Perceived Workload Using Separate (Filtering Facepiece Respirator and Face Shield) and Powered Air-Purifying Respirator and Integrated Lightweight Protective Air-Purifying Respirator: Protocol for an International Multisite Human Factors Randomized Crossover Feasibility Study.

BACKGROUND: The design of personal protective equipment (PPE) may affect well-being and clinical work. PPE as an integrated item may improve usability and increase adherence by healthcare professionals. Human factors design and safety may reduce occupational-acquired diseases. As an integrated PPE, a lightweight protective air-purifying respirator (L-PAPR) could be used during health procedures where healthcare professionals are exposed to airborne pathogens. The human factors affecting the implementation of alternative PPE such as L-PAPR have not been thoroughly studied. The population of interest is health care professionals, the intervention is the performance by PPE during tasks across the three PPE types 1.) N95 respirators and face shields, 2.)traditional powered air-purifying respirator(PAPR), and 3.) L-PAPR. The outcomes are user error, communications, safety, and end-user preferences. OBJECTIVE: This study will assess whether the L-PAPR improves health care professionals' comfort in terms of perceived workload and physical and psychological burden during direct patient care when compared with the traditional PAPR or N95 and face shield. This study also aims to evaluate human factors during the comparison of the use of L-PAPR with a combination of N95 respirators plus face shields or the traditional PAPRs. METHODS: This is an interventional randomized crossover quality improvement feasibility study consisting of a 3-site simulation phase with 10 participants per site and subsequent field testing in 2 sites with 30 participants at each site. The 3 types of respiratory PPE will be compared across medical tasks and while donning and doffing. We will evaluate the user's perceived workload, usability, usage errors, and heart rate. We will conduct semistructured interviews to identify barriers and enablers to implementation across each PPE type over a single continuous wear episode and observe interpersonal communications across conditions and PPE types. RESULTS: We expect the research may highlight communication challenges and differences in usability and convenience across PPE types along with error frequency during PPE use across PPE types, tasks, and time. CONCLUSIONS: The design of PPE may affect overall well-being and hinder or facilitate clinical work. Combining 2 pieces of PPE into a single integrated item may improve usability and reduce occupational-acquired diseases. The human factors affecting the implementation of an alternative PPE such as L-PAPR or PAPR have not been thoroughly studied. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/36549.

Addressing personal protective equipment (PPE) decontamination: Methylene blue and light inactivates severe acute respiratory coronavirus virus 2 (SARS-CoV-2) on N95 respirators and medical masks with maintenance of integrity and fit.

OBJECTIVE: The coronavirus disease 2019 (COVID-19) pandemic has resulted in shortages of personal protective equipment (PPE), underscoring the urgent need for simple, efficient, and inexpensive methods to decontaminate masks and respirators exposed to severe acute respiratory coronavirus virus 2 (SARS-CoV-2). We hypothesized that methylene blue (MB) photochemical treatment, which has various clinical applications, could decontaminate PPE contaminated with coronavirus. DESIGN: The 2 arms of the study included (1) PPE inoculation with coronaviruses followed by MB with light (MBL) decontamination treatment and (2) PPE treatment with MBL for 5 cycles of decontamination to determine maintenance of PPE performance. METHODS: MBL treatment was used to inactivate coronaviruses on 3 N95 filtering facepiece respirator (FFR) and 2 medical mask models. We inoculated FFR and medical mask materials with 3 coronaviruses, including SARS-CoV-2, and we treated them with 10 µM MB and exposed them to 50,000 lux of white light or 12,500 lux of red light for 30 minutes. In parallel, integrity was assessed after 5 cycles of decontamination using multiple US and international test methods, and the process was compared with the FDA-authorized vaporized hydrogen peroxide plus ozone (VHP+O3) decontamination method. RESULTS: Overall, MBL robustly and consistently inactivated all 3 coronaviruses with 99.8% to >99.9% virus inactivation across all FFRs and medical masks tested. FFR and medical mask integrity was maintained after 5 cycles of MBL treatment, whereas 1 FFR model failed after 5 cycles of VHP+O3. CONCLUSIONS: MBL treatment decontaminated respirators and masks by inactivating 3 tested coronaviruses without compromising integrity through 5 cycles of decontamination. MBL decontamination is effective, is low cost, and does not require specialized equipment, making it applicable in low- to high-resource settings.

Household Materials Selection for Homemade Cloth Face Coverings and Their Filtration Efficiency Enhancement with Triboelectric Charging.

The COVID-19 pandemic is currently causing a severe disruption and shortage in the global supply chain of necessary personal protective equipment (e.g., N95 respirators). The U.S. CDC has recommended use of household cloth by the general public to make cloth face coverings as a method of source control. We evaluated the filtration properties of natural and synthetic materials using a modified procedure for N95 respirator approval. Common fabrics of cotton, polyester, nylon, and silk had filtration efficiency of 5-25%, polypropylene spunbond had filtration efficiency 6-10%, and paper-based products had filtration efficiency of 10-20%. An advantage of polypropylene spunbond is that it can be simply triboelectrically charged to enhance the filtration efficiency (from 6 to >10%) without any increase in pressure (stable overnight and in humid environments). Using the filtration quality factor, fabric microstructure, and charging ability, we are able to provide an assessment of suggested fabric materials for homemade facial coverings.

