Posts Tagged Medicare

by Gunter Wessels and Pam Shearman

The Focus of Comparative Effectiveness is Key to the Effectiveness of the Program

This blog is about Hospital Value and in this post we want to take a short step back from what could be, to what is already enacted: funding for Comparative Effectiveness (CE) research for the NIH under ARRA 09 at $1.1 Billion. This CE program, a small part of the >$100 billion slice of ARRA related to healthcare, was described as a cost saving measure. That’s a big order for a small check. In the end CE will affect hospitals through reimbursement changes.

The promise of CE is to inform and update Federal guidelines to support cost-effective options. Federal treatment guidelines turn into payment policy, and the market responds by implementing those guidelines to get paid.

The problem with CE as a score-able cost savings measure is CE will adjust Federal guidelines for treatment. Federal guidelines update very slowly; they are already years in the making and sometimes take decades to be adopted (for an example of a fast update to Federal guidelines see http://waysandmeans.house.gov/hearings.asp?formmode=printfriendly&id=2197 part of MMA03 and the K/DOQI guidelines from the National Kidney Foundation circa 1997; detail on the guidelines at http://www.kidney.org/professionals/KDOQI/). This situation will not be remedied by CE, because research is slow. The upside is that CE findings will hopefully help make better guidelines, so the glacial pace of Federal guideline development will eventually result in better medicine.

Focusing the program on the right diseases/treatments would generate a substantial ROI for healthcare policy makers, while freeing up some capacity and reducing costs for Hospitals. There is a Win-Win.

You get what you pay for…sort of.
Beyond the following we’re going to skip the discussion about “rationing.” In virtually every healthcare system on the globe, individuals are still able to purchase the level of care they desire, no matter how extreme or experimental. Those who cannot afford to purchase more than the standard of care are not subject to rationing, even if they believe so. Ask a Canadian if you disagree (after all we are being pelted with breathless praise for Canadian healthcare outcomes, which are objectively good). You’ll find that wait-times in the Canadian health system are despised up to the point where an immediate appointment costs money. Pay out of pocket or not. There is always choice. Furthermore, if you’ve scheduled an appointment with a specialist in the US, you’ve likely experienced a few weeks “wait time.” Is that rationing? No way.

Getting what you pay for is the real dilemma; enter CE. The theory behind CE research is solid.  Other countries with good healthcare outcomes as well as premier healthcare systems in the US dedicate resources to investigating how to deliver the best outcomes cost effectively. In the UK for example, the National Institute for Health and Clinical Excellence (www.nice.org.uk/aboutnice/) supports this goal.

Any purchaser of healthcare services should not have to pay the same rate for therapeutic interventions that have different outcomes. It makes less sense to pay more for poor outcomes, a sadly frequent situation (see www.hospitalvalueindex.com for analysis on this topic). The inability to determine which providers deliver more or less quality in healthcare has created the move toward transparency, quality measures, and public reporting; all good things.

But, quality measures only illuminate some pretty basic activities; did the provider do X, to people who need X; when the provider performed Y procedure to patients who need Y, did they have to come back because the outcome was poor? With the benefit of quality measures we still don’t know whether providers are implementing Evidence-Based medicine and/or standard of care for X and Y. Worse, the standard of care may not be cost effective; standards are consensus based, and not necessarily on the healthcare economics of care delivery.

Current market-based activities that support Comparative Effectiveness
Giving Americans access to good affordable care should be within our reach as a nation. CE studies would support this goal, but because there is a current crying need for a focused application of comparative effectiveness the marketplace has already responded.

For the last decade at least, a variety of treatment guides known as Clinical Decision Support Systems (CDSS) have been employed by larger, more integrated and sophisticated healthcare providers. These systems are built to enhance the ability to correctly diagnose disease and then identify the most effective therapy choices. As with any comparative effectiveness determination attempt, however, these systems are best able to update Evidence-Based Medicine treatment algorithms within their own “sand-box”, i.e. within the integrated systems. We wonder how ARRA’s NIH funding will build upon these already available resources and accelerate the adoption of what the best providers already know and use.

Focus Comparative Effectiveness Studies on Big Chunks of Spending
Focus in CE research is of utmost importance, because determining the relative benefits of different healthcare interventions is difficult and time-consuming. Making comparative effectiveness pay-off for payers like Medicare–within a meaningful time frame–requires attention to those disease states that could, if improved, give return for the investment.

There are basically three chronic disease states that consume the majority of Medicare and by association Medicaid spending–all three are related to lifestyle and patient compliance. They are Diabetes, Heart Failure, and Kidney Disease; According to the United States Renal Disease Statistics, in 2008 these diseases consumed over $200 billion of Medicare spend. Private payers spent over $15 billion on the same disease states (http://www.usrds.org/2008/slides/htm/vol1_05_costCKD_ESRD_08.swf.).

These three chronic disease states are the outcome of a long progression of disease, and consequently the problem gets worse with age. Sadly the proportion of patients being diagnosed early enough to slow or reverse progression is too low. Treatment is subject to the use of multiple drugs and interventions and patient monitoring is inadequate (adequate monitoring rates of less than 50% at best–e.g. Diabetes monitoring).

The ability to deliver a more effective care pathway in these diseases is a huge opportunity; simple laboratory tests coordinate care, but according to the USRDS these tests are under-utilized. Furthermore, the use of expensive anemia-controlling drugs costs billions of dollars each year, but the outcome of over-use of these drugs is death. The net result, as reported by the USRDS in kidney disease for example, is that huge costs result from common, preventable, under diagnosed, and under-treated conditions. (See www.usrds.org for more.)  

Establishing more comparative effectiveness standards in these three disease states alone would deliver substantial savings–in cost and quality of life.

Small Chunks; Individual Differences and Personalized Medicine Targeted Wisely
“Personalized-medicine” diagnostic tests are emerging with the ability to detect variations in a person’s DNA that control the speed and efficiency that the individual’s body “uses” the drug. It’s a very hot area in diagnostics, and it can benefit cancer patients, people with depression, as well as heart failure, kidney disease, and diabetes.

If enacted, CE will challenge regulators to see through the so-called “genetic flaw” in comparative effectiveness in more genetically variable disease states. The natural variation in people’s genetic makeup in certain areas–like glycemic response–causes certain therapies to fail because of individual genetic make up, rather than whether they are effective in the specific populations represented in randomized controlled trials.  Therefore, to aim CE studies and monies at less prevalent diseases, the “smaller chunks” of our healthcare spend, will require more genetic testing in a larger percentage of the population. These tests continue to evolve, are challenging to interpret, and are expensive, so they do not represent our most cost effective focus option.

A Pragmatic Solution
Fortunately, to contain the biggest costs, we don’t need elaborate genetic testing; we can rely on simple, inexpensive, and readily available laboratory tests for the most common and most costly diseases.

Policy makers should focus CE studies on the “Big Chunks”–Heart Failure, Kidney Disease, and Diabetes. Applying the best current knowledge of diagnostic testing, intervention, and monitoring standards for these diseases will pay back the $1.1 billion more quickly, and improve the lives of thousands of Americans.