Research Focus: Health system analytics using administrative claims data (Indonesia JKN)
I study how health service delivery actually happens in large insurance systems by reconstructing patient care trajectories from routine claims data. My goal is to identify provider behavior, referral dynamics, and clinically implausible utilization patterns that are invisible in conventional reporting.
This work sits between health policy, clinical epidemiology, and real-world data science.
Health insurance data analyst working on Indonesia’s National Health Insurance (JKN) administrative claims data.
My work focuses on transforming raw claims data into clinically meaningful episodes of care, referral pathways, and policy indicators for strategic purchasing and fraud detection.
- Patient episode construction from fragmented claims
- Referral network and provider behavior analysis
- Detection of abnormal clinical utilization patterns
- Cost escalation and catastrophic care predictors
- Strategic purchasing indicators for JKN
- BigQuery (claims warehouse & large-scale SQL analytics)
- R (analysis, modeling, and reproducible pipelines)
- GitHub (reproducible research workflow)
- JKN Claims Warehouse — SQL data warehouse converting claims into patient episodes
- JKN Data Platform — reproducible dataset builder for administrative claims
- Obstetric Fraud Detection — abnormal delivery pattern detection
- Strategic Purchasing EWS — early warning system for cost escalation
To support evidence-based policy and clinical governance using real-world administrative health data.