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تسجيل مستخدم جديدA Systematic Approach to Detect Hierarchical Healthcare Cost Drivers and Interpretable Change Patterns
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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.
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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 s
ystematic 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.
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