Subject: Colfax Managed Care Group Florida Medicaid Bid Projected Medical Trend Recommendation

Hello Ben, Lingyi, and Yinhua,

My role in Colfax Managed Care Group’s Florida Medicaid bid was to determine the projected medical cost trend assumption. Our managed care group (MCG) feels that there are strong benefits to be had from managing this Medicaid program, and to do so we need to submit a desirable bid that will beat out the other competing MCGs. These benefits include profit for our MCG as well as more efficient, higher quality, and lower cost health care for our clients. After acquiring and analyzing historical consumer price index (CPI) data, I recommend setting the medical cost trend assumption at 5.02%. This projected medical cost trend is similar to other projections found through additional research and thus should allow our estimates to be attainable if our bid is accepted.

Background

Colfax MCG wishes to manage the Florida Medicaid program in 2016. In order to so, we must be confident that our bid is able to compete with other MCGs and that our estimates are attainable. Our bid will be competitive with other submissions if our costs are kept low, so that the state can pay us less than other MCGs to manage the same program. The attainability of our estimates is important because if our liabilities are projected too low, then we will end up having to pay more to cover charges in excess of our estimates. Our incentive for managing this program is its potential profitability and thus we need to strike a balance between profitability and attainability, which I believe can be done best by setting a slightly conservative estimate.

Regional and National CPI Connection

In order to set the projected medical cost trend, I thought it made the most sense to examine the CPIs of the four main medical claim types included in our program: inpatient, outpatient, physician, and behavioral health. The CPI is arguably one of the best methods to measure inflation because it is defined by the prices paid by consumers for a basket of goods and services within a given category. First, I checked to see if there existed a strong correlation between the medical care CPIs of the average U.S. city and the south urban area (containing Atlanta, GA, and two Florida regions) because CPIs for the specific claim types are only recorded on the national level. Below is a graph that displays both the annual average CPI and annual change for the medical care category for both populations.

20062007

20082009

20102011

20122013

20140.00%0.50%1.00%1.50%2.00%2.50%3.00%3.50%4.00%4.50%5.00%

0.0100.0200.0300.0400.0500.0600.0700.0800.0900.01000.0

Medical Care CPI Trend

U.S. City AverageSouth Urban

Year

Annu

al C

hang

e

Annu

al A

vera

ge C

PI

Source: http://www.bls.gov/data/#prices

One is able to see from the graph above that there is a close correlation between the CPIs for the two populations. I felt this was enough to justify that there exist close relationships within the CPIs for the specific medical categories between the regional and national level.

Medical Trend Projection Declaration

By assuming this close correlation exists between the regional and national level CPIs, further analysis was able to be completed on the four different claim types. Since our bid is being submitted for 2016, I felt it necessary to only include the previous five years of data to avoid projecting from less relevant years. Furthermore, using data from 2010 and onwards made sense because that’s when the Affordable Care Act (ACA), which standards are being followed by our program, was enacted. To start, the change in CPI for each year was computed for each claim type. As you can see below in the graph, there appears to be a downward trend in the CPI annual change for each claim type. This finding is further supported through an article found online, which quotes the following: “However, when broken down by the percentage increase, it is also clear that the growth in the per capita cost of health care has dramatically slowed in recent years.” (Source: http://www.factcheck.org/2014/02/aca-impact-on-per-capita-cost-of-health-care/)

2010 2011 2012 2013 20140.00%1.00%2.00%3.00%4.00%5.00%6.00%7.00%8.00%9.00%

10.00%

CPI Annual Change by Claim Type

InpatientOutpatientPhysicianBehavioral HealthWeighted Average

Year

Annu

al C

hang

e

Source: http://www.bls.gov/data/#prices

Because of this steep downward trend, I decided it was best to weight the most recent data heavier because it should be more indicative of what’s to come in the near future.

After examining the 2013 fee-for-service expense data for the state of Florida that is provided in the table below, one can see that the prominence of the claim types vary greatly.

Claim Type Total 2013 Claims Relative Weight Weighted AverageInpatient $ 1,455,182.59 66.38% 5.61%Outpatient $ 488,298.10 22.27% 4.87%Physician $ 195,046.67 8.90% 1.98%Behavioral Health $ 53,721.52 2.45% 1.44%Total $ 2,192,248.88 100.00% 5.02%

Source: http://www.bls.gov/data/#pricesSource: Week 1 – Medicaid data_Financial Statement.xlsx

Thus, in order to reflect this variance in our total medical cost trend, I weighted each claim type’s average by its relative weight in terms of its cost during 2013.

From the data and analysis above, I have settled on the projected medical cost trend of 5.02%.

Research Comparison

After searching online to compare our projection with others, I found one study in particular that appeared to relate to our bid the most. This study was done by the Segal Group and they estimate a 5.8% medical trend for 2015 in the southern region. (Source: http://www.segalco.com/infographic/health-benefit-cost-

trends-2015.pdf) I still favor my estimate because as was stated in their study, there is a wide range in trend by region and perhaps a different projection even closer to mine exists for the individual state of Florida.

In Conclusion, I recommend that we project a medical cost trend of 5.02%. This projection should help our bid’s competitiveness while maintaining the desired balance between profitability and attainability. One thing to keep in mind is that this projection is applied three times to the 2013 data and thus any error in the estimate is magnified. However, I feel strongly that this projection best reflects the recent trend and our fee-for-service data. I advise that we attempt to gather more data and if our bid is accepted, assess how well our assumptions hold and how they may need to be tweaked in the future.

Let me know if you have any questions.

Thanks,

Alexander Peterson