Comparative Assessment of Two Data Visualizations to Communicate Medical Test Results Online

ABSTRACT

As most countries in the world still struggle to contain the COVID-19 breakout, Data Visualization tools have become increasingly important to support decision-making under uncertain conditions. One of the challenges posed by the pandemic is the early diagnosis of COVID-19 To this end, machine learning models capable of detecting COVID-19 on the basis of hematological values have been developed and validated. This study aims to evaluate the potential of two Data Visualizations to effectively present the output of a COVID-19 diagnostic model to render it online. Specifically, we investigated whether any visualization is better than the other in communicating a COVID-19 test results in an effective and clear manner, both with respect to positivity and to the reliability of the test itself. The findings suggest that designing a visual tool for the general public in this application domain can be extremely challenging for the need to render a wide array of outcomes that can be affected by varying levels of uncertainty.