The current intent to judge hospital performance and modify hospital payments based on relative rates of readmissions is not wise. Contrary to President Obama’s characterization that readmitting a patient to the hospital is equivalent to bringing a car back to the mechanic after a repair, rates of readmissions are based on a number of factors, of which a significant portion are services not provided by the hospitals and environmental conditions not controlled by the hospitals.
But let’s put my objections aside and determine how we would model an “appropriate” rate of readmissions. Well, a new article in JAMA* explores existing models, noting that robust models are needed “to identify which patients would benefit most from care transition interventions, as well as to risk-adjust readmission rates for the purposes of hospital comparison.” The article concludes that the capability for doing these things does not yet exist.
In “Risk Prediction Models for Hospital Readmission,” the authors state as their objective: “To summarize validated readmission risk prediction models, describe their performance, and assess suitability for clinical or administrative use.” Their conclusion, after reviewing two dozen such models, was that “Most current readmission risk prediction models that were designed for either comparative or clinical purposes perform poorly.”
On occasion, your correspondent fights the northeast’s dreary weekend winter evenings with a dram of spirituous liquor like Macallan 12. Unlocked with a small splash of water and a single ice cube, a generous ounce of that pungent cinnamon leathery elixir turns the cold into cozy.
So naturally, your correspondent relies on spouse to help keep a therapeutic stock available. Both yours truly and spouse run errands and it shouldn’t be too hard for either to be proactive by periodically checking supplies, buying some Macallan when necessary and avoiding the unhappiness of a dispirited and cold author.
Unfortunately, spouse doesn’t always see it that way.
Welcome to the complicated world of behavioral economics. It tells us that it’s difficult for persons to expend effort today to reduce the tomorrow’s risk of an unlikely event. It’s why many persons chose to not take or pay for medications today to reduce the distant likelihood of disability or early death. There’s more on the topic here.
This also explains why persons don’t do a good job getting a flu shot for themselves or their loved ones. Check out this interesting information from athenahealth. According to their pooled electronic health record (EHR) data, 2.5% of children without a flu shot came down with the flu, versus only 0.9% of those who got the shot. While getting a shot reduced the relative risk of coming down with the disease by approximately two thirds, the vast majority of kids who went without immunization (97.5%) did OK. Data from the CDC in adults reflects the same kind of numbers: 80% of persons in the U.S. do not come down with the flu in the course of the year.
How can the population health and care management community leverage behavioral economics to increase immunization rates?