medicare Part B Sources: NASI, 2006; Remler and Glied, 2003.. Note: Medicare Savings Programs (MSP) help Qualified Medicare Beneficiaries (QMB) with income up to 100% FPL and Specified Low-Income Beneficiaries (SLMB) with income between 101 and 120% FPL.
Urban Institute18 Example #4: Community-based facilitators of child health enrollment Source: Flores, et al., Pediatrics, 12/05.
Urban Institute19 Example #5: Retention in Louisiana Source: Summer and Mann, Georgetown University Health Policy Institute (prepared for Commonwealth Fund), June 2006. Note: other policy changes included telephone contact, rather than forms, to supplement data.
Urban Institute20 Example # 6: Auto-enrollment in Massachusetts, based on prior uncompensated care pool Source: Commonwealth Connector Authority, June 2007 (unpublished data).
Urban Institute22 Cross-cutting data issues Privacy Funding IT development
Urban Institute23 Privacy Practical strategy – inform families, in advance, about information disclosure. Provide chance to opt out Builds trust State law changes may be needed to access data Safeguards of confidentiality, data security
Urban Institute24 Building IT infrastructure Enhanced FMAP via MMIS (90% for start- up, 75% for operations) is denied to “eligibility systems,” by federal regulation from MITA – today’s MMIS – Add eligibility data to EHRs Offset with lower operating costs NSLP case study in MN: 80% savings, net
Part III: Applying Auto- Enrollment to State Coverage Reforms
Urban Institute26 Where can auto-enrollment help? Three crucial functions: Identifying the uninsured Determining eligibility Enrollment into coverage
Urban Institute27 Function Number One: Identifying the Uninsured Key life events Master list
Urban Institute28 Key life event strategy It’s a key life event if it includes: Many uninsured Existing mechanism on which to build Examples Health care visits (e.g., at hospitals, CHC/s) State EITC forms (if state has EITC) W-4 forms (wage withholding when starting work) Applications for unemployment insurance Child ages off Medicaid/SCHIP or parent’s insurance Annual start of school, child health forms
Urban Institute29 Critical piece of key life event strategy: a nearly effortless form Check one box to indicate Uninsured Want coverage Want state officials to examine otherwise confidential data to determine eligibility Permission to contact household to follow-up SSN (to facilitate data matching) Uninsured person seeking coverage (essential to FFP) Household adults (can’t be required of non-applicants, but can request, to facilitate eligibility determination – phrase carefully!) Maybe one or two facts unavailable from other data Citizenship? Resist temptation to add!!!
Urban Institute30 Identifying the uninsured through master list comparison Simple idea: compare list of insured with list of all group members People on one list but not the other are probably uninsured
Urban Institute31 Where’s the list of insured people? Medicaid/SCHIP Private coverage – DRA Section 6035 (TPL) Each state must require insurers to provide information re enrollment of Medicaid beneficiaries Explicitly applies to group plans under ERISA CMS developing mechanism
Urban Institute32 Listing all group members Statewide lists are incomplete but useful starting points More targeted lists are promising. E.g.: Compare public program records with Medicaid/SCHIP enrollment records to identify the potentially uninsured For Medicaid – enrollment does not have to await info re private coverage, since the privately insured qualify for Medicaid
Urban Institute33 Example: poor, uninsured parents Source: Dorn and Kenney. Notes: (1) Poor parents have the following characteristics: their income is at or below the FPL; they are ages 18 to 64; and they live with a stepchild, biological child, or adopted child under age 18. (2) Analysis based on 2002 NSAF. (3) NSLP is the National School Lunch Program.
Urban Institute34 Health Coverage Among Poor Parents Whose Families Participated in Means-Tested Nutrition Programs or Whose Children Received Medicaid, 2002 Source: Dorn and Kenney. High-impact, efficient intervention via SPA
Urban Institute35 Function Number Two: Determining Eligibility 1.Define eligibility based on data 2.Express Lane Eligibility 3.Using data to target intensive application assistance D–n it all, sir! Am I not eligible? Illus. John McLenan, A Tale of Two Cities, 1859
Urban Institute36 Defining eligibility based on data: Medicare Part B means-testing Traditionally, Part B premiums received 75 percent subsidy for all enrollees Under Medicare Modernization Act (MMA), Part B subsidy is means-tested, starting 1/07
Urban Institute37 For purposes of Medicare Part B, how is 2007 income determined? 2005 tax year income determines Part B income for all of 2007 It does not matter if you won the lottery in 2006 or 2007 BUT - if you come forward and show your income is lower in 2007 than 2005 and you qualify for deeper subsidies, your 2007 income controls!
