Text-centric Visual Approaches to Support Clinical Overview of Medical Text

Healthcare practice is a complex activity that merges objective medical knowledge with the subjective knowledge of the patient's life to deliver individualized, patient-centred care. This multifaceted understanding of the patient is reflected in medical documentation, which aims to capture the whole picture of the patient's illness trajectory. Among the various data items encompassed in this documentation, the unstructured and semi-structured text reports (e.g., physician notes, discharge summaries, and nursing notes) are among its most informative and perused portions; they are also where healthcare workers spend the most time on when studying a patient record in preparation for care. Text has many desirable features for this task, including expressivity of communication, conciseness as a narrative embodiment, and flexibility of form to accommodate the particularities of a patient's case. On the other hand, text is also time consuming and cognitively demanding to peruse. When patient records grow larger with more text reports, they also become increasingly unwieldy, ridden with a lack of standardization and significant redundancy as healthcare workers repeat past medical information into new notes as an attempt to make reports self contained and reduce overall reading time. While most key information gets propagated this way, important details may still get lost in the process, leading to a potentially incomplete picture of the patient's illness trajectory in these recent reports. A number of data visualization solutions have been proposed to minimize the burden of this study and expedite overviews while increasing the scope of visibility for text notes, via overarching at-a-glance visual summaries of information present in text portions of the record. However, they come with limitations of their own, including limited expressivity and communication bandwidth when compared to text, and issues of trust due to a need for automated text processing which often introduces error. Many data visualization works for the space assume perfect automation, and thus fail to consider the practical limitations that arise when such solutions are deployed in practice. These limitations could be partly mitigated by providing access to original text sources alongside the visual summaries for verification and further context. However, these works tend to heavily lean on graphical representations, and linkage to text documents and reading is still sparsely supported. Arguably, there is complementary synergy between graphical-based and text-based representations that could lead to more sensible solutions to support the clinical overview task. This thesis thus investigates ways that data visualization can benefit from a more text-centric approach to help healthcare workers peruse text portions of the patient record with ease, while still leveraging the power of visual representations. Via an iterative, user-centered approach, the research informed design requirements that guided a series of design cycles, each leading to the creation of interactive visualization prototypes: MedStory, Doccurate, and ChartWalk. Prototypes were validated by healthcare professionals, and findings underscore the importance of text content being visible front and center, while also informing new ways in which information could be summarized and organized for better overview. Evaluation findings reveal additional requirements to be addressed in follow-up cycles (leading to increasingly refined prototypes), and collective findings lead to a compilation of lessons learned to help inform future design efforts in the space.

Keywords: data visualization
EHR
human-computer interaction
medical record
text visualization
Advisor: Chevalier, Fanny
Brudno, Michael