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PublicationGSA: A Framework for Rapid Prototyping of Smart Alarm Systems(2010-11-11) King, Andrew; Roederer, Alex; Arney, David; Chen, Sanjian; Fortino-Mullen, Margaret; Giannareas, Ana; Hanson III, C. William; Kern, Vanessa; Stevens, Nicholas; Viesca Trevino, Adrian; Park, Soojin; Sokolsky, Oleg; Lee, Insup; Tannen, JonathanWe describe the Generic Smart Alarm, an architectural framework for the development of decision support modules for a variety of clinical applications. The need to quickly process patient vital signs and detect patient health events arises in many clinical scenarios, from clinical decision support to tele-health systems to home-care applications. The events detected during monitoring can be used as caregiver alarms, as triggers for further downstream processing or logging, or as discrete inputs to decision support systems or physiological closed-loop applications. We believe that all of these scenarios are similar, and share a common framework of design. In attempting to solve a particular instance of the problem, that of device alarm fatigue due to numerous false alarms, we devised a modular system based around this framework. This modular design allows us to easily customize the framework to address the specific needs of the various applications, and at the same time enables us to perform checking of consistency of the system. In the paper we discuss potential specific clinical applications of a generic smart alarm framework, present the proposed architecture of such a framework, and motivate the benefits of a generic framework for the development of new smart alarm or clinical decision support systems. PublicationThe Medical Device Dongle: An Open-Source Standards-Based Platform for Interoperable Medical Device Connectivity(2012-01-28) Asare, Philip; Cong, Danyang; Vattam, Santosh G.; Kim, BaekGyu; King, Andrew; Sokolsky, Oleg; Lee, Insup; Lin, Shan; Mullen-Fortino, MargaretEmerging medical applications require device coordination, increasing the need to connect devices in an interoperable manner. However, many of the existing health devices in use were not originally developed for network connectivity and those devices with networking capabilities either use proprietary protocols or implementations of standard protocols that are unavailable to the end user. The first set of devices are unsuitable for device coordination applications and the second set are unsuitable for research in medical device interoperability. We propose the Medical Device Dongle (MDD), a low-cost, open-source platform that addresses both issues. PublicationChallenges and Research Directions in Medical Cyber-Physical Systems(2012-01-01) Lee, Insup; Sokolsky, Oleg; Chen, Sanjian; Hatcliff, John; Jee, Eunkyoung; Kim, BaekGyu; King, Andrew; Mullen-Fortino, Margaret; Park, Soojin; Roederer, Alexander; Venkatasubramanian, KrishnaMedical cyber-physical systems (MCPS) are lifecritical, context-aware, networked systems of medical devices. These systems are increasingly used in hospitals to provide highquality continuous care for patients. The need to design complex MCPS that are both safe and effective has presented numerous challenges, including achieving high assurance in system software, intoperability, context-aware intelligence, autonomy, security and privacy, and device certifiability. In this paper, we discuss these challenges in developing MCPS, some of our work in addressing them, and several open research issues PublicationSmart Alarms: Multivariate Medical Alarm Integration for Post CABG Surgery Patients(2012-01-01) Stevens, Nicholas; Giannareas, Ana Rosa; Kern, Vanessa; Trevino, Adrian Viesca; Fortino-Mullen, Margaret; King, Andrew; Lee, InsupIn order to monitor patients in the Intensive Care Unit, healthcare practitioners set threshold alarms on each of many individual vital sign monitors. The current alarm algorithms elicit numerous false positive alarms producing an inefficient healthcare system, where nurses habitually ignore low level alarms due to their overabundance. In this paper, we describe an algorithm that considers multiple vital signs when monitoring a post coronary artery bypass graft (post-CABG) surgery patient. The algorithm employs a Fuzzy Expert System to mimic the decision processes of nurses. In addition, it includes a Clinical Decision Support tool that uses Bayesian theory to display the possible CABG-related complications the patient might be undergoing at any point in time, as well as the most relevant risk factors. As a result, this multivariate approach decreases clinical alarms by an average of 59% with a standard deviation of 17% (Sample of 32 patients, 1,451 hours of vital sign data). Interviews comparing our proposed system with the approach currently used in hospitals have also confirmed the potential efficiency gains from this approach.