Using Diagnoses to Describe Populations and Predict Costs - capitated payment system uses demographic risk adjusted to calculate payments

Health Care Financing Review, Spring, 2000 by Arlene S. Ash, Randall P. Ellis, Gregory C. Pope, John Z. Ayanian, David W. Bates, Helen Burstin, Lisa I. Iezzoni, Elizabeth MacKay, Wei Yu

NOTES: HIV is human immunodeficiency virus. AIDS is acquired immunodeficiency syndrome.

SOURCE: (Ash et al., 1998.)

CC Hierarchies

A payment model should not be sensitive to every diagnostic code recorded because this will result in poorly specified coefficients and unstable estimates of the relative risk of populations. For example, a female who has metastatic cancer (CC 5) could also be coded with cancer in two or more specific body sites, such as the liver (CC 6) or connective and soft tissue (CC 7). She may also have been tested for other "uncertain" (CC 10) or "benign" cellular changes (CC 12). A regression model that separately assigns credit for each of these diagnoses will have confounded parameter estimates, because the costs of people with only the simpler problems get averaged in, or confounded, with costs for people with both simple and more consequential conditions. Also, such models reward most the plans that capture as many codes as can be legitimately defended in an audit--a behavior with little social value. To dampen these incentives, we use hierarchies to constrain CC assignment as follows: a person classified into a CC is not also classified into a lower ranked CC in the same hierarchy. An important feature of an HCC model is that the hierarchies are not imposed across unrelated medical problems. For example, for a female with both cancer and diabetes, hierarchies are used to retain only the "worst" evidence of each disease, but both cancer and diabetes CCs are used in predicting her costs next year.

Hierarchies are identified for each CC in the rightmost column of Table A by indicating which CCs are dominated; dominated CCs are zeroed out for a person when a dominating CC is present.

The CC hierarchies capture both chronic and serious acute manifestations of particular disease processes, as well as their seriousness in terms of expected costs. Some hierarchies, such as neoplasm, are simple; CC 5 dominates CC 6, which dominates CC 7, all the way down to CC 12. Other hierarchies, such as gastrointestinal, are more complex, as illustrated in Figure 1. A person may be classified with either, or both, acute and chronic high-cost gastrointestinal problems; however, if either of these is coded, information about moderate or lower cost GI disorders is ignored.

[Figure 1 ILLUSTRATION OMITTED]

Clinically, hierarchies reduce the sensitivity of predicted payments to the coding of less serious manifestations of the same condition; statistically, they make explanatory variables more nearly orthogonal, increasing statistical precision. Imposing hierarchies typically increases the estimated coefficients and t-ratios of serious condition categories.

Excluded Condition Categories

We also exclude some CCs from the models entirely, by constraining their coefficients to be zero; the result is that the presence of that condition for an individual will not increase his or her predicted cost. Money that "disappears" from the prediction when a positive coefficient is constrained to zero is redistributed--generally reappearing as slight increments to demographic variables. Each model still accounts for the costs of treating all conditions.