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The article explores the use of a visual interactive analytic tool called VIADS in hypothesis generation for clinical researchers. The study compares the effectiveness of VIADS with other tools and found that while the VIADS group generated hypotheses faster, the quality of their hypotheses was rated lower. The study suggests that further research is needed to understand the role of VIADS in hypothesis generation and improve its effectiveness. Another study discussed in the text found that reading and interactions with others were common activities that led to new research ideas, and participants provided positive feedback on the usability of VIADS. However, the study did not find conclusive evidence on the role of VIADS in generating high-quality hypotheses. The document also includes a list of references related to hypothesis generation and scientific discovery. [Extracted from the article]

Objectives: To compare how clinical researchers generate data-driven hypotheses with a visual interactive analytic tool (VIADS, a visual interactive analysis tool for filtering and summarizing large data sets coded with hierarchical terminologies) or other tools., Methods: We recruited clinical researchers and separated them into "experienced" and "inexperienced" groups. Participants were randomly assigned to a VIADS or control group within the groups. Each participant conducted a remote 2-hour study session for hypothesis generation with the same study facilitator on the same datasets by following a think-aloud protocol. Screen activities and audio were recorded, transcribed, coded, and analyzed. Hypotheses were evaluated by seven experts on their validity, significance, and feasibility. We conducted multilevel random effect modeling for statistical tests., Results: Eighteen participants generated 227 hypotheses, of which 147 (65%) were valid. The VIADS and control groups generated a similar number of hypotheses. The VIADS group took a significantly shorter time to generate one hypothesis (e.g., among inexperienced clinical researchers, 258 seconds versus 379 seconds, p = 0.046, power = 0.437, ICC = 0.15). The VIADS group received significantly lower ratings than the control group on feasibility and the combination rating of validity, significance, and feasibility., Conclusion: The role of VIADS in hypothesis generation seems inconclusive. The VIADS group took a significantly shorter time to generate each hypothesis. However, the combined validity, significance, and feasibility ratings of their hypotheses were significantly lower. Further characterization of hypotheses, including specifics on how they might be improved, could guide future tool development., Competing Interests: Competing interests disclosure statement N/A

Background: Visualization can be a powerful tool to comprehend data sets, especially when they can be represented via hierarchical structures. Enhanced comprehension can facilitate the development of scientific hypotheses. However, the inclusion of excessive data can make visualizations overwhelming., Objective: We developed a visual interactive analytic tool for filtering and summarizing large health data sets coded with hierarchical terminologies (VIADS). In this study, we evaluated the usability of VIADS for visualizing data sets of patient diagnoses and procedures coded in the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)., Methods: We used mixed methods in the study. A group of 12 clinical researchers participated in the generation of data-driven hypotheses using the same data sets 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) survey and a brief survey with open-ended questions were administered after the study to assess the usability of VIADS and verify their intense usage experience with VIADS., Results: The range of SUS scores was 37.5 to 87.5. The mean SUS score for VIADS was 71.88 (out of a possible 100, SD 14.62), and the median SUS was 75. The participants unanimously agreed that VIADS offers new perspectives on data sets (12/12, 100%), while 75% (8/12) agreed that VIADS facilitates understanding, presentation, and interpretation of underlying data sets. The comments on the utility of VIADS were positive and aligned well with the design objectives of VIADS. The answers to the open-ended questions in the modified SUS provided specific suggestions regarding potential improvements for VIADS, and the identified problems with usability were used to update the tool., Conclusions: This usability study demonstrates that VIADS is a usable tool for analyzing secondary data sets with good average usability, good SUS score, and favorable utility. Currently, VIADS accepts data sets with hierarchical codes and their corresponding frequencies. Consequently, only specific types of use cases are supported by the analytical results. Participants agreed, however, that VIADS provides new perspectives on data sets and is relatively easy to use. The VIADS functionalities most appreciated by participants were the ability to filter, summarize, compare, and visualize data., International Registered Report Identifier (irrid): RR2-10.2196/39414., (©Xia Jing, Vimla L Patel, James J Cimino, Jay H Shubrook, Yuchun Zhou, Brooke N Draghi, Mytchell A Ernst, Chang Liu, Sonsoles De Lacalle. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 27.04.2023.)