David Cutler at JAMA Forum lays out four fairly technocratic changes to the US healthcare system to make it better. I want to engage on one of them as there is a significant implementation challenge in the form of network adequacy regulation. It is fixable but it needs to be addressed.
First, systems can encourage their clinicians to specialize in particular types of patients or procedures. It has long been known that volume matters for outcomes—for example, a surgeon who does 100 operations a year typically has better outcomes than one who does 20. In addition, a recent study found that specialization matters. For several common procedures, including coronary artery bypass graft surgery and valve replacement, physicians in the top quartile of specializing in a given procedure have mortality rates that are 15% to 46% lower than those in the lowest quartile of physicians specializing in that procedure.
The evidence is strong that volume matters. Insurers that have a long shadow of the future should want to send as many of their covered lives to hospitals and doctors that do a lot of a particular procedure. This is probably easier to do in urban areas rather than rural areas for two reasons. First, urban areas have far more people within a given radius who need a particular service. Secondly, urban areas are more likely to have multiple hospitals or doctors so specialization is far easier to support economically. A clinical oncologist at Duke University Hospital can specialize in lower GI tract cancers while a clinical oncologist on the Outer Banks treats everything from skin cancer to pancreatic cancer to brain cancers on any given afternoon.
An insurance company constructing networks that prioritize high volume and specialization runs into a major regulatory challenge; network adequacy regulations are means of state regulators and private accreditation agencies to determine if a network is good enough. Most of these regulations are based on either a specific percentage of the covered population has to be within a particular distance to a particular type of provider or there must be one provider of a given type for every so many thousand people.
The underlying assumption behind either a radius method or a ratio method of network adequacy is that any provider in a particular specialty is close enough to identical to another provider. These methods are quality agnostic.
This is problematic is an insurer thinks that it can build a high quality network based on centers of excellence and a few specialists who do a lot of repeated procedures in order to maximize their learning by doing as well as maximizing the division of very skilled and expensive labor. Such a network could have a large, dense and widespread network of primary care physicians and first line specialists. However it would be a network that routinely has patients driving past half a dozen hospitals to get to a local center of excellence. Or it would be a network that has patients flying halfway across the country for a back surgery consult.
These types of network designs happen in self-insured corporate plans. Walmart will only pay for spine surgery at a small number of specialty hospitals across the country now. These are high end hospitals like Mayo Clinic and Geissenger. The idea is to minimize variation in treatment and seek out the best care possible. The cost savings comes from reducing re-treatment and properly steering people who don’t need surgery out of the surgical pathway.
Self-insured plans are very loosely regulated. A center of excellence model can easily pass regulatory muster there. However fully insured plans where the insurance company has the full risk of an OMG claim, or government plans tend to have ratio or radii network adequacy rules. These rules make a center of excellence model far harder to implement. There could be value in short term waivers to pilot demonstration models that exempt some plans from network adequacy requirements on a few deferrable services and specialties to see if there is a way to hold cost constant and improve quality or decrease costs while holding quality constant.