Random sampling of paid medicare claims has been legally acceptable for investigating suspicious billing practices by health care providers since 1986. A population of payments made to a given provider during a given time frame is isolated and a probability sample selected for investigation. A lower confidence bound for the total amount overpaid to the provider is then used as a recoupment demand. Edwards et al. (Health Serv Outcomes Res Methodol 4:241-263, 2005) show that methods based on the Central Limit Theorem can fail badly and propose an alternative method, called the minimum sum method, for fixed sample sizes. In this paper the sampling is performed in two stages. In case of little abuse in the first stage the investigation is stopped; otherwise a second sample is examined. Based on this strategy a lower confidence bound for the total number of universe payments in error and a corresponding lower bound for the total overpayment amount are defined. Criteria for choosing the sampling parameters are considered. Relative efficiencies are studied.
We consider closed sequential or multistage sampling, with or without replacement, from a lot of N items, where each item can be identified as defective (in error, tainted, etc.) or not. The goal is to make inference on the proportion, π , of defectives in the lot, or equivalently on the number of defectives in the lot D = Nπ. It is shown that exact inference on π using closed (bounded) sequential or multistage procedures with general prespecified elimination boundaries is completely tractable and not at all inconvenient using modern statistical software. We give relevant theory and demonstrate functions for this purpose written in R (R Development Core Team 2005, available as online supplementary material). Applicability of the methodology is illustrated in three examples: (1) sharpening of Wald's (1947) sequential probability ratio test used in industrial acceptance sampling, (2) two-stage sampling for auditing Medicare or Medicaid health care providers, and (3) risk-limited sequential procedures for election audits.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.