Usability of data integration and visualization software for multidisciplinary pediatric intensive care: a human factors approach to assessing technology

Abstract

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.

Association of Health Record Visualizations With Physicians’ Cognitive Load When Prioritizing Hospitalized Patients

“…This study provides additional evidence that designing concept-oriented visualizations requires a human-centered approach to uncover the content, organization, and visualization methods to best support the information needs of practicing physicians. 29,30…”

Design and Evaluation of an Integrated, Patient-Focused Electronic Health Record Display for Emergency Medicine

“…Two out of 7 participants who answered correctly also referred to the vitals shown in the timeline (via vitals toggle button) but hovered over each heart rate result before answering, and the rest of the participants (5) referred to the dropdown heart rate feature in the current patient status window. Interestingly, usability issues related to time-series data lying outside the visual boundaries were previously raised by Lin et al 34 In a timeseries visual display of patient physiological functions, participants assumed they had found the start of patient data, when it was actually the end of a gap in the timeline which extended outside the boundary of the plot. These findings indicate a need to address usability issues specific to continuous longitudinal data.…”

Diabetes and technology in 2030: a utopian or dystopian future?

“…For example, devices may be created with the ‘if we build it they will come’ mentality, common for consumer‐facing devices without evidence of value. Data overload. If people with diabetes send physicians copious information streams from devices, with an expectation that they will be able to make sense of the data and create a personal action plan, there could be an overload of multimodal continuous data which might contribute to poor decision making . Loss of income. If an individual with diabetes can have his or her problem resolved by a machine, then he or she may need to see a clinician less often, resulting in loss of income for clinicians. Increased liability.…”

“…Data overload. If people with diabetes send physicians copious information streams from devices, with an expectation that they will be able to make sense of the data and create a personal action plan, there could be an overload of multimodal continuous data which might contribute to poor decision making [36].…”