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Under the data-hood of the “Patient Protection and Affordable Care Act, 2010” (this presentation based solely on publicly-available data and reports)

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Presentation on theme: "Under the data-hood of the “Patient Protection and Affordable Care Act, 2010” (this presentation based solely on publicly-available data and reports)"— Presentation transcript:

1 Under the data-hood of the “Patient Protection and Affordable Care Act, 2010” (this presentation based solely on publicly-available data and reports)

2 Some goals of the ACA Make quality healthcare insurance affordable to the 35- 41m Americans previously without it, using premium tax credits (APTC) supporting purchase of health coverage through state and federal marketplaces. Shift healthcare business model from a “fee-for-service” to a “value-based-medicine” basis. Encourage IT upgrades by providers. Lower healthcare costs and improve quality. Build on and simplify Medicaid and CHIP to cover more adults with low incomes and children. Complete the historical insurance coverage jigsaw.

3 A Seamless System of Coverage 0 133% FPL 241% FPL 400% FPL Adults Children Medicaid/CHIP Children Marketplace Subsidies Medicaid Adults Varies by State Source: Stephanie Bell, CMCS, HHS April 2013.

4 ACA achievements to date By end of Mar. 2015, about 10.2 million consumers had effectuated coverage. 85% received APTC to make premiums more affordable: average APTC was $272 a month. 57% were receiving CSR, further lowering the monthly cost. Consumer pays on average about 30% of total cost of a “silver plan”. Source: HHS Press Office, June 2, 2015 (http://www.hhs.gov/news/press/2015pres/06/20150602a.html)

5 The ACA landscape: EXCHANGES 16 states enroll customers through their own “State-Based Marketplace” (SBM). 34 states enroll customers using the “Federally- Facilitated Marketplace” (FFM), which produces the healthcare.gov datasets. Individual states can shift between the two types: some are “Supported State-Based Marketplaces” (SSBMs) inside the FFM for enrollment but independent for other things.

6 The ACA landscape: ISSUERS c.430 private or nonprofit health insurance policy issuers. c.20,000 different healthcare insurance plans. Plans vary: e.g. Individual Major Medical (Bronze, Silver, Gold, Platinum, Catastrophic), Pediatric-only, Standalone Dental Plans, Small Group, SHOP. Premium costs are allowed to vary only by: age, tobacco use, family size, and rating area.

7 The ACA landscape: PROVISIONS WITH ENCOURAGEMENT Individuals must get coverage. Large employers must offer coverage. Plans must offer specified “minimum essential health benefits” (MEHB) with no annual limits or exclusion of pre-existing conditions, in order to be a “qualified health plan” (QHP) listed on exchanges. Plans must meet “Medical Loss Ratio” (MLR) requirements.

8 The ACA landscape: FINANCIAL FLOWS APTC CSR “Three R’s”: Reinsurance, Risk Adjustment, Risk Corridors – spread out unknown risk across issuers in first few years Revenues: c.$12 billion allocated fee on insurance providers, c.$3.1 billion Medical Device Tax, various other small taxes.

9 The ACA landscape: DATA FLOWS SBMs and issuers send enrollment and payments data to federal data-sharing hub (“dish”). Data staged from Hub into MIDAS along with FFM data, then extracted by contractors into excel for end users. IRS form 1095 issued by SBMs to enrollees, with information necessary for their IRS tax returns. Issuers’ checks sent out from Treasury’s pay.gov

10 The ACA landscape: DATA CHALLENGES Healthcare.gov is probably largest federal customer-facing enrollment site. Under the hood, needed to build new “pipes” between 16 SBM states, FFM, 5 major federal agency systems (CMS, IRS, Treasury, SSA, DHS). Data system-building at the same time as expected to function. Multiple forms, formats, report/audit requirements, statutory/regulatory provisions.

11 Pilot example of potential Big Data-driven research Using analytics to gauge the potential for increasing plan selection in low- income areas Data sources: U.S. Census and HHS “Plan Selections by zip code in the Health Insurance Marketplace” (http://aspe.hhs.gov/health/reports/2015/M arketPlaceEnrollment/EnrollmentByZip/rpt_E nrollmentByZip_Apr2015.cfm

12 SENSITIVE – DO NOT DISTRIBUTE The “anomalous” quadrant: zip codes with below-median household income and below-median plan selections 12

13 Identify outliers: Zip Code 53233, “West Central Milwaukee” In this one zip code: Census Median Household income = $13,032 Census Tot. Households = 4,789 Plans actually selected = 158 Plans selected per 1,000 Census households = 33.0 If the households in 53233 were at the median zip codes’ rate of 64.6 plans per 1,000 households, there would be 309 plans selected in total, an increase of 151 additional plans, or 49% in this one zip code.


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