  There is strong interest among payers to identify emerging healthcare cost
drivers to support early intervention. However, many challenges arise in
analyzing large, high dimensional, and noisy healthcare data. In this paper, we
propose a systematic approach that utilizes hierarchical and multi-resolution
search strategies using enhanced statistical process control (SPC) algorithms
to surface high impact cost drivers. Our approach aims to provide
interpretable, detailed, and actionable insights of detected change patterns
attributing to multiple demographic and clinical factors. We also proposed an
algorithm to identify comparable treatment offsets at the population level and
quantify the cost impact on their utilization changes.