Urban Institute38 Applying this model to state coverage initiatives Pure: disregard income above taxable income during the most recent available tax year 1902(r)(2) Adjusted: disregard such income, as modified by more recent income information from state workforce agencies New hires and quarterly earnings data Either way: Continuous eligibility, regardless of post-enrollment changes in household circumstances No asset test – data not as good re assets Other eligibility pathways remain open
Urban Institute39 Is this reasonable? Fewer possibilities of error – MEQC/PERM Key: prior SPA approval But low-income people don’t file tax forms! Income information still reported – 1099, W-2 If state provides EITC, most low-income people file 86% of eligible families with children, 45% without children Income changes are more common with working families than with seniors With proposed Administration tax credits for low- income workers, prior-year tax data determined eligibility
Urban Institute40 Express lane eligibility Concept: if another means-tested program has already found a family to have sufficiently low income to qualify for Medicaid or SCHIP, enroll the family in Medicaid or SCHIP! But there are obstacles to overcome!
Urban Institute41 Most low-income, uninsured children live in families that receive means- tested nutrition assistance Source: Dorn and Kenney, Urban Institute (prepared for Commonwealth Fund), June 2006. Notes: (1) Analysis based on 2002 NSAF. (2) NSLP is the National School Lunch Program. (3) Low- Income is at or below 200% of the FPL.
Urban Institute42 Obstacle: methodologies Problem: each program has its own methodology Generally, Medicaid will determine families to have lower income than will other programs But not always – e.g.,food stamps, excess shelter cost deduction Upshot: health program must recalculate eligibility, family may need to reapply
Urban Institute43 Overcoming methodology obstacle Pick non-health program with income threshold far below Medicaid’s. E.g, with children: Medicaid to 150% FPL (after disregards) Free school lunch - 130% FPL (gross income) SSA 1902(r)(2) income disregard. E.g.: Disregard all income above net family income found by food stamp program FS net income limit = 100% FPL 1115 waiver to disregard methodological differences Budget neutrality: unspent SCHIP allocations
Urban Institute44 Will federal government say yes? Uncharted terrain - but Bush Administration supported Express Lane in context of Frist-Bingaman bill in 109 th Congress (S. 1049) CMS already provides more aggressive Express Lane eligibility into low-income subsidies (LIS) for Medicare Part D Auto-enrollment from MSP into LIS, even though: 6 states waive asset test for MSP, and LIS has asset test 18 states disregard in-kind income for MSP 10 states define household to include resident grandchildren Statutory standard: “Substantially the same”
Urban Institute45 Proposed legislation Express lane becomes state option or demonstration Children and adults Extra federal money for IT connections between health agencies and others More access to federal data Context: SCHIP reauthorization
Urban Institute46 At a minimum, can use data to target intensive application assistance With children, can provide presumptive eligibility, then follow-up to transition to ongoing coverage
Urban Institute47 More on data-based targeting of intensive application assistance To target, use income data from multiple sources In gathering income data, notify re: (a) possible use for health coverage and (b) how to opt out of such use Simplifying application process Phone calls, not forms (send cards, ask family to call at convenient time) Pre-populate forms with income estimates, ask for corrections Use MCOs to provide assistance? Leveraging someone else’s dollars – BUT Conflict of interest
Urban Institute48 Function Number Three: enrollment into coverage Default enrollment Phone-activated insurance cards
Urban Institute49 Default enrollment “You’re eligible! We’ll enroll you unless you say no.” Example: NYC enrolled 13,000 children based on Food Stamp data. Parents could decline, but only 2% did. Probably best without premiums Risks Wrong address Capitated payments, no services Strategies To start capitated payments, MCO must confirm w/family Partial withhold of capitation until 1 service provided In large part, base default enrollment shares on preventive services to prior default enrollees Monitor real-time encounter data
Urban Institute50 Phone-activated insurance cards Idea from Ruth Kennedy, director of child health for LA Medicaid and SCHIP Send cards, with strip of tape saying, “Call toll- free number to activate” Voice prompts can allow choice of plan Can use with premiums
Urban Institute51 “ Applications? We don’t need no stinkin’ applications!” The Auto-Enrollment motto:
Urban Institute52 Summary For new state initiatives to succeed, enrollment and retention methods must be effective The more you ask people to do, the fewer people will do it If you want new initiatives to cover as many eligible individuals as possible, consider automatic mechanisms to: identify the uninsured; determine eligibility; and enroll people into coverage.