Humanizing Big Data and Detailing Social Determinants of Health via Information Visualizations.

The pandemic has had devastating impacts on humanity and the global healthcare sector. An analysis into the social determinants of health, in particular racial and ethnic disparities may explain why certain population groups have been disproportionately affected by COVID-19. The objective of this study is to humanize and personify numerical data.

Towards the Adoption of Novel Visualizations in Public Health.

Visualizations form an important part of public health informatics (PHI) communications. Visualizing data facilitates discussion, aids understanding, makes patterns apparent, promotes analysis, and fosters recall. How rare are novel visualizations in the PHI literature? In Phase 1, we used a rapid review methodology to test the commonness of the Sankey diagram in the PHI theory literature via an automated text search for key terms.

Factors Influencing Clinician Trust in Predictive Clinical Decision Support Systems for In-Hospital Deterioration: Qualitative Descriptive Study.

Background: Clinician trust in machine learning-based clinical decision support systems (CDSSs) for predicting in-hospital deterioration (a type of predictive CDSS) is essential for adoption. Evidence shows that clinician trust in predictive CDSSs is influenced by perceived understandability and perceived accuracy.

Objective: The aim of this study was to explore the phenomenon of clinician trust in predictive CDSSs for in-hospital deterioration by confirming and characterizing factors known to influence trust (understandability and accuracy), uncovering and describing other influencing factors, and comparing nurses' and prescribing providers' trust in predictive CDSSs.