Two interpretations of US health care cost vs. life expectancy

On the other day, Andrew Gelman posted this chart illustrating the high cost of US health care:

He did so to correct a “somewhat misleading (in my opinion) presentation of these numbers [that] has been floating around on the web recently.” The misleading graph, which appeared on a National Geographic blog, was — I agree — a confusing way to show information better represented in a scatterplot.

But I’ve seen this data before, and there’s more to the story. Neither the National Geographic nor FiveThirtyEight has anything to say about which numbers they’re charting.

Back in 2005, in a review of John Abramson’s excellent book Overdo$ed America, I noted that he had used a different source to reach a slightly different conclusion.

His chart, based on OECD health-expenditure data (link now 404) and WHO healthy life expectancy data (link still alive), looked like this:

He used it to make the oft-cited point that US healthcare isn’t just wildly expensive, but that it also correlates with worse life expectancy than in many countries that spend less.

I wondered what the chart would look like if based on the same OECD expenditure data but on the OECD’s rather than the WHO’s definition of life expectancy. The result looked like this:

The U.S. is the clear cost outlier on both charts. The first chart, however, places us near the low end of the life expectancy range, justifying Abramson’s assertion that we combine “poor health and high costs.” The second chart places us near the high end of the life expectancy range, suggesting that while value still isn’t proportional to cost, we’re at least buying more value than the first chart indicates.

Although based on older data, this second chart closely resembles the ones recently shown and discussed by the National Geographic and FiveThirtyEight.

My review of Abramson’s book concluded:

Has Abramson spun the data to make his point, just as he accuses the pharmaceutical industry of doing? Of course. Everybody spins the data. What matters is that:

  • Everybody can access the source data, as we can in the case of Abramson’s book but cannot (he argues) in the case of much medical research
  • The interpretation used to drive policy expresses the values shared by the citizenry

Would we generally agree that we should measure the value of our health care in terms of healthy life expectancy, not raw life expectancy? That the WHO’s way of assessing healthy life expectancy is valid? These are kinds of questions that citizens have not been able to address easily or effectively. Pushing the data and surrounding discussion into the blogosphere is the best way — arguably the only way — to change that.

That was five years ago. The data was, and is, out there. So it’s disheartening to see the same chart pop up again without any further discussion of the sources of its data, or of the definitions underlying those sources.

Talking with Peter O’Toole about gathering clinical data and sharing medical knowledge

My guest for this week’s Innovators show is Peter O’Toole from mTuitive, a company whose authoring toolkit for clinical data collection I featured in a 2006 screencast. mTuitive is working at the intersection of a number of disciplines that all need to come together to deliver cheaper and better health care.

First, usability. Designing clinical data gathering systems that capture what’s right for the patient, along with what’s mandated by the insurance company, requires a careful balancing of constraints and freedom in software user interfaces.

Second, knowledge engineering. Clinical systems don’t merely record data, they embody medical protocols that reflect an ever-changing consensus about methods and best practices. mTuitive’s authoring system aims to enable leading practioners to encode that knowledge in ways that can then guide others. But knowledge grows at the edge as well as at the center. So mTuitive also enables practitioners to extend and modify the software, injecting local knowledge and custom. Who owns this knowledge? Who’s liable for the consequences of its use? These are some of the implications we discussed.

Third, semantics. Electronic medical records are still mainly narrative in form, says Peter O’Toole. But we’re moving toward more computable ways of describing observations about, say, the nature and size of tumors.

Fourth, social software. My hunch, and Peter O’Toole’s too, is that progress toward the nirvana of medical records that are both semantically rich and interoperable will be powered by a two-stroke engine. One stroke of the piston will be driven by centrally-defined standards and centrally-imposed legislation. But the other will be driven by networked collaboration, at the edge, among doctors who pool and codify their experiential knowledge using ad-hoc, Web 2.0-like methods.