DRG Summary for Medicare Hospital Payments

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SUMMARY – Medicare Hospital Payments 

Medicare does not rely on hospital billings but on data built over decades as to the reasonable cost of services. Some may question the absolute amount of Medicare reimbursements but the relative payment scales are extensively validated by actual data.  Conversely, this analysis shows hospital pricing has inconsistencies that cannot be rationally explained.

However, private insurers negotiate discounts from these hospital pricings. If billed prices are inconsistent, then so are discounts based on them. A major constraint on medical costs will occur when patients can make informed cost decisions at the DRG level, not just for overall premiums and co-pays. Currently, few persons can make those informed decisions.

Many states have enacted legislation for hospitals to be more transparent about their prices, but enforcement is spotty.  This Medicare data suggests that the country would be well served if hospitals posted DRG prices for all to compare.


In May, 2013 Medicare released its most comprehensive set ever of statistical data regarding hospital payments.  The data covered fiscal year 2011 and included the top 100 DRG’s (diagnostic related codes) based on inpatient discharges. Data excludes DRG’s for hospitals with fewer than 11 discharges for that DRG. This allows focus on higher volume services and their financial impact.  The final data set of the top 100 DRG’s results in over 166,000 records of nearly 7 million discharges from over 3,300 hospitals.

The data itself lists for DRG’s for each hospital, the number of discharges, the average covered (billed) charges, and the average total payment including Medicare. Each hospital, also includes its HRR (hospital referral region) which is the method governments use to determine “market areas”.

The chart below from Kaiser Foundation indicates that inpatient hospital is just over a quarter of Medicare spend or about $140 billion annually.


This analysis examines inpatient service pricing. Step one was to reduce the extreme data, both high and low. To minimize billing overstatement, this analysis removed 51 discharges that were high cost outliers. To minimize billing understatement, the smallest states totaling 10 % of the population and which tend to be more rural and variable were skipped. The sample data covers 6.3 million discharges from over 145,000 records of 100 DRG bills and costs. Total inpatient payments are $61 billion or 40% of total spend.

The data itself was analyzed five different ways.

  1. Percent of average paid vs. average billed, grouped by percent paid quartile
  2. Percent of average paid vs. average billed, grouped by state
  3. Variance from average of billed charges, grouped by state
  4. Variance from average of paid charges, grouped by state
  5. Extremes of 15 largest DRG groups expressed as a ratio of the maximum to minimum billed, along with the number of discharges included in each group


% average paid vs average billed, by % paid quartile

The graph below shows 5 sets of bars representing four quartiles 0% to 100% plus a small number of DRG’s that paid more than was billed. The left (gold) bar is the average bill for the four quintiles while the right (blue) bar is the corresponding average paid for each group. The right axis shows average dollars per discharge. Total average billed dollars is $36,384 and ranges from $54,000 highest to $11,867 lowest. Total average paid dollars is $9,754 and ranges from $14,481 highest to $9,548 lowest.

Note the inverse relationship of billed versus paid. One might expect higher billings to result in a lower percentage paid. What was not expected is that the actual dollars paid goes up as the overbilling goes down closer to paid dollars. Clearly billings for lower cost DRG’s bear little resemblance to cost.


% average paid vs. average billed, grouped by state

The graph below uses the same payment data above but groups results by state.  And rather than two separate bars for billed and paid, there is one bar representing the percent of bill paid. (i.e. paid/billed) equivalent to the blue bar above.


This graph does highlight the extent of overbilling by state. It does not show either the billed amounts or the paid amounts.  The graph begins with the states with the highest overbilling (and hence lower paid percent) and extends to more realistic levels of overpricing. Maryland at the bottom has billed prices very close to paid, with only a 6% discount to bill.

In the above graph, Illinois payments of 27% billed is the average for these 30 states. States listed above Illinois have more severe overpricing issues than states following Illinois.

“Discounts” from billed rates can have serious side effects. Just to call them discounts is something of a misnomer.  For many, there seems little connection between what it costs and what is billed.  Medicare of course ignores billed prices and pays what the procedures cost plus a margin.  But private insurers do not have the extensive national database that Medicare has. Instead they negotiate “discounts” from billed or list price. But as this graph shows, and as one drills down deeper by hospital, these list prices are all over the map, and that alone can skew private insurance payment amounts.

But two other adverse factors also come into play. The most important is that billed rates are what uninsured people are charged when they require treatment. Most of the uninsured cannot afford the insurance, and should they be hospitalized, things get far worse. Over 60% of personal bankruptcies have medical bills as a significant factor.

Another adverse factor is that hospitals report the amount of uncompensated care that they provide, and are provided tax exempt status if that care exceeds a specific target, and/or get reimbursed for some of these expenses. The computations are far from transparent, and it is quite possible that taxes are avoided or reimbursements received that overstate actual uncompensated care were it calculated as Medicare does.

Variance from average of billed charges, by state

The graph below offers a more close-up view of overbilling. It shows how each state’s average dollar amounts differ from the 30 state billing average of $36,384.


Data is sorted from the most overbilling at the top to the least at the bottom. Note that Massachusetts, which state closely resembles the Affordable Care Act, has less overpricing (though still 50%) than all other states except Maryland.

Variance from average of paid charges, by state

The graph below is the same format as the prior except using paid instead of amounts. Its scale is also much lower. In the former graph, Maryland had the least overbilling. But as shown below, Maryland has the second most expensive payments following only slightly behind California.

This graph, more than any other highlights the cost-of-living differences between different parts of the country. Larger urban states tend to have higher costs than smaller less urban states. Nevertheless, the $5,000 difference between the extremes reflects costs nearly double from the lowest cost states to the highest.  The financial effect (+30%/-40) seems larger than justified by differences in cost-of-living alone.


