Transitioning from INA-CBG to INA-DRG: Implementation Challenges and the Role of AI-Augmented Claim Verification in Indonesian Hospitals
Year: 2026
Type: article
Abstract: Background. Indonesia's national health insurance program (Jaminan Kesehatan Nasional, JKN), administered by BPJS Kesehatan, has used the Indonesia Case Based Groups (INA-CBG) prospective payment system since 2014. The Ministry of Health and the National Casemix Center (NCC) are transitioning toward Indonesia Diagnosis Related Groups (INA-DRG), modeled on internationally established DRG systems. As of early 2026, the transition is in a parallel-running and pilot expansion phase. Objective. This paper analyzes the structural, operational, and information-system implications of the INA-CBG to INA-DRG transition for Indonesian hospitals, and examines the role of AI-augmented claim verification tools as a mitigation mechanism for documentation and coding gaps during the transition period. Methods. We combined a narrative review of peer-reviewed literature on DRG implementation in low- and middle-income countries, regulatory document analysis of Kemenkes and BPJS Kesehatan publications, and an anonymized aggregate analysis of inpatient claim verification metrics collected from a multi-site Indonesian hospital cohort during 2024–2026, derived from de-identified aggregate metrics generated during routine claim verification workflows. Results. Approximately 41% of primary diagnoses and 53% of secondary diagnoses use unspecified ICD-10 codes; 22% of cases contain zero documented secondary diagnoses. Under simulated INA-DRG classification, ~18% of cases shift upward and ~14% downward in severity tier, with direction strongly associated with hospital class. The within-DRG documentation premium ranges 40-80% of base tariff for common adult medical and surgical DRGs. Conclusion. The INA-DRG transition is best understood as an institutional change in clinical documentation practice, not a software upgrade. Hospitals supported by AI-augmented verification tooling and SATUSEHAT-aligned data infrastructure are positioned to capture revenue that less prepared peers are likely to lose.
Author: Hapsari, Choirunnisa
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7156222472
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