Enhancing Clinical Coding Expertise in Indonesia’s National Health Insurance Program - Gultom, Novianti Br.; Saputra, Arisandy; Surini, Dwi; Erwinsyah; Novelia, Elsa; Johana; