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We compare and demonstrate the effectiveness of two clustering methods with the main purpose of identifying characteristic profiles of high utilizers of health care. In this work, we use three sets of mutually independent longitudinal data that are nationally representative of the US adult working-age civilian non-institutionalized population. We compare k-means, a commonly used clustering method, with a k-medoids algorithm called Partitioning Around Medoids. We use one cohort of data to create clusters based on similar characteristics of individuals for both clustering methods. We examine these characteristic compositions of the highest three average total expenditure clusters from this cohort. We also examine the health expenditure distributions for this cohort over the following two years. We validate the approach by applying the centers of the clusters to two other cohorts of similar data. We form clusters based on demographic, economic, and health-related characteristics that are commonly used in studies of health care utilization. We demonstrate the consistency of our results across the three cohorts of data and across different types of health expenditures, such as office-based/outpatient and drug. Clusters can be formed with other more homogeneous data, such as Medicaid, Medicare, employer sponsored insurance, or individual private plans issued under the Affordable Care Act. This approach can be used to follow similar groups over time for other types of health outcomes.

Black women tend to be diagnosed with breast cancer at a more advanced stage than whites and subsequently experience elevated breast cancer mortality. We sought to determine whether there are racial differences in tumor natural history that contribute to these disparities. We used the University of Wisconsin Breast Cancer Simulation Model, a validated member of the National Cancer Institute's Cancer Intervention and Surveillance Modeling Network, to evaluate the contribution of racial differences in tumor natural history to observed disparities in breast cancer incidence. We fit eight natural history parameters in race-specific models by calibrating to the observed race- and stage-specific 1975-2000 U.S. incidence rates, while accounting for known racial variation in population structure, underlying risk of breast cancer, screening mammography utilization, and mortality from other causes. The best fit models indicated that a number of natural history parameters must vary between blacks and whites to reproduce the observed stage-specific incidence patterns. The mean of the tumor growth rate parameter was 63.6 % higher for blacks than whites (0.18, SE 0.04 vs. 0.11, SE 0.02). The fraction of tumors considered highly aggressive based on their tendency to metastasize at a small size was 2.2 times greater among blacks than whites (0.41, SE 0.009 vs. 0.019, SE 0.008). Based on our simulation model, breast tumors in blacks grow faster and are more likely to metastasize earlier than tumors in whites. These differences suggest that targeted prevention and detection strategies that go beyond equalizing access to mammography may be needed to eliminate breast cancer disparities.

A health care measure that assesses the occurrence or non-occurrence of an event may be costly to obtain. For instance, measuring whether an inpatient hospital stay was necessary according to accepted clinical protocols is a relevant, though expensive analysis. Prior research (Rosenberg, 1998; Rosenberg, et al., 1999; Rosenberg and Griffith, 2000) has shown that a Bayesian logistic regression model is an excellent tool to predict the rate of Non-Acceptable inpatient hospital Claims (NACs). This paper describes a decision-theoretic approach to determine whether a change in the NAC rate warrants investigation. The Bayesian model is used to estimate, by principal discharge diagnosis, the posterior distribution of the NAC rate for a group of claims at the time of the audit (defined as time 0). This distribution of the NAC rate is compared against another posterior distribution of the NAC rate obtained at a later time (without further audit). Claim and investigation costs are included in the assessment of whether an investigation of a change in the NAC rate should be undertaken. This approach provides an inexpensive, supplemental level of control between audits for monitoring the level of the NAC rate. The methodology is also applicable to other health care measurements.

The Medicare program, private insurers, and managed care organizations reimburse hospitals for inpatient admissions using the Diagnosis Related Group (DRG). The DRG is determined from a complicated algorithm based on patient medical records. Previous studies generated concerns about ‘DRG upcoding’, in which incorrect DRG codes may be selected by the hospital to obtain higher reimbursement. Insurers rely on expensive manual audits of claims to verify the appropriateness of the DRG coding. A statistical system that can adaptively detect claims with incorrect DRG codes would provide a powerful improvement to current practice. This paper describes two aspects of the statistical system that provides proof that the concept is viable. The first aspect of the paper is the design of a hierarchical Bayesian model to be applied to claims data (without audit) to estimate the probability that a claim is coded incorrectly. The second aspect of the paper is the use of the Bayesian model to aid in the selection process of claims to audit by proposing that a claim should be investigated if the predicted recovery is more than the cost of auditing that claim. This approach improves upon that used currently by auditing 88% of the claims and recovering 98% of the overpayments. While these results improve upon the current approach for determining which claims to investigate, they are based on data that have been systematically selected for audit based on one insurer's past experience. Future work will create an adaptive system to determine the selection of claims to audit from the entire paid claims database, and that can be generalized for use by other insurers.