  There is strong interest among healthcare 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 search
strategies and 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
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. To illustrate our approach, we apply it to the IBM Watson
Health MarketScan Commercial Database and organized the detected emerging
drivers into 5 categories for reporting. We also discuss some findings in this
analysis and potential actions in mitigating the impact of the drivers.
