Marion Blount
ABSTRACT
As a result of advances in software technology, particularly stream computing, it is now possible to implement scalable systems capable of real-time analysis of multiple physiological data streams of multiple patients. There is a growing body of evidence showing that early onset indicators of some medical conditions can be observed as subtle changes in the physiological data streams of affected patients. These real-time healthcare analytics systems can detect the early onset indicators and thus may result in earlier detection of the medical condition which may lead to earlier intervention and improved patient outcomes. Blood draws and nasal suctioning can cause changes in the values of some physiological data stream elements. Such events, sometimes referred to as physiological stream artifacts can cause the real-time analytics systems to generate false alarms since the systems assume each data element is indicative the patient's underlying physiological condition. In order to minimize the generation of false alarms, artifact events must be captured and integrated in real time with the analytics result. We present the summary of an artifact study in a tertiary neonatal intensive care unit within a children's hospital where a real-time analytics system is being piloted as part of a clinical research study. We utilize the information gathered relating to the nature of these events and propose a framework to integrate the artifact events with the analytic results in real time
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Index Terms
- On the integration of an artifact system and a real-time healthcare analytics system
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