Intensive Care Cloud, is based on previous efforts that led in the design and development of Intensive Care Window, ICW software application. ICW is an open-source solution composed of a middleware framework (medical device bedside controller) and an end-user application. The middleware enables communication with intensive care unit bedside-installed medical devices over standard and proprietary communication protocol stacks. The ICW application facilitates the acquisition of vital signs and physiological parameters exported from patient-attached medical devices and sensors. Moreover, ICW provides runtime and post-analysis procedures for data annotation, data visualization, data query, and analysis. The ICW application can be deployed as a stand-alone solution or in conjunction with existing clinical information systems providing a holistic solution to inpatient medical condition monitoring, early diagnosis, and prognosis.

Venus-C project represent a promising venue for addressing the computational challenges and provide a scalable on demand solution in regards to data storage, data analysis and data mining.

User scenario

ICW is deployed as a bedside controller as a single PC application. Current deployment faces severe limitations in processing power and storage capabilities.

Real time data acquisition and analysis (vital signs, physiological parameters, waveforms, etc) requires:

  • Huge data storage capabilities. An estimate of the amount of data that would be generated daily is given in the following scenario. Suppose naive sensor tuple composed of 16 Bytes (4 bytes=> Who, 4 bytes => When, 4 bytes => What, 4 bytes => Value) and that there is only 1 hospital with 10 beds, where each bed has 20 sensors. Assuming that each bed is used for 24 hours per day and the system retrieves data every 15 seconds, the data collected amounts to ~17 MB per day. This number only represents the data from a basic set of the sensors and settings. Additional information includes metadata, ļ¬gures, waveforms etc.

  • Near real time diagnosis. Real time analysis to achieve complementary or computer-aided diagnosis includes signal processing and data mining over a huge data pool.

Proposed Solution

Venus-C platform will provide a solid solution in both aforementioned issues. In order to overcome on-premise infrastructures and storage limitations Intensive Care Cloud shall exploit Venus-C data storage capabilities. Also, real time diagnosis could be achieved utilizing Venus-C parallel processing capabilities and elasticity feature.