Abstract: An innovative digital health management system has been designed with the intent of improving healthcare services in rural sub-centres. It allows health workers to register individuals and automatically generate unique IDs, streamlining the tracking and updating of patient medical records across different levels of care. By utilizing sequential ID generation, the system employs an auto-incremental ID generation feature within the database to assign each patient a distinct identifier upon registration, thereby facilitating efficient data management and patient follow-up. This system specifically focuses on diabetes and hypertension; it gathers comprehensive health data and utilizes a multinomial logistic regression model to categorize patients’ health conditions as healthy, diabetic, hypertensive, or both diabetic and hypertensive. Based on these classifications, health workers can create personalized lifestyle management plans tailored to each individual’s health needs. For infants and children under 18, the system offers detailed immunization charts, while pregnant women receive vaccination schedules and nutrition guidelines to ensure comprehensive prenatal care. In more complex cases, health workers can refer patients to doctors at Primary Health Centres (PHCs), and if advanced diagnostics or specialized treatments are required, PHCs can further refer patients to hospitals. The unique ID facilitates seamless information sharing and updates among sub-centres, PHCs, and hospitals, enhancing continuity of care and improving health outcomes in rural areas. This integrated approach aims to bridge the healthcare gap in underserved regions by leveraging data and predictive analytics to provide targeted and effective healthcare services.

Keywords: Digital Health Management System, Rural Healthcare, Unified Patient ID System, Predictive Analytics, Multinomial Logistic Regression

| DOI: 10.17148/IARJSET.2024.11840