  Improper health insurance payments resulting from fraud and upcoding result
in tens of billions of dollars in excess health care costs annually in the
United States, motivating machine learning researchers to build anomaly
detection models for health insurance claims. This article describes two such
strategies specifically for ER claims. The first is an upcoding model based on
severity code distributions, stratified by hierarchical diagnosis code
clusters. A statistically significant difference in mean upcoding anomaly
scores is observed between free-standing ERs and acute care hospitals, with
free-standing ERs being more anomalous. The second model is a random forest
that minimizes improper payments by optimally sorting ER claims within review
queues. Depending on the percentage of claims reviewed, the random forest saved
12% to 40% above a baseline approach that prioritized claims by billed amount.
