A Design Study Process Model for Medical Visualization

A Design Study Process Model for Medical Visualization Mengjie Fan and Liang Zhou* Abstract—We introduce a design study process model for medical visualization based on the analysis of existing medical visualization and visual analysis works, and our own interdisciplinary research experience. With a literature review of related works covering various data types and applications, we identify features of medical visualization and visual analysis research and formulate our model thereafter. Compared to previous design study process models, our new model emphasizes: distinguishing between different stakeholders and target users before initiating specific designs, distinguishing design stages according to analytic logic or cognitive habits, and classifying task types as inferential or descriptive, and further hypothesis-based or hypothesis-free based on whether they involve multiple subgroups. In addition, our model refines previous models according to the characteristics of medical problems and provides referable guidance for each step. These improvements make the visualization design targeted, generalizable, and operational, which can adapt to the complexity and diversity of medical problems. We apply this model to guide the design of a visual analysis method and reanalyze three medical visualization-related works. These examples suggest that the new process model can provide a systematic theoretical framework and practical guidance for interdisciplinary medical visualization research. We give recommendations that future researchers can refer to, report on reflections on the model, and delineate it from existing models. Index Terms—Design study, medical visualization, process model 1 INTRODUCTION Medical visualization aims to help practitioners and researchers better understand and analyze medical data and information by transforming medical data into visual forms such as graphs and images through computer graphics and image processing techniques [43]. Medical visualization is a key research area in the current data-driven healthcare practice, focusing on the acquisition of medical evidence and helping stakeholders to understand and analyze medical data. Visualization and visual analysis of medical data enables supporting clinical decision making, improving healthcare, simplifying presentation of healthcare data, and accelerating healthcare performance, etc. [1,61]. Design study is an increasingly popular form of problem-driven visualization research [66]. Sedlmair et al. define a design study as an interdisciplinary research method in which visualization researchers analyze a specific real-world problem faced by domain experts and de- sign a visualization method to support the solution of the problem [66]. Tasks in the real world can be classified into problem-driven tasks and technology-driven tasks [31]. Medical data analysis tasks are mostly problem-driven, and their main goals are to use various medical data to address specific problems in real medical scenarios. The design study methodology can take advantage of the synergy between visual- ization and medical experts to create tailored visualization techniques and solutions for diverse needs, thereby addressing specific medical challenges. Although some design study process models and practical guidelines are available to guide general visualization design practices, there is still a lack of a systematic methodology considering the specialty of the area of medicine to guide the specific design and research process in medical visualization. This absence often makes it difficult for researchers to realize the full potential of visualization when solving complex problems in medicine, and it also limits the efficiency of interdisciplinary collaboration. In this paper, we propose a design study process model for med- ical visualization and visual analysis. The model is tailored for the characteristics of medical data and analysis tasks based on a literature • Mengjie Fan and Liang Zhou are with the Institute of Medical Technology, Peking University Health Science Center, and the National Institute of Health Data Science (NIHDS), Peking University. Liang Zhou is the corresponding author. E-mails: mengjiefan@bjmu.edu.cn, zhoulng@pku.edu.cn. Manuscript received xx xxx. 201x; accepted xx xxx. 201x. Date of Publication xx xxx. 201x; date of current version xx xxx. 201x. For information on obtaining reprints of this article, please send e-mail to: reprints@ieee.org. Digital Object Identifier: xx.xxxx/TVCG.201x.xxxxxxx review and our own experiences. Our first contribution is that the spe- cialized model covers the whole process from collaborator selection all the way to the final evaluation through steps of visualization solution implementation, as shown in Fig. 1. We identify three factors that are critical for medical visualization, namely, stakeholders, stages, and subgroup analysis. At each stage of the model, we provide referable guidance to assist researchers

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