Cost-Effective Big Data Orchestration via n8n Workflow Automation for Digital Health Transformation in Resource-Constrained Hospitals - Wandi Purnama; Akhmad Unggul Priantoro (Politeknik Ganesha Medan, 2026)
Achieving compliance with Indonesia’s national SATUSEHAT health data mandate remains a complex hurdle for underfunded regional medical centers. The primary operational challenges stem from isolated departmental information architecture combined with the exorbitant licensing expenses of commercial middleware systems. To overcome these barriers, this study introduces a budget-friendly Big Data integration framework powered by n8n, an open-source, low-code workflow engine designed to dynamically unify disparate hospital environments. The methodology employs a Hadoop-based ecosystem and Apache Kafka for robust data ingestion, while n8n automates the Extract, Transform, Load (ETL) process to map raw clinical records into standardized HL7-FHIR JSON resources. Additionally, a lightweight Linear Regression model is applied as a low-compute operational optimization for dynamic batch-size prediction to prevent network overload during data transmission. Experimental results under a 72-hour continuous simulation on a single-core legacy server using 25,000 synthetic records demonstrate that the n8n-driven framework successfully sustains a throughput of 150 to 180 records per minute with a prediction error (RMSE) of 0.042. Furthermore, by eliminating proprietary software licensing fees and utilizing existing hardware, a comparative financial model indicates an estimated 85% reduction in the Total Cost of Ownership (TCO). Ultimately, this research provides a scalable technical blueprint for automating healthcare data integration, enabling under-resourced hospitals to achieve national interoperability mandates efficiently without compromising data integrity or financial stability.