BackgroundIntensive care clinicians use several sources of data in order to inform decision-making. We set out to evaluate a new interactive data integration platform called T3™ made available for pediatric intensive care. Three primary functions are supported: tracking of physiologic signals, displaying trajectory, and triggering decisions, by highlighting data or estimating risk of patient instability. We designed a human factors study to identify interface usability issues, to measure ease of use, and to describe interface features that may enable or hinder clinical tasks.MethodsTwenty-two participants, consisting of bedside intensive care physicians, nurses, and respiratory therapists, tested the T3™ interface in a simulation laboratory setting. Twenty tasks were performed with a true-to-setting, fully functional, prototype, populated with physiological and therapeutic intervention patient data. Primary data visualization was time series and secondary visualizations were: 1) shading out-of-target values, 2) mini-trends with exaggerated maxima and minima (sparklines), and 3) bar graph of a 16-parameter indicator. Task completion was video recorded and assessed using a use error rating scale. Usability issues were classified in the context of task and type of clinician. A severity rating scale was used to rate potential clinical impact of usability issues.ResultsTime series supported tracking a single parameter but partially supported determining patient trajectory using multiple parameters. Visual pattern overload was observed with multiple parameter data streams. Automated data processing using shading and sparklines was often ignored but the 16-parameter data reduction algorithm, displayed as a persistent bar graph, was visually intuitive. However, by selecting or automatically processing data, triggering aids distorted the raw data that clinicians use regularly. Consequently, clinicians could not rely on new data representations because they did not know how they were established or derived.ConclusionsUsability issues, observed through contextual use, provided directions for tangible design improvements of data integration software that may lessen use errors and promote safe use. Data-driven decision making can benefit from iterative interface redesign involving clinician-users in simulated environments. This study is a first step in understanding how software can support clinicians’ decision making with integrated continuous monitoring data. Importantly, testing of similar platforms by all the different disciplines who may become clinician users is a fundamental step necessary to understand the impact on clinical outcomes of decision aids.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-017-0520-7) contains supplementary material, which is available to authorized users.

Key Points Question Do data integration and visualization technologies alleviate clinicians’ cognitive workload and alter decision-making performance? Findings In this systematic review and meta-analysis of 20 studies, data integration and visualization technologies were associated with improvements in self-reported performance, mental and temporal demand, and effort compared with paper-based recording systems, but no specific type is superior to others. Only 10% of studies of data integration and visualization technology evaluated them in clinical settings. Meaning Data integration and visualization technologies offer promising features to improve decision making by clinicians in the intensive care setting, but standardized test protocols are needed to generate clinician-centered evaluations and accelerate screening of technologies that support data-driven decision making.

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