VisAhoi: Towards a Library to Generate and Integrate Visualization Onboarding Using High-level Visualization Grammars

Visualization onboarding supports users in reading, interpreting, and extracting information from visual data representations. General-purpose onboarding tools and libraries are applicable for explaining a wide range of graphical user interfaces but cannot handle specific visualization requirements. This paper describes a first step towards developing an onboarding library called VisAhoi, which is easy to integrate, extend, semi-automate, reuse, and customize. VisAhoi supports the creation of onboarding elements for different visualization types and datasets. We demonstrate how to extract and describe onboarding instructions using three well-known high-level descriptive visualization grammars - Vega-Lite, Plotly.js, and ECharts. We show the applicability of our library by performing two usage scenarios that describe the integration of VisAhoi into a VA tool for the analysis of high-throughput screening (HTS) data and, second, into a Flourish template to provide an authoring tool for data journalists for a treemap visualization. We provide a supplementary website that demonstrates the applicability of VisAhoi to various visualizations, including a bar chart, a horizon graph, a change matrix or heatmap, a scatterplot, and a treemap visualization

Design of Visualization Onboarding Concepts for a 2D Scatterplot in a Biomedical Visual Analytics Tool

Biomedical research is highly data-driven. Domain experts need to learn how to interpret complex data visualizations to gain insights. They often need help interpreting data visualizations as they are not part of their training. Integrating visualization onboarding concepts into visual analytics (VA) tools can support users in interpreting, reading, and extracting information from visual presentations. In this paper, we present the design of the onboarding concept for an interactive VA tool to analyze large-scale biological data, particularly high-throughput screening (HTS) data. We evaluated our onboarding design by conducting a cognitive walkthrough and interviews with thinking aloud. We also collected data on domain experts’ visualization literacy. The results of the cognitive walkthrough showed that domain experts positively commented on the onboarding design and proposed adjusting smaller aspects. The interviews showed that domain experts are well-trained in interpreting basic visualizations (e.g., scatterplot, bar chart, line chart). However, they need support correctly interpreting the data visualized in the scatterplot, as they are new to them. Another important insight was fitting the onboarding messages into the domain’s language

Design of Visualization Onboarding Concepts for a 2D Scatterplot in a Biomedical Visual Analytics Tool

Biomedical research is highly data-driven. Domain experts need to learn how to interpret complex data visualizations to gain insights. They often need help interpreting data visualizations as they are not part of their training. Integrating visualization onboarding concepts into visual analytics (VA) tools can support users in interpreting, reading, and extracting information from visual presentations. In this paper, we present the design of the onboarding concept for an in- teractive VA tool to analyze large scaled biological data, particularly high-throughput screening (HTS) data. We evaluated our onboard- ing design by conducting a cognitive walkthrough and interviews with thinking aloud. We also collected data on domain experts’ visu- alization literacy. The results of the cognitive walkthrough showed that domain experts positively commented on the onboarding design and proposed adjusting smaller aspects. The interviews showed that domain experts are well-trained in interpreting basic visualiza- tions (e.g., scatterplot, bar chart, line chart). However, they need support correctly interpreting the data visualized in the scatterplot, as they are new to them. Another important insight was fitting the onboarding messages into the domain’s language

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)