EdWordle: Consistency-Preserving Word Cloud Editing

Doctors', Patients' and Physician Associates' Perceptions of the Physician Associate Role in the Emergency Department

INTRODUCTION

The Emergency Department (ED) has seen increased patient attendance and difficulty meeting demands. New healthcare professions such as Physician Associates (PAs) are being utilised to complement the existing medical workforce. Despite the growth of their professions in the United Kingdom, little evidence is available about the perceptions of their roles.

OBJECTIVE

This study aims to provide evidence of doctors', PAs' and patients' perceptions of the PA role in the UK ED.

METHODS

A mixed methods approach consisted of the following: 1. An online exploratory survey of ED doctors at one English ED over 1 month (February-March 2022). 2. Post consultation semi-structured patient questionnaires over 2 weeks (April 2022). 3. Semi-structured virtual interviews with ED consultants across the four regions of the United Kingdom (3 months in 2022). 4. Semi-structured virtual interviews with ED PAs across the four regions of the United Kingdom (3 months in 2022). The analysis methods that were used included frequency counts and percentages from closed questions, and hybrid thematic analysis of free text and interview transcripts.

RESULTS

Four ED consultants and four ED PAs across the United Kingdom were interviewed. Twenty-eight ED doctors participated in the online survey. Fifty-seven patients completed the post consultation questionnaire. Four main themes (PAs being fit for purpose; patient recognition of PAs, PAs providing continuity of care, and future PAs and regulation) were deduced as per the General Medical Council, Good Medical Practice domains (knowledge, skills and development; patients, partnership and communication; colleagues, culture and safety; and trust and professionalism). Other subthemes were induced via hybrid thematic analysis. In this study, doctors and patients had mixed comments about the role of PAs. Most of them were positive as doctor participants perceived PAs to be knowledgeable, highly skilled, with mostly good communication skills, team players, providing continuity of care and overall being fit for purpose. However, some doctor participants commented negatively about PAs for providing little quality healthcare and being inexperienced. There was a desire for career progression among the PA participants and a need to work to their full potential. Although the clinicians of this study displayed a clear understanding of the PA role in the ED, a high frequency of surveyed patients mistook PAs for doctors. It was suggested that future PAs could complete a postqualification programme in emergency medicine, combine roles, be paid on an alternative scale and be formally regulated.

CONCLUSION

In this study, mixed views were expressed by ED consultants, ED junior doctors and patients regarding the role of the PA in the ED. Stakeholders can use the information presented to develop a better understanding of the perceptions of the PA role within the UK ED.

PATIENT OR PUBLIC CONTRIBUTION

The Patient and Public Involvement and Engagement (PPIE) group, led by Healthwatch, made significant contributions to the study's design by providing valuable feedback on the information sheets and consent forms utilised. The patients' responses helped guide the study's direction and shape its future work. As part of the dissemination activities, the study findings was shared with both the PPIE team and Healthwatch media production team.

Creating Emordle: Animating Word Cloud for Emotion Expression

We propose emordle, a conceptual design that animates wordles (compact word clouds) to deliver their emotional context to audiences. To inform the design, we first reviewed online examples of animated texts and animated wordles, and summarized strategies for injecting emotion into the animations. We introduced a composite approach that extends an existing animation scheme for one word to multiple words in a wordle with two global factors: the randomness of text animation (entropy) and the animation speed (speed). To create an emordle, general users can choose one predefined animated scheme that matches the intended emotion class and fine-tune the emotion intensity with the two parameters. We designed proof-of-concept emordle examples for four basic emotion classes, namely happiness, sadness, anger, and fear. We conducted two controlled crowdsourcing studies to evaluate our approach. The first study confirmed that people generally agreed on the conveyed emotions from well-crafted animations, and the second one demonstrated that our identified factors helped fine-tune the extent of the emotion delivered. We also invited general users to create their own emordles based on our proposed framework. Through this user study, we confirmed the effectiveness of the approach. We concluded with implications for future research opportunities of supporting emotion expression in visualizations.

Deep Colormap Extraction From Visualizations

This article presents a new approach based on deep learning to automatically extract colormaps from visualizations. After summarizing colors in an input visualization image as a Lab color histogram, we pass the histogram to a pre-trained deep neural network, which learns to predict the colormap that produces the visualization. To train the network, we create a new dataset of  ∼ 64K visualizations that cover a wide variety of data distributions, chart types, and colormaps. The network adopts an atrous spatial pyramid pooling module to capture color features at multiple scales in the input color histograms. We then classify the predicted colormap as discrete or continuous, and refine the predicted colormap based on its color histogram. Quantitative comparisons to existing methods show the superior performance of our approach on both synthetic and real-world visualizations. We further demonstrate the utility of our method with two use cases, i.e., color transfer and color remapping.