Technology-mediated macrocognition: Investigating how physicians, nurses, and respiratory therapists make critical decisions.

PURPOSE: Although intensive care clinicians are expected to make data-driven critical decisions using the technologies available to them, the effect of those technologies on decision-making are not well understood. Using the macrocognitive framework, we studied critical decision-making and technology use to understand how different specialists within teams make decisions and guide the development of decision-making support technologies. MATERIALS AND METHODS: The Critical Decision Method was used to understand the macrocognitive processes used during critical decision-making of twelve critical care clinicians. Deductive (based on the macrocognition framework) and inductive coding were used to analyze the macrocognitive processes, their interrelationships, and their relation to technologies. RESULTS: Over 60% of critical decision-making macrocognition was devoted to Sensemaking, Anticipation, and Communication. The most technology-mediated process was Sensemaking. Of particular note, physicians and respiratory therapists extracted information for their own use, while nurses extracted information to communicate to others. Physicians switched between ten macrocognitive processes, whereas nurses and respiratory therapists switched between five processes. CONCLUSIONS: This exploratory study provides much needed details about the different ways in which specialists use technologies to support decision-making tasks, particularly those involving sensemaking, which are essential to the design and development of decision-support technologies.

Association of Data Integration Technologies With Intensive Care Clinician Performance: A Systematic Review and Meta-analysis.

Importance: Sources of data in the intensive care setting are increasing exponentially, but the benefits of displaying multiparametric, high-frequency data are unknown. Decision making may not benefit from this technology if clinicians remain cognitively overburdened by poorly designed data integration and visualization technologies (DIVTs). Objective: To systematically review and summarize the published evidence on the association of user-centered DIVTs with intensive care clinician performance. Data Sources: MEDLINE, Embase, Cochrane Central Register of Controlled Trials, PsycINFO, and Web of Science were searched in May 2014 and January 2018. Study Selection: Studies had 3 requirements: (1) the study tested a viable DIVT, (2) participants involved were intensive care clinicians, and (3) the study reported quantitative results associated with decision making in an intensive care setting. Data Extraction and Synthesis: Of 252 records screened, 20 studies, published from 2004 to 2016, were included. The human factors framework to assess health technologies was applied to measure study completeness, and the Quality Assessment Instrument was used to assess the quality of the studies. PRISMA guidelines were adapted to conduct the systematic review and meta-analysis. Main Outcomes and Measures: Study completeness and quality; clinician performance; physical, mental, and temporal demand; effort; frustration; time to decision; and decision accuracy. Results: Of the 20 included studies, 16 were experimental studies with 410 intensive care clinician participants and 4 were survey-based studies with 1511 respondents. Scores for study completeness ranged from 27 to 43, with a maximum score of 47, and scores for study quality ranged from 46 to 79, with a maximum score of 90. Of 20 studies, DIVTs were evaluated in clinical settings in 2 studies (10%); time to decision was measured in 14 studies (70%); and decision accuracy was measured in 11 studies (55%). Measures of cognitive workload pooled in the meta-analysis suggested that any DIVT was an improvement over paper-based data in terms of self-reported performance, mental and temporal demand, and effort. With a maximum score of 22, median (IQR) mental demand scores for electronic display were 10 (7-13), tabular display scores were 8 (6.0-11.5), and novel visualization scores were 8 (6-12), compared with 17 (14-19) for paper. The median (IQR) temporal demand scores were also lower for all electronic visualizations compared with paper, with scores of 8 (6-11) for electronic display, 7 (6-11) for tabular and bar displays, 7 (5-11) for novel visualizations, and 16 (14.3-19.0) for paper. The median (IQR) performance scores improved for all electronic visualizations compared with paper (lower score indicates better self-reported performance), with scores of 6 (3-11) for electronic displays, 6 (4-11) for tabular and bar displays, 6 (4-11) for novel visualizations, and 14 (11-16) for paper. Frustration and physical demand domains of cognitive workload did not change, and differences between electronic displays were not significant. Conclusions and Relevance: This review suggests that DIVTs are associated with increased integration and consistency of data. Much work remains to identify which visualizations effectively reduce cognitive workload to enhance decision making based on intensive care data. Standardizing human factors testing by developing a repository of open access benchmarked test protocols, using a set of outcome measures, scenarios, and data sets, may accelerate the design and selection of the most appropriate DIVT.

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

BACKGROUND: Intensive 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. METHODS: Twenty-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. RESULTS: Time 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. CONCLUSIONS: Usability 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.