Applying real-time analytics to data streams in digital health

Abstract

In this presentation we will describe the benefits of real-time analytics, specifically complex event processing technology, in addressing a number of challenges in digital health applications. We will focus on three uses cases. The first use case is a real-time detection of unusual or inappropriate laboratory orders, the problem which leads to significant and unnecessary costs to many healthcare providers. The second use case is the detection of potential data quality issues associated with source systems in pathology labs, by using a novel idea of applying syndromic surveillance method to the legacy clinical data streams, achieved through statistical analysis of pathology messages to identify “outliers”. The third use case is about supporting clinicians in making timely decisions regarding patient care, taking into account a combination of real-time information about patient conditions and their existing medical conditions taken from electronic health records. We will demonstrate how we used the EventSwarm software framework for complex event processing to support this real-time analytics and will discuss a number of future research directions.