I’m reading (listening, actually) to the recently released SuperFreakonomics by Steven Levitt and Stephen Dubner. It’s the sequel to the best selling Freakonomics in which the tools of economic analysis are used to evaluate scenarios that we do not typically think of as economics problems.
One of the topics in the new book is healthcare quality. The book has a fantastic product placement for Microsoft’s Amalga (and kudos to whomever it was at Microsoft who thought to hand these guys a few terabytes of data). Amalga, coupled with social security records (for information on patients dying after leaving the hospital), provides the authors with fantastic amounts of data to work with. They have more information than any organization I’ve seen, which makes the conclusion that I carry away from their analysis even more surprising.
There is no way to systematically measure the quality of doctors.
This seems crazy to any of us paying attention to the healthcare space. Hospitals are measuring and reporting thousands of quality metrics. It seems that every government agency that touches healthcare along with every certification or designation program has its own list of items that must be tracked and communicated.
There’s even pressure to adjust reimbursements based on quality.
Further, communicating quality is a foundation for the empowerment of health consumers. If consumers can’t determine the quality of care being given at one facility or by one physician over another, how can they ever make better care decisions?
So why do Levitt and Dubner come to this conclusion? The answer is selection bias.
Sicker patients end up with different doctors and at different facilities than less sick patients. Multiple comorbidities associated with a condition that one physician may treat often pushes that patient to a different physician or, in the case of surgical care, the surgeon may choose to perform surgery at a hospital rather than an outpatient surgical center.
Some of this selection bias is perfectly reasonable. For example, a heart patient with an unusual issue may be referred to another specialist who focuses on treating that specific issue.
Unfortunately, as the stakes for “making the numbers” grows, there are greater concerns about whether or not we’re tracking the right numbers and the potential for unforeseen consequences.
A Wall Street Journal Op-ed piece from earlier this year presents the risks in making quality a high-stakes game for providers. The concerns range from the potential for insurers to be mandating sub-optimal treatment to physicians who drop patients that don’t respond to treatment or fail to adhere to treatment regimens, thereby damaging that physicians’ outcome statistics.
While we need to continue to push for improvement and standards for healthcare, how we go about doing so is critically important. Measuring the wrong things can be worse than measuring nothing at all.