Real-Time Analysis for Intensive Care: Development and Deployment of the Artemis Analytic System

• Published in: IEEE engineering in medicine and biology magazine 2010-03, Vol.29 (2), p.110-118
• Main Authors: Blount, M., Ebling, M.R., Eklund, J.M., James, A.G., McGregor, C., Percival, N., Smith, K.P., Sow, D.
• Format: Article
• Language: English
• Subjects: ARTEMIS satellite; Blood; Computer Systems; Critical Care; Decision Support Systems, Clinical; Diagnosis, Computer-Assisted - instrumentation; Displays; Electrocardiography; Electronics; Equipment Design; Equipment Failure Analysis; Health care; Heart rate; Indicators; Intensive care; Medical; Medical conditions; Medical Records Systems, Computerized; Medical services; Monitoring, Physiologic - instrumentation; Nurses; Patients; Pediatrics; Purchasing; Real time systems; Streaming media; Video on demand
• ISSN: 0739-5175; 1937-4186
• DOI: 10.1109/MEMB.2010.936454

The lives of many thousands of children born premature or ill at term around the world have been saved by those who work within neonatal intensive care units (NICUs). Modern-day neonatologists, together with nursing staff and other specialists within this domain, enjoy modern technologies for activities such as financial transactions, online purchasing, music, and video on demand. Yet, when they move into their workspace, in many cases, they are supported by nearly the same technology they used 20 years ago. Medical devices provide visual displays of vital signs through physiological streams such as electrocardiogram (ECG), heart rate, blood oxygen saturation (SpO 2 ), and respiratory rate. Electronic health record initiatives around the world provide an environment for the electronic management of medical records, but they fail to support the high-frequency interpretation of streaming physiological data. We have taken a collaborative research approach to address this need to provide a flexible platform for the real-time online analysis of patients' data streams to detect medically significant conditions that precede the onset of medical complications. The platform supports automated or clinician-driven knowledge discovery to discover new relationships between physiological data stream events and latent medical conditions as well as to refine existing analytics. Patients benefit from the system because earlier detection of signs of the medical conditions may lead to earlier intervention that may potentially lead to improved patient outcomes and reduced length of stays. The clinician benefits from a decision support tool that provides insight into multiple streams of data that are too voluminous to assess with traditional methods. The remainder of this article summarizes the strengths of our research collaboration and the resulting environment known as Artemis, which is currently being piloted within the NICU of The Hospital for Sick Children (SickKids) in Toronto, Ontario, Canada. Although the discussion in this article focuses on a NICU, the technologies can be applied to any intensive care environment.