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Healthcare is an important aspect in everyday life, with quality and affordable care being essential for a population's well-being and life expectancy. Even so, associated costs for medical services continue to rise. One aspect contributing to increased costs in healthcare is waste and fraud. In particular, with the rapidly rising elderly population in the United States, programs like Medicare are subject to high losses due to fraud. Therefore, fraud detection approaches are critical in lessening these losses. Even so, many studies using Medicare data do not provide sufficient details regarding data processing and/or integration making it potentially more difficult to understand the experimental results and challenging to reproduce the experiments. In this paper, we present current research using Medicare data to detect fraud, focusing on data processing and/or integration, and assess any gaps in the provided data-related details. We then present discussions on important details to look for when processing and merging different Medicare datasets indicating opportunities for future work.
Bauder et al. (2018) studied this question.