A recent article on risk adjustment by a group of researchers from Dartmouth attracted our attention. Entitled Observational intensity bias associated with illness adjustment: cross sectional analysis of insurance claims , it describes biases that are found in various types of healthcare risk adjustment processes, with the bias is being caused by an increasing number of physician–patient encounters.
Risk adjustment in bundled payments
Our article on this topic is the cover article in the July issue of Healthcare Financial Management and can be downloaded here. This article provides an overview of capitation payment, the types of providers who can be capitated, and the relationships between the financial statements and incentives between providers and capitated organizations. It also discusses metric
Here's an interesting article that's essentially about trying too hard to develop a comprehensive model for healthcare risk adjustment. It's from the Healthcare Economist blog entitled "Risk Adjustment: Overfitting the Model". It describes a situation in which parameters are introduced to a model in an attempt to qualtify the effect of a factor, but that end up being more sensitive to the characteristics of an individual person (because of small sample size) than the factor itself. Here's the link:
Participants in Accountable Care Organizations expect to be rewarded based on the success or failures of their clinical initiatives. However, a recent report in the New England Journal of Medicine suggests that the geographic adjustment factors apply by CMS may have a significant effect on ACO financial results. This article, entitled Implications for ACOs of Variations in Spending Growth describes situations in which the geographic region in which the ACO is located has a significant
The CMS guidelines for the Medicare bundled payment initiative suggest that CMS will be receptive to the use of risk adjustments in establishing the episode payment budget for DRGs to be paid under this program. This prompted the Singletrack Analytics research team to fire up its HCC grouper to explore how risk adjustment might be integrated into a bundled payment process.
In the draft ACO regulations, CMS established a single ACO risk adjustment factor using historical data from all participants, and did not modify it throughout the participation period. This policy reflected CMS’ concern that risk scores would increase due to changes in coding rather than actual changes in patient status, which would inappropriately lower the benchmarks and allow additional “savings” to be realized. Therefore, the risk score of the initial group of members would be applied throughout the participation period regardless of changes in the actual
The results of the Physician Group Practice (PGP) demonstration project have recently been released and are available from CMS here. Of interest in these results is the unevenness of the results. Of the 10 participating groups, one group received 52% of all shared savings payments, while three groups failed to achieve any shared savings.
A new report was recently released by CMS that measures the accuracy of the risk adjustment models that CMS utilizes for Medicare Advantage plans, the Physician Group Practice demonstration and others, and is expected to be used for Accountable Care Organizations (ACOs). This report provides some interesting insights into the effectiveness of risk adjusters.
Many recent healthcare initiatives, including the Accountable Care Organizations that are created under health reform, utilize risk adjusters to compensate for the differences in disease status of the members. These risk adjusters are used to establish payment targets that compare costs incurred by patients in the organization to what the costs “should be” based on those patients’ diagnoses. The accuracy of the risk adjusters, therefore, is critically important when significant amounts of money are involved. A small percentage difference between the risk-