There is an interesting paper in Health Affairs on what types of insurances are paying for increased opioid admissions and costs. I had a good discussion on Twitter with a health researcher about data validity due to risk adjustment. I am a bit wary of diagnosis based assessments because there is a major non-random shock to the data set for some classes of admissions that is not applicable to other groups.
— Walid Gellad (@walidgellad) June 5, 2016
@bjdickmayhew To be clear, any discharge diagnosis including opioid ICD-9’s included. so not necessarily primary diagnoses. but still a lot.
— Walid Gellad (@walidgellad) June 6, 2016
As an insurance plumber, one of the truisms is that we only get good, complete and complex data from providers when that data triggers money to the providers. This is why directory data tends to be bad. Most claims and claims systems pay on the CPT-4 procedure code. Most claims systems will require at least one ICD-9 or ICD-10 diagnosis code for payment. They can handle dozens of diagnosis codes, but paying a low dollar claim is seldom dependent on what particular diagnosis is submitted.
This matters for risk adjustment.
Medicare Advantage and Medicaid Managed Care Organizations (MCOs) operate in a world of risk adjustment. Medicare Advantage uses the HCC model which is a diagnosis based model. If a person gets a diagnosis of X on a clean claim, then the Medicare Advantage insurer gets a kick payment tied to the Diagnosis X. MCOs often are risk adjusted using similar non-HCC models that are overwhelmingly driven by diagnoses received on a claim. There are some models that take some pharmacy data into consideration. Some states run their risk adjustment programs similar to Medicare where a kick payment is made for each particular diagnosis category submitted, while others use a relative risk revenue neutral model like Exchange.
Risk adjustment where there is significant money at stake creates a strong incentive for risk adjusted insurers to aggressively chase diagnoses. Insurers will create chase lists and pay incentives to providers to close diagnoses. The incentive is to create an exhaustive list of defensible diagnoses. One of the projects that I am familiar with creates chases lists that dump directly into the Electronic Medical Record system of a major hospital and specialist provider group. A patient being discharged will have a list of possible conditions that were not primary treatment conditions that could be coded for it the doctor used that information in any of their decision making processes. Coding education and coding payments can lead to upcoding and fraud because there is money on the line.
Commercial/Employer sponsored insurance and pre-PPACA individual market insurance was not risk adjusted. For low dollar and medium dollar claims as long as the CPT4 codes made sense to the claims system’s logic and there was a valid diagnosis code on the claim, the claim would pay at the regular rate. There is no strong reason in the ESI world for insurers to care too much about the diagnosis coding intensity.
We have some evidence of the impact of risk adjustment on coding intensity as Fee For Service Medicare currently sees its covered lives as coded as 10% healthier than Medicare Advantage membership even though the underlying health status of the two populations are roughly the same.
According to a new paper by Richard Kronick and W. Pete Welch, upcoding by Medicare Advantage plans happens. Big time. This matters because Medicare Advantage (MA) plans are paid more for higher risk score enrollees….
Fee for Service Medicare has no risk adjustment incentive so the coding is lighter than Medicare Advantage.
What does this mean?
I have complete trust in the comparability of the data between ESI, Medicare and Medicaid if the diagnosis is the primary diagnosis that is driving the admission. That data will have some idiosyncratic variance but over a national claims universe, those things should wash out. However, I would be suspicious of the diagnosis data if it occupies the “other diagnoses” slots for Medicare Advantage and MCO Medicaid as those are the places where providers fill in their risk adjustment codes even if there was minimal involvement of those codes for decision making, treatment or evaluation purposes.
Ideally, large studies that attempt to attribute disease conditions to different payer categories would use a combination of CPT-4 procedure codes as those codes drive payment and thus drive clean provider created data and then diagnosis coding to clean the data after the first pass. As more and more of the world becomes risk adjusted through Medicare Advantage and MCO expansion, Exchange/small group SHOP, Medicare FFS Accountable Care Organization (ACO) build out and private sector ACO proliferation, the diagnosis data will become even muddier and less comparable between different payer groupings.