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There is strong interest among payers to identify emerging healthcare costdrivers to support early intervention. However, many challenges arise inanalyzing large, high dimensional, and noisy healthcare data. In this paper, wepropose a systematic approach that utilizes hierarchical and multi-resolutionsearch strategies using enhanced statistical process control (SPC) algorithmsto surface high impact cost drivers. Our approach aims to provideinterpretable, detailed, and actionable insights of detected change patternsattributing to multiple demographic and clinical factors. We also proposed analgorithm to identify comparable treatment offsets at the population level andquantify the cost impact on their utilization changes.

A Systematic Approach to Detect Hierarchical Healthcare Cost Drivers and Interpretable Change Patterns