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Comparative healthcare systems as an argument for ...

Well! After we summarized some web chatter on universal coverage, John and Theora smacked one another on the US vs. European and government vs. private sector debates (scroll down to the comments).

While looking at other countries is provocative and useful for learning, it does not in itself form an argument for one mechanism over another. There is sufficient rationale for universal coverage independent of how the US may compare to other countries.

Both John and Theora trotted out some data to support their points and much of it was interesting. For example, John pointed to an OECD presentation here. Note, in particular, page 18 which shows health-adjusted life expectancy of 60 year old male and females in seven countries, including the US and UK. The life expectancies for the US and UK are practically identical. But John had already noted the higher costs in the US. If I understood his point correctly though, he was only referring to Medicare data which, since it would not include private and Medicaid expenditures, would understate the spending differences.

But none of the data were sufficient to convince me either way. The back and forth between the two of them did remind me of a related debate just a few months ago regarding the much improved quality of care in the VA system. That was used by some as an argument that a single government run system would be superior. And they were off and running there too.

Here was my response to the VA discussion: a pox on both your houses. That argument should apply here too.

 

More on variation

John Wennberg, MD and his colleagues continue to set the pace in an entire sector of health services research. A whole new generation of medical service researchers is extending Wennberg's work on variation, particularly including Elliott Fisher, MD, MPH, Therese Stukel, Wennberg's son, David Wennberg, MD and others. Their early work led to their establishing Dartmouth's Center for Clinical Evaluative Sciences, where they publish the Dartmouth Atlas of Health Care. They have made huge contributions to our understanding of medical service use. Most importantly:

The provision of health services is far from standardized. There are dramatic variations in care, sometimes many-fold, even after taking into account such factors as differences in age, gender, illness, and coverage.

Higher service levels do not equate with improved access, do not necessarily generate improved clinical outcomes, and do not lead to higher patient satisfaction, but of course they do lead to high expenditures.

About 15 years ago, I did several years worth of work on variation in hospital use in New York, primarily focused on Medicaid clients. Care of Medicaid clients was and remains highly variable. (If you'd like to see some of that work, send me an e-mail.) The original work was inspired by and based on the same methods as Wennberg. I asked at the time, "what differences do these differences make?" Since then Fisher and some of the others have written that the differences don't lead to improved outcomes or patient satisfaction. Wennberg, Fisher and their colleagues have shown that the differences in service use do yield big differences in spending. For another example, check here.

The latest work of this group, just been published in Health Affairs, is here. Particularly for those of us in New York, which is dominated by Academic Medical Centers, this one is a big deal. For example, New York's Mt Sinai has a length-of-stay double that of the Mayo Clinic. Wennberg and his colleagues looked at Medicare beneficiaries who were cared for in the last six months of life at brand-name hospitals. Highlights include:

Striking variations across hospitals in resource inputs and use of services. This includes variation in frequency of hospitalization, use of intensive care units, physician services

High correlations between patterns of care in the last six months of life with patterns of care in prior periods for the same patients with chronic illnesses.

High correlations between service use patterns for different chronic illnesses, i.e., hospitals that have high use rates for one condition tend to have high rates for other conditions. For example, they show a strong correlation between service use patterns for cancer and CHF.

Consistency of hospital-specific use rates over time.

Differences in mix of primary care physicians and specialists.

In terms of policy and operations, what does this mean?

The availability of provider-specific information should open up new opportunities for the active management of FFS health care.

Provider-specific information that profiles performance in managing chronic illness may be useful in implmenting provisions of the Medicare, Prescription Drug, Improvement and Modernization Act (MMA) of 2003.

Ultimately, success in the "longitudinal" management of populations with chronic illness will depend on the integration of care across various sectors: acute care hospitals, primary care, nursing home care, home health care, and hospice care. (I would particularly emphasize this for Medicaid clients as well as Medicare.)

Why this type of thinking is not at the center of health policy generally, Medicare policy, and Medicaid policy in New York, and every other state for that matter, is beyond me.

If it works for cops, can it work for docs?

New York City's Compstat program has been in use for a decade now and is spreading throughout the country. The essence is "statistics driven" resource deployment. Crime rates went down. When we do the same, we'll see changes in healthcare too.

More on baseball and Medicaid

Last November, I wrote an op-ed for the New York Post. It's no longer online, but here's the gist of it:

For decades, New York officials distressed with the cost of its Medicaid program have compared it to California's, which serves more people for less money. A more instructive lesson can be drawn by comparing the performances of their baseball teams.

As each usually does, the Oakland Athletics and the Yankees once again won their division titles this year. But it cost the Yankees three times as much in payroll. How does Oakland do it? In "Moneyball, the Art of Winning an Unfair Game," Michael Lewis credits General Manager Billy Beane - who hires statisticians who examine reams of baseball data to figure out what really wins.

New York needs a Billy Beane for its Medicaid program, someone who treasures the data and builds a team around it. Never mind the Medicaid differences between New York and California, the differences within New York are even greater - and we can do something about them.

The second baseball lesson is this: At the end of the ball game, we know who won and who lost. Not so in Medicaid, which measures hits but not runs. We know if the patient went to the hospital, but not if she got better.

In baseball, geeks and science are replacing tobacco-chewing coaches and common wisdom. With Gov. Pataki playing General Manager like Billy Beane, we can do the same with Medicaid. Let's redefine what a Medicaid "win" really is, and use the data to figure out how to do it. Then let's train new coaches and players so that a decade from now we can watch some real major league Medicaid.

For you baseball geeks (and I know you're out there) and others of you who would like to speculate on what we could be doing with Medicaid data, take a look at Sabernomics, Economic Thinking about Baseball. All we need is to be able to access Medicaid data like we do baseball data.