Project SIGNAL: A Dashboard for Supporting Community Confidence in Making Data-Driven Decisions.

Area of Science:

• Public Health Surveillance
• Health Disparities Research
• Geospatial Data Analytics

Background:

• Data dashboards are vital for public health surveillance and resource allocation, especially during pandemics like COVID-19.
• Nonprofit community-based organizations require tailored tools and training for effective public health partnerships.
• Understanding the specific needs of community organizations is crucial for developing impactful public health initiatives.

Purpose of the Study:

• To develop and evaluate Project SIGNAL, a spatiotemporal decision tool for community organizations.
• To assess the effectiveness of an interactive dashboard and training program in enhancing community leaders' data literacy and decision-making capacity.
• To identify and address neighborhood-level disparities in COVID-19-related outcomes.

Main Methods:

• Project SIGNAL (Spatiotemporal Insights to Guide Nuanced Actions Locally) was developed through a partnership between the Rhode Island Department of Health and Brown University.
• An interactive online dashboard was created using principles of the CDC Clear Communication Index, focusing on spatiotemporal analytics.
• A 6-hour virtual workshop series was implemented to train community leaders on dashboard utilization and public health data concepts.

Main Results:

• The Project SIGNAL dashboard has been accessed over 7500 times since its launch in August 2022.
• Eighty-four community leaders received training, reporting increased confidence in using public health metrics for decision-making.
• The initiative successfully equipped leaders to utilize spatiotemporal methods for identifying and addressing local health disparities.

Conclusions:

• Project SIGNAL is a pioneering effort in integrating best practices into a spatiotemporal decision tool specifically for community organizations.
• Pairing data dashboards with hands-on training empowers community leaders to leverage advanced analytics for localized public health action.
• This approach demonstrates a successful model for enhancing community capacity in addressing health inequities and preparing for future public health emergencies.