Allison Bell at LifeHealthPro has a couple of concerns about the risk adjustment process for the Exchanges. I don’t think they are big concerns:
Privacy concern:
One possible threat is consumer privacy concerns. Consumers may not be thrilled to learn that insurers are assigning them risk scores.
I don’t think this is a strong concern. Every insurer has some means of identifying who is healthy and who is not, and then how unhealthy a person is unlikely to be. There are dozens of vendors that have systems that crawl through claims and other data in an attempt to predict which individuals will have preventable big claims in the future. I have three meetings before Christmas concerning one particular vendor whose analysis is actually better than what their sales people promised.
Any insurer that is involved in any other guaranteed issue offering (CHIP, Medicaid, Medicare Advantage) will also have some type of government risk scoring system because population health will change capitated revenue. Any insurer that is involved in a gain sharing or ACO provider model has some type of risk adjustment. It is a fact of life.
Revenue neutrality means more insurer on insurer fighting:
In the new PPACA HHS-RADV system, all of the payments to insurers have to come from other insurers. Insurers have an incentive to “upcode” their enrollees, but other insurers have an incentive to detect the upcoding, or to leave the individual and small-group market if they feel the risks of having to pay into the risk adjustment program outweigh the potential benefits of being in the individual and small-group markets.
This to me is far more interesting. Medicare uses a non-revenue neutral model. An additional diagnosis of diabetes means the insurer gets a payment bump for the incrementally diagnosed individual. The incentive here is to maximize coding to maximize revenue.
Exchange, and some state Medicaid programs use revenue neutral risk adjustment models. That model changes insurer incentives towards a red queen race:
The problem is insurance companies that elect not to chase their providers for higher diagnosis coding intensity will see their risk scores look comparatively lower compared to high intensity coding companies, and thus they’ll lose money. So everyone has to do it, or no one has to do it. Right now we are in a stable, socially negative equilibrium of high intensity claim coding for risk score optimization.
Red queen racing is already happening in other revenue neutral risk adjustment programs. This is not a change in behavior only a change in breadth of a behavior. I don’t think her concern that insurers will be fighting other insurers is a big deal (besides for popcorn farmers as they’ll get to go on vacation more often). State and federal risk pool administrators don’t care too much as the money is not coming out of their budgets. Insurers know the data best. They’ll know that when they look at the statistics that there is no way in hell Insurer X is covering every single known hemophiliac in the country despite what their risk score is saying about the incidence of very severe hematological conditions. Instead, they’ll appeal the data and discover that a single provider had been miscoding low level diagnosistic claims as their biller was flipping codes once every three hundred submissions by a software error.
The insurers are the entities that are closest to the data, so they are the entities that are most likely to be able to call bullshit in real time. I don’t see this as a bad thing.
MomSense
IIRC you covered in previous posts that a large percentage of us are low utilizers and that a smaller percentage of us are accessing the majority of health care services. Is there some constructive way that insurers and providers could use risk assessments for good? I can imagine offering financial incentives to the providers and patients who embark in a serious smoking cessation or weight loss/exercise program. There are other populations who could benefit from nutritional counseling, stress reduction, etc.
Richard Mayhew
@MomSense: Oh definately, risk adjustment can be used for good.
One recent example at work. That vendor who has a really nice product identified a cluster of kids with young moms who they thought were at high risk of being high utilizers for minimal medical need. We split the sample into two, and sent a social worker and a home nurse to visit one group. The big issue was lack of parenting knowledge. That group of kids over the next six months had slightly higher than average ER visit usage, higher PCP utilization than expected and overall slightly higher than normal costs.
The non-intervention group had 3x expected ER utilization, half the PCP utilization, half the vaccinations compared to expected, and no overnight hospital admissions at 3x expected costs.
The same applies to connecting Type 2 diabetics to care managers. Not all Type 2 diabetics need a care manager, some have their diseases well managed but some people need a lot of help. So using a diagnostic based risk scoring system to figure out who is a Type 2 diabetic and then crosswalking that data to the prescription data to see who is filling their prescriptions on a regular basis allows a focus to be made on the people who are not filling their prescriptions.
MomSense
@Richard Mayhew:
With a little financial incentive to the participants to encourage programs like the one you mentioned, I can see some big improvements in care and cost especially for known risk factors like smoking, obesity, etc.