AMIA Annu Symp Proc - A healthcare utilization analysis framework for hot spotting and contextual anomaly detection.


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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.

Resumo Limpo

patient medic record today contain vast amount inform regard patient condit along treatment procedur record systemat healthcar resourc util analysi leverag observ data can provid critic insight guid resourc plan improv qualiti care deliveri reduc cost particular interest provid hot spot abil identifi time manner heavi user system pattern util target intervent program can institut anomali detect abil identifi anomal util case patient incur level util unexpect given clinic characterist may requir correct action past work medic util pattern analysi focus diseas specif studi present framework util analysi can easili appli patient popul framework includ two main compon util profil hot spot use vector space model repres patient util profil appli cluster techniqu identifi util group within given popul isol high util differ type contextu anomali detect util model map patient clinic characterist util level built order quantifi deviat expect actual util level identifi anomali demonstr effect framework use claim data collect popul diabet patient analysi demonstr use propos approach identifi clinic meaning instanc hot spot anomali detect futur work plan incorpor addit sourc observ data includ emr diseas registri develop analyt model leverag tempor relationship among medic encount provid indepth insight

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