The most obvious difference would be intensity where higher cost states are able to justify more services. Another factor could be the use of more expensive equipment and methods.

Extremes of 15 largest DRG groups expressed as a ratio of maximum to minimum billed, along with the number of discharges included in each group

The graph below represents two different data, each with its own range of values.  The grouping is a selection of 15 of the most frequent DRGs. The wider (green) bars have their value scale shown along the top. The wide bar represents the ratio of the maximum billing divided by the minimum billing – in other words, the ratio of maximum to minimum, the extremes of over-pricing. For instance, the second DRG, “Cellulitis” has its highest billing more than 70 TIMES that of the lowest bill. Bad as that is, the extreme for septicemia is over 100 times the lowest billing. These are extreme differences for closely related illnesses. Sure there are differences in how serious the illness is, but high-low factors greater than 50, not even considering ratios greater than 100 are hard to explain.


Then there are the narrow (gold) bars. They represent the number of discharges in each DRG group and whose values are shown below the graph. There are over 3.6 million discharges in the data.  One may reasonably conclude that hospital pricing bears little relationship to costs of service. While deep discounts mitigate some of this, discounting just reduces the magnitude but not the irrational pricing itself.

Download PDF Report >>> Medicare Hospital Payments

Link to Medicare Provider Charge Data

Response to Essential Health Benefits Bulletin

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Health and Human Services (HHS) Bulletin sets guidelines for defining Essential Health Benefits (EHB). It ingeniously allows each State to have a say in its own EHB definition, yet provides a method to bring closure to the process should any State not reach an agreement. It also allows States to add benefits, but at their own expense. With federally providing premium assistance to lower income enrollees, it is important that only minimum State EHB premiums be supported.

The bulletin will likely require every State to add or enhance some services that are not now offered to small groups and individuals. This may lead to a premium increase for small groups and individuals not eligible for premium assistance.  Until actuarial efforts identify these costs, this remains an unanswered issue.  Everyone is concerned about higher costs, but Insurers have added concerns about adverse selection. The Affordable Care Act (ACA) mitigates this concern by reinsurance and risk adjustment provisions in the act. Continue reading

Income Disparity and Sources of Income

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Regardless of how one views the Occupy Wall Street (OWS) movement and “We are the 99 percent”, it has succeeded in raising awareness about wealth distribution. Also in October, 2011, the Congressional Budget Office (CBO) published an analysis of household incomes addressing that subject.

 CBO first grouped households by quintiles (1/5 in each) and clearly the top fifth have done far better than the bottom fifth. CBO then divided the top 20% (fifth) into 10%, 5%, 4%, and the highest 1% of households. The income disparity was even more striking with the highest 1% far greater than the others. 

This analysis further drills down within this top 1% and finds income disparity wider yet, and vastly greater than all other group comparisons. Pre-tax income for the top 1/100 of 1%, (or 12,000 households) totaled some $450 billion, greater than the combined pre-tax income of the 24 million lowest income households. To rephrase, income of just one of these richest households is more than 2,000 lowest income households. Continue reading

Worsening Inequality of Wealth and Incomes


In October 2011, the Congressional Budget Office (CBO) released an analysis at the request of the Senate Committee on Finance. The analysis documents changes in household income distribution from 1979 to 2007. That analysis titled “Trends in the Distribution of Household Income notes that the share of average after-tax income for the top 20% gained, while the lower 80% declined seen in Summary Figure 2.

Further, within the top 20%, the share of after-tax income of the top 1% grew from less than 18% to over 30% of the top 20% income bracket. While actual incomes for all quintiles increased, only the share of total after-tax income of the top 1% increased. The 81-99% remained essentially flat while the lower 80% of all households declined over 28 years.   Continue reading

Senate Gridlock – the Filibuster Factor – Update

This Analysis has been replaced with a new version: Senate Filibusters Reveal Deliberate Obstruction


Government Medical Spend Forecasts

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Open Google Motion Chart >>> Total Public Medical Spend

Open Google Motion Chart >>> Medicare Spend


There is a close relationship between the current concerns over government debt and prior years’ controversies over the Affordable Care Act (ACA) or health reform.  The link between them is that Medicare costs will rise significantly above current levels. That will put pressure on entitlement spending that will be nearly impossible to offset with other spending cuts.  Medicare itself will need significant reform.

However, the principal factor behind the enormous cost increases is the huge increase in the senior population.  The population for those 0 to 54 years is expected to rise just 16% between 2010 and 2050.  The population of those 55 and over is expected to grow nearly 70% over that same period and the older the age, the greater the increase.  This major shift in the aging population is the main cause of higher Medicare costs. Higher per-capita costs just add to the problem. Continue reading

Health Care Reform – Accountable Care Organizations

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Kaiser health news recently came out with two documents providing clarification on Accountable Care Organizations or ACO’s that were included in the Affordable Care Act (ACA).

The mainstream media rarely  discussed this. It comprised only seven pages of the health care law and dwelt  with Medicare to which few critics paid serious attention.

For providers of health care, this offers a major change in the way Medicare operates. It delivers care at lower cost while maintaining quality.   The ACO model can also apply to all patients, not just Medicare.  Savings while maintaining quality care can run into the hundreds of billions of dollars.

A study of 4,272 hospitals found utilization levels at two of five most expensive hospitals more than 30% greater than at Mayo Clinics. The study covered Medicare patients who died. If that same service ratio held for all patients, those hospitals could generate annual savings of $170 Billion with no change in prices. The savings occur if they had hospital days and physician visits similar to Mayo. Continue reading