Using information on clinical conditions to predict high-cost patients.

Predicting health care expenditures is important for health care research, policy, and practice. In health care delivery settings, interest often focuses on identifying potential high-cost patients, who could be enrolled in case-management or disease-management programs. Moreover, when used as a means of risk adjustment to improve comparability of different groups, predictions of high-cost cases are useful for evaluating clinical interventions or the effects of policy changes. Expenditure prediction models typically incorporate information on clinical conditions based on data from medical records or claims databases, in addition to demographic information (gender and age). The diagnostic cost group (DCG) system is one system for predicting health care expenditures based on ICD-9 codes obtained from insurance claims databases. “DCG models use age, sex, and diagnoses … to infer which medical problems are present for each individual and their likely effect on health care costs for a population” (Ash et al. 2000, p. 7). Although software to estimate DCG models is available commercially, some potential users may not have the resources or expertise to run such software and may instead prefer simpler algorithms. One possible alternative algorithm is to focus on the presence of certain conditions that are prevalent and chronic; indicators of such conditions may provide sufficient prediction of expenditures (Farley, Harley, and Devine 2006; Baser, Palmer, and Stephenson 2008; Charlson et al. 2008;). Another substitute could be a count of chronic conditions. Several studies have found that simple counts of diagnoses or conditions are associated with mortality or other outcomes (Melfi et al. 1995; Farley, Harley, and Devine 2006;). To what extent does using such simpler algorithms result in a loss of predictive power, compared with DCG risk scores? A second issue pertains to the utility of incorporating self-reported health status in risk-adjustment models. Several studies have examined the extent to which patient-reported health status information improves the performance of expenditure prediction models (Hornbrook and Goodman 1995; Pope et al. 1998; Pietz et al. 2004; Maciejewski et al. 2005;). Adding scales from the SF-36 health status measure (Ware and Sherbourne 1992) to predictive models increased the adjusted R2, but by small amounts (Hornbrook and Goodman 1995; Pietz et al. 2004;). Generalization from these studies is limited by their use of nonrepresentative samples, such as VA patients, Medicare beneficiaries, or working-age members of a single HMO; the VA samples included relatively few women, and the HMO sample few elderly patients. Fleishman et al. (2006) used a nationally representative sample to estimate predictive models that included the SF-12 health status measure (Ware, Kosinski, and Keller 1996). SF-12 summary scores were significantly associated with expenditures, controlling for demographic characteristics and specific chronic conditions. A model including one general health status question instead of the full SF-12 performed nearly as well. However, these analyses included indicators for only six highly prevalent conditions. It is unclear whether the same results would be obtained if richer information on clinical conditions had been included in the predictive models. The literature on risk adjustment is large (Iezzoni 2003). However, relatively few studies systematically compare the performance of different risk adjusters when predicting high-cost cases. Some studies compare the DCG risk score to the Charlson comorbidity index or the SF-36 but use mortality or some other clinical indicator as a criterion, not expenditures (Ash et al. 2003; Baldwin et al. 2006; Fan et al. 2006;). Relative performance may differ when predicting different outcomes (Perkins et al. 2004). Other studies do examine expenditures as an outcome, but they rely on data from selected samples, such as VA patients with diabetes (Maciejewski, Liu, and Fihn 2009), HMO enrollees with hypertension (Farley, Harley, and Devine 2006), or patients with private insurance treated for migraine (Baser, Palmer, and Stephenson 2008). The current study uses nationally representative data from the Medical Expenditure Panel Survey (MEPS) to compare expenditure prediction models. We examine three approaches to incorporating clinical condition information: the prospective risk score generated by the DCG algorithm; indicators of specific prevalent chronic conditions; and a count of the number of chronic conditions. We focus on chronic conditions because their associated expenditures may be more predictable than those for acute conditions, and because a large proportion of total aggregate health care spending is associated with care of chronic diseases (Anderson and Horvath 2004; Machlin, Cohen, and Beauregard 2008;). In addition, extending the results of Fleishman et al. (2006), we examine the additional predictive power contributed by a global health status question and two measures of functioning. We focus on individuals who have high expenditures. Specifically, we examine the extent to which predictive models can identify individuals with “high future costs,” that is, costs in the upper 10 percent of the subsequent year's expenditure distribution. While various thresholds for defining “high cost” have been used, the upper 10 percent is common (e.g., Farley, Harley, and Devine 2006).