OBJECTIVE To identify issues during donning and doffing of personal protective equipment (PPE) for infectious diseases and to inform PPE procurement criteria and design. DESIGN A mixed methods approach was used. Usability testing assessed the appropriateness, potential for errors, and ease of use of various combinations of PPE. A qualitative constructivist approach was used to analyze participant feedback. SETTING Four academic health sciences centers: 2 adult hospitals, 1 trauma center, and 1 pediatric hospital, in Toronto, Canada. PARTICIPANTS Participants (n=82) were representative of the potential users of PPE within Western healthcare institutions. RESULTS None of the tested combinations provided a complete solution for PPE. Environmental factors, such as anteroom layout, and the design of protocols and instructional material were also found to impact safety. The study identified the need to design PPE as a complete system, rather than mixing and matching components. CONCLUSIONS Healthcare institutions are encouraged to use human factors methods to identify risk and failure points with the usage of their selected PPE, and to modify on the basis of iterative evaluations with representative end users. Manufacturers of PPE should consider usability when designing the next generation of PPE. Infect Control Hosp Epidemiol 2016;37:1022-1028.
BackgroundThe design complexity of critical care ventilators (CCVs) can lead to use errors and patient harm. In this study, we present the results of a comparison of four CCVs from market leaders, using a rigorous methodology for the evaluation of use safety and user experience of medical devices.MethodsWe carried out a comparative usability study of four CCVs: Hamilton G5, Puritan Bennett 980, Maquet SERVO-U, and Dräger Evita V500. Forty-eight critical care respiratory therapists participated in this fully counterbalanced, repeated measures study. Participants completed seven clinical scenarios composed of 16 tasks on each ventilator.Use safety was measured by percentage of tasks with use errors or close calls (UE/CCs). User experience was measured by system usability and workload metrics, using the Post-Study System Usability Questionnaire (PSSUQ) and the National Aeronautics and Space Administration Task Load Index (NASA-TLX).ResultsNine of 18 post hoc contrasts between pairs of ventilators were significant after Bonferroni correction, with effect sizes between 0.4 and 1.09 (Cohen’s d). There were significantly fewer UE/CCs with SERVO-U when compared to G5 (p = 0.044) and V500 (p = 0.020). Participants reported higher system usability for G5 when compared to PB980 (p = 0.035) and higher system usability for SERVO-U when compared to G5 (p < 0.001), PB980 (p < 0.001), and V500 (p < 0.001). Participants reported lower workload for G5 when compared to PB980 (p < 0.001) and lower workload for SERVO-U when compared to PB980 (p < 0.001) and V500 (p < 0.001). G5 scored better on two of nine possible comparisons; SERVO-U scored better on seven of nine possible comparisons. Aspects influencing participants’ performance and perception include the low sensitivity of G5’s touchscreen and the positive effect from the quality of SERVO-U’s user interface design.ConclusionsThis study provides empirical evidence of how four ventilators from market leaders compare and highlights the importance of medical technology design. Within the boundaries of this study, we can infer that SERVO-U demonstrated the highest levels of use safety and user experience, followed by G5. Based on qualitative data, differences in outcomes could be explained by interaction design, quality of hardware components used in manufacturing, and influence of consumer product technology on users’ expectations.Electronic supplementary materialThe online version of this article (doi:10.1186/s13054-016-1431-1) contains supplementary material, which is available to authorized users.
These findings demonstrate that latent usability related design faults manifest themselves as an increase in NFF reporting and that devices containing usability related design faults can be identified through an analysis of medical equipment maintenance data.
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