The Medicare Advantage Money Grab
Organisation: The Center for Public Integrity (United States)
Publication Date: 04/10/2015
DescriptionCongress expected Medicare Advantage plans, mostly run by major health insurers, to constrict rising costs and help curb fraud, abuse and waste plaguing traditional Medicare. But a yearlong Center for Public Integrity investigation found that rather than slow health-care spending, Medicare Advantage plans have sharply driven up treatment costs in some areas—larding on tens of billions of dollars in overcharges and other suspect billings. Taxpayers are taking a soaking from abuse of an obscure government billing formula called a “risk score” that is supposed to pay plans more for sicker patients and less for healthy ones. “The Medicare Advantage Money Grab” revealed nearly $70 billion in “improper” Medicare payments to the health plans from 2008 through 2013. The investigation exposed how federal officials missed multiple opportunities to corral tens of billions of dollars in billing errors tied to inflated risk scores. And, it showed how the industry has turned to home visits and sophisticated “data mining” analysis of patient medical records to raise risk scores even further with little government oversight. It also noted that Medicare’s problems policing risk scores doesn’t bode well for the success of the Affordable Care Act, which is using a similar payment system. Our analysis of risk score growth used government data for the first time to plot changes in risk scores at more than 5,700 health plans in 3,000 counties nationwide between 2007 and 2011. To explain risk scores, we created an interactive graphic that allowed readers to manipulate a risk score by changing diagnoses for a fictional patient to raise or lower the simulated cost. The calculations were carefully reconstructed from code, data and formulas released piecemeal by CMS and originally intended for use by health plans. Months of research and over 1,200 lines of code are behind this interactive. We also created an animated graph of how risk scores changed over four years for thousands of plans, which readers could filter for just the plans in their county, with data points that provide more details.
Technologies used for this project:We obtained three databases essential for tracking Medicare Advantage plans and assessing their costs and how risk scores have changed over time. All three data sources are available for free download from the Center for Medicare and Medicaid Services website. The three data sources were combined into a relational database, allowing us to combine tables based on common county identifiers and common Medicare Advantage plan IDs. Without combining the three data sets, the data sets would not allow tracking potential inflation in risk scores over time. The essential analysis was in the comparison between Medicare Advantage and traditional fee-for-service Medicare. A key to understanding cost differences between MA and traditional Medicare lies in comparing the risk score in each plan and each county. To accomplish the comparison, MA data were weighted by insurance plan county enrollment as Fee-For-Service (FFS) risk score data were only available on the county level. Weighted risk scores were calculated by taking county total enrollment, further broken down by the MA providers’ total enrollment in that county, and weighting the risk score by that number. We weighted the payments by county enrollments, similar to methodology used by the Medicare Payment Advisory Commission (MedPAC). To compare annual payments instead of monthly costs between MA and traditional Medicare, the weighted base payment for each county for MA plans and the FFS base payments were multiplied by 12. From this initial analysis, we calculated the total, rising costs of MA over traditional Medicare. Further, we could calculate which Medicare Advantage plans cost taxpayers more than traditional Medicare by factors well above what gaps Medicare officials expected between the plans, allowing us to identify specific plans and counties with out-of-control MA costs that escalated over the five years of data. This allowed us to work backwards to understand the effect of rising risk scores, key to seeing how MA plans code differently than in traditional Medicare. The result showed that in the last three years of our data, the cost to taxpayers was more than $10 billion per year and escalating. Microsoft SQL Server was used to import, clean and join the various data tables in the three data sets. Once finished, the combined data tables were exported to Microsoft Excel for the necessary weighting and subsequent analysis.
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