JMIR Publications

ABSTRACT

Background:

Visualization can be a powerful tool for comprehending datasets, especially when datasets can be represented via hierarchical structures. Enhanced comprehension can facilitate users to develop scientific hypotheses. However, the inclusion of excessive data can make a visualization overwhelming.

Objective:

We developed a Visual Interactive Analytic tool for filtering and summarizing large health Data Sets (VIADS) coded with hierarchical terminologies. In this study, we evaluated the usability and utility of VIADS for visualizing datasets of patients’ diagnoses and procedures coded in ICD-9-CM.

Methods:

We used mixed methods in the study. A group of 12 clinical researchers participated in data-driven hypotheses generation using the same datasets and time frame (a 1-hour training session and a 2-hour study session), utilizing VIADS via the think-aloud protocol. The audio and screen activities were recorded remotely. A modified version of the System Usability Scale (SUS) and a brief survey with open-ended questions were administered after the study.

Results:

The range of SUS scores was 37.5–87.5. The mean SUS score for VIADS was 71.88 (± 14.62) (out of a possible 100), and the median SUS was 75. Participants agreed unanimously that VIADS offers new perspectives on datasets (100%); while 75% of participants agreed that VIADS facilitates the understanding, presentation, and interpretation of underlying datasets. The comments on the utility of VIADS were overwhelmingly positive and aligned very well with the design purposes of VIADS. The answers to the open-ended questions in the modified SUS provided specific suggestions from participants about how VIADS could be improved. Identified problems in usability were used to update the tool.

Conclusions:

This usability and utility study demonstrated that VIADS is high in utility with a good average usability SUS score and a valuable tool for analyzing secondary datasets. Currently, VIADS accepts datasets with hierarchical codes and their corresponding frequencies. Accordingly, the analytical results support only particular types of use cases. Nevertheless, participants unanimously agreed that VIADS provides new perspectives on datasets and is relatively easy to use. The functionalities mostly appreciated by participants were VIADS’ ability to filter, summarize, compare, and visualize data.