Heuristic Evaluation of Data Integration and Visualization Software Used for Continuous Monitoring to Support Intensive Care: A Bedside Nurse`s Perspective Ying Ling Lin 1-3 , Anne-Marie Guerguerian 3-5 Peter Laussen 3,4 and Patricia Trbovich 1,2,4,6* 1 Institute of Biomaterials and Biomedical Engineering, University of Toronto, Canada 2 Centre for Global eHealth Innovation, University Health Network, Canada 3 Interdepartmental Division of Critical Care Medicine, Hospital for Sick Children, Canada 4 Faculty of Medicine, University of Toronto, Canada 5 Neuroscience and Mental Health Research, Hospital for Sick Children, Canada 6 Institute of Health Policy, Management and Evaluation, University of Toronto, Canada * Corresponding author: Patricia Trbovich, Institute of Biomaterials and Biomedical Engineering, Faculty of Engineering, University of Toronto, Canada, Tel: +1-416-340-4800 ext. 7180; Fax: +1-416-340-3595; E-mail: Patricia.Trbovich@uhn.ca Received date: Aug 01, 2015; Accepted date: Sep 22, 2015, Published date: Sep 30, 2015 Copyright: © Lin YL, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Abstract The Intensive Care Unit (ICU) is a complex and technologically advanced healthcare setting. Technologies enable continuous monitoring through patient signals that are sensed, recorded and displayed at the bedside. Although such technologies have significantly decreased mortality rates in the ICU, the large amounts of data have contributed to clinician information overload. Critical care nurses spend more than half of their time scanning and assimilating information from disparate monitors, at the bedside to assess the patient status. Software that integrates and allows visualization of large data sets on a single screen are now available. In the present study, we evaluated software entitled T3™ (Tracking, Trajectory and Triggering). Such computationally powerful software has great potential to support nurses’ monitoring and decision-making tasks but the usability, efficiency, and effectiveness of the software are key to end-user adoption. As such, we conducted a Heuristic Evaluation, where the study’s evaluators interacted with the software interfaces and were asked to comment on it by describing the usability issues and if they were in compliance with established usability principles, or heuristics, specifically for medical device interfaces. A total of 50 usability issues associated with 194 heuristic violations were found. Identified issues included difficulty with choosing the time period of the patient data signals, distinguishing between several patient signals and appearance of patient values which were imperceptible to evaluators; both issues could lead nurses to misinterpret the timing and/or the physiological status of the patient (e.g., time of shock and exact value of vitals). Heuristic evaluation, an efficient and inexpensive method, was successfully applied to the T3™ software to identify usability problems that if left unresolved could lead to patient safety issues. These findings may have broad implications for the design of the T3™ and other continuous monitoring systems. Keywords: Data integration; Visualization; Heuristic evaluation; Multimodal monitoring display; Intensive care; Nursing; Trend; User interface Introduction Intensive care units (ICUs) are settings where close monitoring and interventions aimed at achieving homeostasis (i.e. stable vitals within target ranges) are performed on the most fragile patients. Te complexity of a pediatric patient’s underlying condition is exacerbated by their rapidly evolving developmental physiology [1]. For example, target ranges for a basic vital such as heart rate is highly dependent on age [2]. Long-term monitoring of the critically-ill, pediatric patient is a signature feature of the intensive care unit, and is ofen associated with the heavy use of monitoring technologies, which collectively, generate large quantities of data[3]. Clinicians specialized in critical care have been known to experience “information overload” [4,5] due to a high degree of multi-tasking [6] and sustained prolonged vigilant monitoring [7]. Te negative efects of the technology-intense ICU environment may hinder nurses’ ability to monitor and signal changes in critically ill patients. Due to the complexity and fragility of the critically ill patient clinicians need to use diferent technologies to get a sense of organ function, the physiological systems afected and the overall patient status. Te use of multiple technologies, used simultaneously to continually assess the patient status, is termed “multimodal monitoring” [8]. Practically, multimodal monitoring is challenging since nurses must constantly scan each discrete monitoring technology to mentally integrate the data, assess current stability and predict the future trend of the patient to anticipate interventions. In the modern technology-driven ICU, a critical care nurse spends half of the time assimilating information embedded in clinical information systems and 15% of the time on monitoring live vitals [9]. Tus, these aforementioned factors make continuous monitoring during extended periods of time challenging and increase the difculty of making critical decisions based on large data sets. Nurses’ workload could Lin YL et al., J Nurs Care 2015, 4:6 DOI: 10.4172/2167-1168.1000300 Research Article Open Access J Nurs Care ISSN:2167-1168 JNC, an open access journal Volume 4 • Issue 6 • 1000300 J o u r n a l o f N u r s i n g & C a r e ISSN: 2167-1168 Journal of Nursing and Care