Affect-Aware Word Clouds

Word clouds are widely used for non-analytic purposes, such as introducing a topic to students, or creating a gift with personally meaningful text. Surveys show that users prefer tools that yield word clouds with a stronger emotional impact. Fonts and color palettes are powerful typographical signals that may determine this impact. Typically, these signals are assigned randomly, or expected to be chosen by the users. We present an affect-aware font and color palette selection methodology that aims to facilitate more informed choices. We infer associations of fonts with a set of eight affects, and evaluate the resulting data in a series of user studies both on individual words as well as in word clouds. Relying on a recent study to procure affective color palettes, we carry out a similar user study to understand the impact of color choices on word clouds. Our findings suggest that both fonts and color palettes are powerful tools contributing to the affects evoked by a word cloud. The experiments further confirm that the novel datasets we propose are successful in enabling this. We also find that, for the majority of the affects, both signals need to be congruent to create a stronger impact. Based on this data, we implement a prototype that allows users to specify a desired affect and recommends congruent fonts and color palettes for the word.

An Evaluation of Semantically Grouped Word Cloud Designs

Word clouds continue to be a popular tool for summarizing textual information, despite their well-documented deficiencies for analytic tasks. Much of their popularity rests on their playful visual appeal. In this paper, we present the results of a series of controlled experiments that show that layouts in which words are arranged into semantically and visually distinct zones are more effective for understanding the underlying topics than standard word cloud layouts. White space separators and/or spatially grouped color coding led to significantly stronger understanding of the underlying topics compared to a standard Wordle layout, while simultaneously scoring higher on measures of aesthetic appeal. This work is an advance on prior research on semantic layouts for word clouds because that prior work has either not ensured that the different semantic groupings are visually or semantically distinct, or has not performed usability studies. An additional contribution of this work is the development of a dataset for a semantic category identification task that can be used for replication of these results or future evaluations of word cloud designs.

Word cloud analysis and single word summarisation as a new paediatric educational tool: Results of a neonatal application

AIMS

To analyse the value of computerised 'word cloud' (WC) generated from spontaneously articulated 'single word summarisation' (SWS) by medical students to assist their learning during the neonatal intensive care unit placement. To highlight WC as a potential new tool in paediatric teaching, improving student engagement and reflective feedback.

METHODS

An observational study was conducted in the neonatal intensive care unit of University Maternity Hospital Limerick, Ireland for 5 years (October 2012 to September 2017). One faculty member prospectively recorded SWS in neonatology by graduate entry medical students. An online 'WC generator' under an open-source licence was used to compute the WC. Hospital audit committee approved the study.

RESULTS

A total of 268 SWS were recorded consecutively from 268 medical students towards the WC generation. Structured multi-response student feedback showed SWS and WC as stimulating, unique and creative. Powerpoint presentation of the computed WC prompted students to reflect on their chosen words and that of peers.

CONCLUSIONS

Visualisation of medical student-generated SWS as a WC could stimulate interaction, reflection and clinical discussion, thus assisting teachers to foster better student engagement. This innovative educational tool equips students to 'convey more with fewer words' and has the potential transferability to other clinical disciplines.

ShapeWordle: Tailoring Wordles using Shape-aware Archimedean Spirals

We present a new technique to enable the creation of shape-bounded Wordles, we call ShapeWordle, in which we fit words to form a given shape. To guide word placement within a shape, we extend the traditional Archimedean spirals to be shape-aware by formulating the spirals in a differential form using the distance field of the shape. To handle non-convex shapes, we introduce a multi-centric Wordle layout method that segments the shape into parts for our shape-aware spirals to adaptively fill the space and generate word placements. In addition, we offer a set of editing interactions to facilitate the creation of semantically-meaningful Wordles. Lastly, we present three evaluations: a comprehensive comparison of our results against the state-of-the-art technique (WordArt), case studies with 14 users, and a gallery to showcase the coverage of our technique.

On Building Online Visualization Maps for News Data Streams by Means of Mathematical Optimization

In this article we develop a novel online framework to visualize news data over a time horizon. First, we perform a Natural Language Processing analysis, wherein the words are extracted, and their attributes, namely the importance and the relatedness, are calculated. Second, we present a Mathematical Optimization model for the visualization problem and a numerical optimization approach. The model represents the words using circles, the time-varying area of which displays the importance of the words in each time period. Word location in the visualization region is guided by three criteria, namely, the accurate representation of semantic relatedness, the spread of the words in the visualization region to improve the quality of the visualization, and the visual stability over the time horizon. Our approach is flexible, allowing the user to interact with the display, as well as incremental and scalable. We show results for three case studies using data from Danish news sources.