Background: Access to healthcare and social services remains constrained for vulnerable populations due to systemic barriers rooted in social determinants of health (SDOH). Addressing these challenges requires innovative approaches that leverage health informatics to transform data into actionable insights. This project collaborated with the Randolph County Caring Community Partnership (RCCCP) to explore how open-source platforms like DHIS2 could enhance SDOH data utilisation for equitable resource allocation. Methods: The study integrated Self-Sufficiency Survey data into DHIS2, employing Docker for system configuration and PostgreSQL for database management. Data preprocessing involved Excel and R for cleaning and label encoding. The Support Need Index categorised respondents into high, moderate, and low-need groups. Power BI generated visualisations, including pie charts and stacked column graphs, to identify disparities across demographics and geographies. Weekly stakeholder meetings ensured alignment between technical outputs and community health objectives. Results: Analysis revealed 81.16% of respondents faced high needs, with Randolph County disproportionately affected. Ethnic disparities emerged, particularly among Mexican and Central American populations. Dashboards effectively mapped needs distribution, enabling targeted intervention planning. Technical hurdles in DHIS2 visualisation necessitated supplemental Power BI solutions, underscoring the importance of flexible tool integration. Conclusion: The project demonstrates how informatics tools can bridge gaps between SDOH data and community health interventions. Future implementations should prioritise scalable, adaptable frameworks while addressing technical and ethical challenges. This approach offers a replicable model for data-driven health equity initiatives.
Adepoju et al. (Sat,) studied this question.
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