Stroke remains the third most frequent cause of death in the United States and the leading cause of disability in adults. Long-term effects of ischemic stroke can be mitigated by the opportune administration of Tissue Plasminogen Activator (t-PA); however, the decision regarding the appropriate use of this therapy is dependant on timely, effective neurological assessment by a trained specialist. The lack of available stroke expertise is a key barrier preventing frequent use of t-PA. We report here on the development of a prototype research system capable of performing a semi-automated neurological examination from an offsite location via the Internet and a Computed Tomography (CT) scanner to facilitate the diagnosis and treatment of acute stroke. The Video Stroke Assessment (VSA) System consists of a video camera, a camera mounting frame, and a computer with software and algorithms to collect, interpret, and store patient neurological responses to stimuli. The video camera is mounted on a mobility track in front of the patient; camera direction and zoom are remotely controlled on a graphical user interface (GUI) by the specialist. The VSA System also performs a partially-autonomous examination based on the NIH Stroke Scale (NIHSS). Various response data indicative of stroke are recorded, analyzed and transmitted in real time to the specialist. The VSA provides unbiased, quantitative results for most categories of the NIHSS along with video and audio playback to assist in accurate diagnosis. The system archives the complete exam and results.
KEYWORDS: Data modeling, Software development, Computing systems, Human-machine interfaces, Computer architecture, Computer simulations, Systems modeling, Data archive systems, Data communications, Signal detection
Current patient monitoring procedures in hospital intensive care units (ICUs) generate vast quantities of medical data, much of which is considered extemporaneous and not evaluated. Although sophisticated monitors to analyze individual types of patient data are routinely used in the hospital setting, this equipment lacks high order signal analysis tools for detecting long-term trends and correlations between different signals within a patient data set. Without the ability to continuously analyze disjoint sets of patient data, it is difficult to detect slow-forming complications. As a result, the early onset of conditions such as pneumonia or sepsis may not be apparent until the advanced stages. We report here on the development of a distributed software architecture test bed and software medical models to analyze both asynchronous and continuous patient data in real time. Hardware and software has been developed to support a multi-node distributed computer cluster capable of amassing data from multiple patient monitors and projecting near and long-term outcomes based upon the application of physiologic models to the incoming patient data stream. One computer acts as a central coordinating node; additional computers accommodate processing needs. A simple, non-clinical model for sepsis detection was implemented on the system for demonstration purposes. This work shows exceptional promise as a highly effective means to rapidly predict and thereby mitigate the effect of nosocomial infections.
Diabetes management involves constant care and rigorous compliance. Glucose control is often difficult to maintain and onset of complications further compound health care needs. Status can be further hampered by geographic isolation from immediate medical infrastructures. The Home Care Interactive Patient Management System is an experimental telemedicine program that could improve chronic illness management through Internet-based applications. The goal of the system is to provide a customized, integrated approach to diabetes management to supplement and coordinate physician protocol while supporting routine patient activity, by supplying a set of customized automated services including health data collection, transmission, analysis and decision support.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.