A healthcare utilization analysis framework for hot spotting and contextual anomaly detection

Abstract

Patient medical records today contain vast amount of information regarding patient conditions along with treatment and procedure records. Systematic healthcare resource utilization analysis leveraging such observational data can provide critical insights to guide resource planning and improve the quality of care delivery while reducing cost. Of particular interest to providers are hot spotting: the ability to identify in a timely manner heavy users of the systems and their patterns of utilization so that targeted intervention programs can be instituted, and anomaly detection: the ability to identify anomalous utilization cases where the patients incurred levels of utilization that are unexpected given their clinical characteristics which may require corrective actions. Past work on medical utilization pattern analysis has focused on disease specific studies. We present a framework for utilization analysis that can be easily applied to any patient population. The framework includes two main components: utilization profiling and hot spotting, where we use a vector space model to represent patient utilization profiles, and apply clustering techniques to identify utilization groups within a given population and isolate high utilizers of different types; and contextual anomaly detection for utilization, where models that map patient's clinical characteristics to the utilization level are built in order to quantify the deviation between the expected and actual utilization levels and identify anomalies. We demonstrate the effectiveness of the framework using claims data collected from a population of 7667 diabetes patients. Our analysis demonstrates the usefulness of the proposed approaches in identifying clinically meaningful instances for both hot spotting and anomaly detection. In future work we plan to incorporate additional sources of observational data including EMRs and disease registries, and develop analytics models to leverage temporal relationships among medical encounters to provide more in-depth insights.

Figure 1:

Two-stage clustering for large patient population. The whole population is first over segmented to a set of micro-clusters, then the mean of each micro-cluster is extracted as representatives for further clustering using HAC.

Figure 2:

Mean utilization profiles of the identified clusters. Visit types: 1-PCP visit; 2-Specialist visit; 3-Emergency visit; 4-Inpatient hospital; 5-Outpatient hospital; 6-Patient’s home.

Figure 3:

Actual vs. expected utilization for three patients detected as contextual anomalies. Visit types: 1-PCP visit; 2-Specialist visit; 3-Emergency visit; 4-Inpatient hospital; 5-Outpatient hospital; 6-Patient’s home.