Doccurate: A Curation-Based Approach for Clinical Text Visualization

Abstract

Before seeing a patient, physicians seek to obtain an overview of the patient's medical history. Text plays a major role in this activity since it represents the bulk of the clinical documentation, but reviewing it quickly becomes onerous when patient charts grow too large. Text visualization methods have been widely explored to manage this large scale through visual summaries that rely on information retrieval algorithms to structure text and make it amenable to visualization. However, the integration with such automated approaches comes with a number of limitations, including significant error rates and the need for healthcare providers to fine-tune algorithms without expert knowledge of their inner mechanics. In addition, several of these approaches obscure or substitute the original clinical text and therefore fail to leverage qualitative and rhetorical flavours of the clinical notes. These drawbacks have limited the adoption of text visualization and other summarization technologies in clinical practice. In this work we present Doccurate, a novel system embodying a curation-based approach for the visualization of large clinical text datasets. Our approach offers automation auditing and customizability to physicians while also preserving and extensively linking to the original text. We discuss findings of a formal qualitative evaluation conducted with 6 domain experts, shedding light onto physicians' information needs, perceived strengths and limitations of automated tools, and the importance of customization while balancing efficiency. We also present use case scenarios to showcase Doccurate's envisioned usage in practice.

Criteria for Rigor in Visualization Design Study

“…Many perceptual, cognitive, and computational phenomena can be studied effectively through controlled, empirical studies where objectivity, repeatability, and prediction are valued and efforts are made to remove bias and error. Studying people and their considered, complex, contextualized, social reactions to dynamic settings often benefits instead from relativist approaches that involve subjective interpretation of qualitative data [30,57,61,63,107,108,113]. A key concern, however, is that work that is conducted from one position is judged from another [28].…”

“…Design study -an approach to applied visualization research [96] -is now a standard method for conducting visualization inquiry, guided by validation methods [78,82], process models [72,74,96], scenarios [95], and an increasing set of representative examples in the literature [9,43,56,62,77,79,84,107,117]. In the context of the wider visualization discipline that is increasingly assessing its practices, the maturing of design study has exposed a series of provocative, open questions that we hear researchers asking: What are the research contributions made through design studies, and do they generalize?…”

The Validity, Generalizability and Feasibility of Summative Evaluation Methods in Visual Analytics

“…Therefore, researchers include subjective assessment methods as ways of determining a solution's usefulness. Subjective assessment can be performed quantitatively [61,76] or qualitatively [27], and can be done with the help of human subjects [88] or through inspecting the design without relying on human subjects [42]. Qualitative methods have the flexibility to assess both objective and subjective aspects.…”

Data Streams Curation for Better Machine Learning Functionality and Result to Serve IoT and other Applications: A Survey

“…The introduced framework is particularly useful in the medical industry and can be easily integrated with big data platforms deployed by health care institutions, to ensure the continual assessment of the quality of electronic medical and health records. Hence, it's important to ensure the availability of curated datasets in the healthcare industry, which is currently driving research regarding the creation of platforms specifically designed for the curation of healthcare data (Landis et al, 2015;Wollatz et al, 2018;Sultanum et al, 2018).…”

“…Landis et al, 2015;2. Healthcare data curation (Landis et al, 2015;Wollatz et al, 2018;sultanum et al, 2018). Wollatz et al, 2018;3.…”