While risk adjustment is pervasive in the health care system, relatively little attention has been devoted to studying the fairness of these formulas for individuals who may be harmed by them. In practice, risk adjustment algorithms are often built with respect to statistical fit, as measured by p-values or R2 statistics. The main goal of a health plan payment risk adjustment system is to convey incentives to health plans such that they provide health care services efficiently , a component of which is not to discriminate in access or care for persons or groups likely to be expensive. In an attempt to accomplish this, risk adjustment formulas include indicators for the presence of health conditions associated with higher costs. The salient issue is that incentives mainly operate at a group level, not an individual level; plans can discriminate at the group level in ways they cannot at the person level. Because health plans providing sparse care for certain illnesses is a key policy concern, group-level fit is arguably one of the most important metrics for formula evaluation. Giving primacy on the basis of individual fit when group fit may be the larger concern can lead to harmful decision making. We therefore discuss the role of p-values and statistical fit for this policy problem while considering the fairness of the risk adjustment algorithm for vulnerable groups. Enrollees with mental health and substance use disorders have been found to be subject to the adverse incentives noted above. We apply our ideas to this vulnerable group with a group-level net compensation metricof the incentives to health plans to underprovide services .
Stepwise regression building procedures are commonly used applied statistical tools, despite their well-known drawbacks. While many of their limitations have been widely discussed in the literature, other aspects of the use of individual statistical fit measures, especially in high-dimensional stepwise regression settings, have not. Giving primacy to individual fit, as is done with p-values and R^2, when group fit may be the larger concern, can lead to misguided decision making. One of the most consequential uses of stepwise regression is in health care, where these tools allocate hundreds of billions of dollars to health plans enrolling individuals with different predicted health care costs. The main goal of this "risk adjustment" system is to convey incentives to health plans such that they provide health care services fairly , a component of which is not to discriminate in access or care for persons or groups likely to be expensive. We address some specific limitations of p-values and R^2 for high-dimensional stepwise regression in this policy problem through an illustrated example by additionally considering a group-level fairness metric .
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While risk adjustment is pervasive in the health care system, relatively little attention has been devoted to studying the fairness of these formulas for individuals who may be harmed by them. In practice, risk adjustment algorithms are often built with respect to statistical
Stepwise regression building procedures are commonly used applied statistical tools, despite their well-known drawbacks. While many of their limitations have been widely discussed in the literature, other aspects of the use of individual statistical fit measures, especially in high-dimensional stepwise regression settings, have not. Giving primacy to individual
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measured by
is done with
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or R2 statistics.
and R^2, when group fit may be the larger concern, can lead to misguided decision making. One of the most consequential uses of stepwise regression is in health care, where these tools allocate hundreds of billions of dollars to health plans enrolling individuals with different predicted health care costs.
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a health plan payment risk adjustment
this "risk adjustment"
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efficiently
fairly
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In an attempt to accomplish this, risk adjustment formulas include indicators for the presence of health conditions associated with higher costs. The salient issue is that incentives mainly operate at a group level, not an individual level; plans can discriminate at the group level in ways they cannot at the person level. Because health plans providing sparse care for certain illnesses is a key policy concern, group-level fit is arguably one of the most important metrics for formula evaluation. Giving primacy on the basis of individual fit when group fit may be the larger concern can lead to harmful decision making. We therefore discuss the role
We address some specific limitations
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statistical fit for
R^2 for high-dimensional stepwise regression in
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while considering the fairness of the risk adjustment algorithm for vulnerable groups. Enrollees with mental health and substance use disorders have been found to be subject to the adverse incentives noted above. We apply our ideas to this vulnerable group with
through an illustrated example by additionally considering
R
net compensation metricof the incentives to health plans to underprovide services
fairness metric