We propose an approach to identify high health care utilizers using residuals from a regression-based health care utilization adjustment model to analyze the variations in health care expenditures. Using a large administrative claims dataset from a state public insurance program, we show that the residuals can identify a group of patients with high residuals whose demographics and categorization of comorbidities are similar to other patients but who have a significant amount of unexplained health care utilization. Additionally, these high utilizers persist from year to year. Correlation analysis with 3M™Potentially Preventable Events (PPE) software shows that a portion of this utilization may be preventable. In addition, these residuals can be useful in predicting future PPEs and hence may be useful in identifying impactable high utilizers.