The quality of data-driven hypotheses generated by inexperienced clinical researchers: A case study.

Objectives: We invited inexperienced clinical researchers to analyze coded health datasets and develop hypotheses. We recorded and analyzed their hypothesis generation process. All the hypotheses generated in the process were rated by the same group of seven experts by using the same metrics.

Data-driven hypothesis generation among inexperienced clinical researchers: A comparison of secondary data analyses with visualization (VIADS) and other tools.

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 datasets 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.

How do clinical researchers generate data-driven scientific hypotheses? Cognitive events using think-aloud protocol.

Objectives: This study aims to identify the cognitive events related to information use (e.g., "Analyze data", "Seek connection") during hypothesis generation among clinical researchers.

Data-driven hypothesis generation among inexperienced clinical researchers: A comparison of secondary data analyses with visualization (VIADS) and other tools.

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.

A Visual Analytic Tool (VIADS) to Assist the Hypothesis Generation Process in Clinical Research: Mixed Methods Usability Study.

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.

Predicting Areas with High Concentration of the Long-Term Uninsured and Their Association with Emergency Department Usage by Uninsured Patients in South Carolina.

Background: To predict areas with a high concentration of long-term uninsured (LTU) and Emergency Department (ED) usage by uninsured patients in South Carolina.

Methods: American Community Survey data was used to predict the concentration of LTU at the ZIP Code Tabulation Area (ZCTA) level. In a multivariate regression model, the LTU concentration was then modeled to predict ED visits by uninsured patients.