Technology-Mediated Data, its Integration and its Impact on Intensive Care Cognitive Work

Intensive care clinicians face an ever-increasing burden of continuous data from monitoring and therapeutic technologies. Under typically hurried and stressful conditions, these continuous arrays of high-resolution data make interpretation even more challenging. Data integration technologies that organize and visually communicate meaning may potentially improve team decision making but have yet to show compelling evidence on the benefits to individual performance, or team performance for that matter. Facets of decision making which are not well understood are the role of contemporary intensive care technologies in decision making, the technology-mediated cognitive processes, and the effects of dense, multi-parametric visualizations on data retrieval, integration and interpretation tasks. Therefore, this thesis investigates these facets of decision making in the contemporary intensive care unit from the perspective of physicians, nurses and respiratory therapists. The focus on clinicians in this particular sociotechnical setting is known as Human factors, an area of research which seeks to understand the interaction between humans and technologies and optimize overall system performance. Through the lens of these three types of clinicians we inform the design of data integration technologies, specifically T3™, a state-of-the-art data integration and visualization technology. It enables tasks related to Tracking of physiologic signals, displaying Trajectory, and Triggering decisions. This thesis consists of a systematic review of literature related to data integration and visualization technology for intensive care decision-making and three experimental phases. First, the systematic review was conducted to identify studies that looked at decision making processes using technological sources and the facilitation of these processes using decision support tools. The systematic review identified qualitative studies which described physicians’ and nurses’ cognitive processes during clinical tasks and quantitative studies which measured differences in human performance in terms of time, accuracy of decisions, and cognitive load. Collectively, the most mature technologies had been developed over decades and were informed by both qualitative and quantitative studies. A meta-analysis, or aggregation of data from multiple studies, found that perceived mental and temporal demands were lower, and performance was better with new data visualizations compared to traditional paper-based systems. Second, the cognitive processes of physicians, nurses and respiratory therapists, were analyzed using the macrocognition framework, a taxonomy for cognitive processes occurring in complex, real-world settings. The framework was used to analyze interview data of critical decision-making and the role of technology-mediated sources. Among ten macrocognitive processes, Sensemaking was heavily informed and influenced by technology. For Sensemaking, physicians utilized all sources available and compartmentalized the data sets according to different physiological systems. Nurses were the most active in their manipulation of technology and devoted much of their cognition to communicating information to physicians and respiratory therapists. Respiratory therapists made sense of data specific to the respiratory system and had in-depth knowledge of respiratory support data. These findings suggest that to improve team care, it is essential that data integration technologies be designed for nurse usability and that Sensemaking should be tailored to each type of clinician. Third, a heuristic evaluation method, a low-cost method to test interface compliance with usability design principles, was conducted on T3™. Evaluation, by a team of two clinicians and two human factors specialists found 50 usability issues associated with 194 heuristic violations. Issues included (1) difficulty with choosing the time period of the patient data signals, (2) distinguishing between several patient signals and (3) imperceptible changes in physiological values; 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). Timescale manipulation and rapid visualization of out-of-range signals were identified as catastrophic issues that should be addressed. Fourth, usability testing identified interface facilitators and barriers to the use of T3™ by physicians, nurses and respiratory therapists. The current interface facilitated simple tracking and trajectory tasks when a small set of parameters were displayed simultaneously. The barriers included: (1) difficulty with acquiring multiple parameter data from data-dense visualizations and perceiving out-of-target data and (2) limited clinical context of integrated continuous data due separate clinical notes (e.g., in the electronic medical record). Though T3™ integrated and condensed large amounts of data, visual pattern overload and poor data recall obfuscated the raw data and thus, hindered data interpretation. While this study tested T3™, findings and design recommendations may be applied generally to technologies that display data in a similar format or to the same degree of integration, as the T3™ version studied. Overall, this thesis contributes to the understanding of how fractured clinical data and information systems and their integration impact intensive care cognitive work.

Keywords: clinical engineering
data integration
data visualization
decision-making
human factors
intensive care
Advisor: Guerguerian, Anne-Marie
Trbovich, Patricia