Notes on Austin and Health Economics

I was in Austin Texas for the first time this week for the first in-person meeting of the American Society of Health Economists since 2019. Some quick impressions on Austin:

  • Austin reminds me of many Southern cities, but Nashville most of all. Both historic state capitals that are booming, lots of people moving in and new infrastructure actually being built, forests of cranes putting up new glass towers. Both filled with bars, restaurants, and especially live music. But even with so much happening and so much being built, they don’t *feel* dense, you can always see lots of sky even downtown.
  • Austin seems to be a bizarre “pharmacy desert”, I think I walked 14 miles all through town before I saw one. Contrast to NYC with a Duane Reade on every block. In fact downtown seemed to have almost no chains of any kind, restaurants included; I wonder if this is just about consumer preferences or there’s some sort of anti-chain law.
  • Good brisket and tacos, as expected
  • Most US cities have redeveloped their waterfronts the last few decades to make them pleasant places to be, but Austin has done particularly well here, many miles of riverfront trails right downtown.

Thoughts from the conference:

  • Health economics is rapidly growing in popularity as a field for new PhDs to study. I’ve always felt like a young person at conferences, but now as a 35 year old associate professor I’m suddenly old.
  • The conference required masks “except while eating, drinking, or speaking at a podium”, but there were many opportunities to do all of those. So if you’re wondering how worried health economists are about Covid now, the answer seems to be “more than most people but much less than last year”.
  • In the opening plenary on AI in health care, the panel was asked “what’s something most people get wrong about AI in health”. Ziad Obermeyer’s answer stood out because he said what he thought most people *in that room* get wrong- which is thinking that AI is over-hyped. While IBM Watson was, and many simple algorithms and regressions get sold as “AI” when they aren’t, Obermeyer thinks that the next decade will see a dramatic increase in the number profitable companies building useful applications of AI in health.
  • In his Presidential address David Cutler laid out 5 theories for “why US health care is so messed up”, then had the audience vote on them. The two winners, each with 38% of the vote, were “government failure” and “the patient is not the customer”.
  • Most poster sessions at conferences are bad because few people show up to look at the posters. The big exceptions I’ve seen are APEE and now ASHE, because they give out food and alcohol at the poster session.
  • Certificate of Need laws harm dialysis patients, according to this deep dive from new Columbia PhD David Rosenkranz
  • Many papers confirm that hospital systems are rapidly buying up doctors’ outpatient practices, all get different results as to whether this helps or harms patients
  • I’ve worried that disability insurance is more expensive than it should be because of adverse selection, but Abidah Abdulhadi finds that its actually advantageously selected (bought by unusually healthy people), which makes it cheaper

My biggest issue with the field of health economics right now is that almost every paper we write is backward-looking; we use data from past years to see how things worked, with the hope that this will inform us about the future. Maybe its weird to complain about this when that’s what I do in almost all my papers; I do think we learn a lot from it, it probably even makes sense to have it be most of the field. But I get nervous when any methodology starts to become the *only* way of doing things. Other methodologies like theory, forecasting, and even non-mathematical arguments seem to now be underrated.

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