Identifying Digital Rhetoric in the Telemedicine User Interface

Abstract

Telemedicine is an alternative healthcare delivery system whereby patients access digital technology to consult with a physician virtually. Patients first interact with telemedicine via a consumer-facing website. Telemedicine promises numerous benefits to patients, such as increased access to healthcare, yet poor usability of the telemedicine user interface (UI) may hinder patient acceptance and adoption of the service. The telemedicine UI moderates patients’ ability to utilize telemedicine, and therefore it must be usable, but it must also be rhetorical to motivate patients to perform certain actions. Digital rhetoric refers to UI elements that influence user actions and knowledge and is tied to usability because of these same human–computer interaction (HCI) factors. This study examined the usability of three telemedicine provider UIs and by identifying usability problems, reveals digital rhetoric that is significant to telemedicine UIs. The article concludes by offering heuristics of digital rhetoric that lead to optimal usability.

Toward TPC-UX: UX Topics in TPC Journals 2013–2022

“…The weak correlation and high frequency of articles within this category suggest that multimodality is a topical stratum within the corpus. Broadly, the articles within this category demonstrate a repeated concern with conveyance—e.g., information visualization (Meng, 2019; Passera & Haapio, 2013; Qian et al, 2014), the function of icons and other UI components (Gallagher & Holmes, 2019; Kascak et al, 2013), and interactive technologies (Tham, 2018; Valino Koh et al, 2013)—within varying contexts marked by complexity—e.g., e-health and telemedicine (Campbell, 2022; Lazard & Mackert, 2015), risk communication technologies (Richards, 2018), and cross-cultural communications (Li et al, 2021).…”

“…For instance, Gallagher and Holmes (2019) draw on visual rhetoric, embodied rhetoric, and Pierre Bourdieu's habitus to argue that empty state pages “shape user habits through embodied repetition” and that “[s]uch habituation necessitates specific attention to the ways in which interfaces shape user habits even as users communicate creatively through them for a variety of rhetorical purposes” (p. 272). Similarly, Campbell (2022) uses visual and digital rhetorical theory to examine the rhetoricities of telemedicine UIs and provide practitioners with rhetorically attuned usability heuristics. The linking of multimodality and explicit discussions of rhetoric may stem from the long-standing interest in multimodality within the academic discipline of rhetoric and composition (e.g., Hocks, 2003), a discipline with which TPC has a long-standing relationship (Sullivan & Porter, 1993).…”

“…More recently, health and medicine TPC-UX work has examined telemedicine UIs (e.g., Campbell, 2022), mobile health applications (e.g., Acharya, 2022b; Kirkscey, 2021; Welhausen & Bivens, 2021), and wearable health technologies (Arduser, 2018; Jones et al, 2017). Given that much of this research has migrated to RHM , future meta-analyses of TPC-UX scholarship, or general TPC scholarship, should consider this journal in their analyses.…”

Digital Visual Design Reengineering and Application Based on K-means Clustering Algorithm

“…Literature [11] proposes a color system extraction method based on K-means clustering algorithm, and constructs a color database of aesthetic artifacts; literature [12] adopts an image processing method, extracts the mural color system from the color three-channel, and puts forward a gene extraction method based on machine learning algorithms for the digital visual design recreation; literature [13] studies the color particles of the mural paintings, and through the particle swarm optimization algorithm Improved K-means clustering analysis of color features; Literature [14] uses deep learning methods to extract and learn graphic shapes, and constructs a digital reengineering model of multi-dimensional linear structure shapes; Literature [15] improves the K-means clustering method to digitally extract texture representations by using the peak density strategy, and at the same time, uses a self-coder neural network to construct the artifacts graphic texture expression model ; Literature [16] combines the gray wolf optimization algorithm, K-means algorithm and convolutional neural network method table mural texture aesthetics for feature extraction and reengineering representation; Literature [17] combines the color characteristics of aesthetic artifacts and line characteristics, to build a digital visual design reengineering evaluation system. For the analysis of the above literature, the existing graphic visual gene extraction methods have the following defects [18]: 1) digital visual design reengineering feature selection is not standard enough [19]; 2) gene extraction methods lack of generalization [20]; 3) gene extraction methods are not efficient enough.…”