Using Business Intelligence Tools to Monitor Chronic Disease Trends across Demographics - Azeez Kunle Akinbode,; Florence Ifeanyichukwu Olinmah,; Onyeka Kelvin Chima,; Babawale Patrick Okare,; Tope David Aduloju, (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024)
Chronic diseases such as diabetes, cardiovascular illnesses, and respiratory disorders remain leading causes of morbidity and mortality worldwide, with disproportionate impacts across age, gender, racial, and socioeconomic groups. Traditional surveillance systems often lack the granularity and timeliness necessary to detect emerging patterns, leading to delayed public health responses. This study investigates the application of Business Intelligence (BI) tools to monitor chronic disease trends across diverse demographic segments. Leveraging data integration, visualization, and predictive analytics capabilities, BI platforms provide real-time insights into the progression and prevalence of chronic diseases within population subgroups. The research utilizes publicly available datasets including electronic health records (EHRs), hospital admissions, and public health surveys to develop interactive dashboards that track disease burden across demographic indicators such as age, race, income level, and geographic location. Through case analysis and comparative trend evaluation, this study demonstrates how BI tools such as Microsoft Power BI, Tableau, and QlikView enable health professionals and policymakers to identify high-risk populations, allocate resources more efficiently, and evaluate the effectiveness of intervention strategies. The dashboards also support temporal and spatial trend analysis, revealing disparities in disease incidence and treatment access. Results indicate that when BI systems are embedded within public health infrastructure, they enhance surveillance accuracy, foster data-driven decision-making, and enable proactive response to chronic disease escalation. Additionally, incorporating machine learning models into BI platforms improves forecasting capabilities, facilitating early warning systems for emerging hotspots and population health risks. This research underscores the transformative potential of Business Intelligence in chronic disease epidemiology, advocating for its broader integration in health monitoring systems. It highlights key considerations for successful implementation, including data privacy, interoperability, and training for end-users. Ultimately, the study supports the strategic use of BI tools to bridge health equity gaps and optimize chronic disease management across demographics. The findings align with national public health goals to reduce preventable deaths, improve quality of life, and ensure equitable access to care through technology-enabled solutions.
Keywords: Business Intelligence; Chronic Disease; Demographics; Health Surveillance; Predictive Analytics; Public Health Informatics; Data Visualization; Health Equity; Disease Trends; Population Health Management