Biomedical subjects

Randall P Ellis

Publications and source records attributed to Randall P Ellis.

6 recordsLinked to original sources

PubMed2006

Predictability and predictiveness in health care spending.

This paper re-examines the relation between the predictability of health care spending and incentives due to adverse selection. Within an explicit model of health plan decisions about service levels, we show that predictability (how well spending on certain services can be anticipated), predictiveness (how well the predicted levels of certain services contemporaneously co-vary with total health care spending), and demand responsiveness all matter for adverse selection incentives. The product of terms involving these three measures of predictability, predictiveness, and demand responsiveness define an empirical index of the direction and magnitude of selection incentives. We quantify the relative magnitude of adverse selection incentives bearing on various types of health care services in Medicare. Our results are consistent with other research on service-level selection. The index of incentives can readily be applied to data from other payers.

Forecasting↗

PubMed2005

Predicting pharmacy costs and other medical costs using diagnoses and drug claims.

BACKGROUND: Predicting health care costs for individuals and populations is essential for managing care. However, the comparative power of diagnostic and drug data for predicting future costs has not been closely examined. OBJECTIVE: We sought to compare the predictive performance of claims-based models using diagnoses, drugs claims, and combined data to predict health care costs. SUBJECTS: More than 1 million commercially insured, nonelderly individuals in a national (MEDSTAT MarketScan) research database comprised our sample. MEASURES: We used 1997 and 1998 drug and diagnostic profiles to predict costs in 1998 and 1999, respectively. To assess model performance, we compared R2 values and predictive ratios (predicted costs/actual costs) for important subgroups. RESULTS: Models using both drug and diagnostic data best predicted subsequent-year total health care costs (highest R2 = 0.168 versus 0.116 and 0.146 for models based on drug or diagnostic data alone, respectively), with highly accurate predictive ratios (0.95-1.05) for subgroups of patients with major medical conditions. Models predicting pharmacy costs had substantially higher R2 values than models predicting other medical costs (highest R2 0.493 versus 0.124). Drug-based models predicted future pharmacy costs better than diagnosis-based models (highest R2 = 0.482 versus 0.243), whereas diagnosis-based models predicted total costs (highest R2 = 0.146 versus 0.116) and nonpharmacy costs (highest R2 = 0.116 versus 0.071) more effectively than drug-based models. Newer models had markedly higher R values than older ones, largely because of richer data rather than model refinements. CONCLUSIONS: Combined drug and diagnostic data predicts total health care costs better than either type of data alone. Pharmacy spending is particularly predictable from drug data, whereas diagnoses are more useful than drugs for predicting other medical costs and total costs. Using even slightly more recent data can substantially boost model performance measures; thus, model comparisons should be conducted on the same dataset.

Actuarial Analysis↗

PubMed2004

Risk adjustment of Medicare capitation payments using the CMS-HCC model.

This article describes the CMS hierarchical condition categories (HCC) model implemented in 2004 to adjust Medicare capitation payments to private health care plans for the health expenditure risk of their enrollees. We explain the model's principles, elements, organization, calibration, and performance. Modifications to reduce plan data reporting burden and adaptations for disabled, institutionalized, newly enrolled, and secondary payer subpopulations are discussed.

Adolescent↗

PubMed2002

How profitable is risk selection? A comparison of four risk adjustment models.

To mitigate selection triggered by capitation payments, risk-adjustment models bring capitation payments closer on average to individuals' expected expenditure. We examine the maximum potential profit that plans could hypothetically gain by using their own private information to select low-cost enrollees when payments are made using four commonly used risk adjustment models. Simulations using a privately insured sample suggest that risk selection profits remain substantial. The magnitude of potential profit varies according to the risk adjustment model and the private information plans can employ to identify profitable enrollees.

Actuarial Analysis↗

PubMed2002

Cost-minimizing risk adjustment.

Conventional risk adjustment, which sets capitation payments equal to the average cost of individuals with similar observable characteristics, is not optimal if health plans can use private information to select low-cost enrollees. "Cost-minimizing risk adjustment" minimizes the sum of capitated HMO premiums plus FFS costs by balancing the gains from HMO cost efficiency against the overpayments that result from HMO selection. Estimations using privately-insured data suggest that cost-minimizing risk adjusted premiums reduce total sponsor costs as much as 25.6% below conventional risk adjustment premiums.

Actuarial Analysis↗

PubMed2002

Disease burden profiles: an emerging tool for managing managed care.

As health plans assume financial risk for providing health care services, effectively managing the health of a population remains one of the toughest challenges. This article shows how risk assessment methods can be used to measure disease burden in the full population and to discriminate levels of future health care needs within specific disease cohorts. We also examine and compare the predictive power of claims-based models within a diabetic cohort.

Chronic Disease↗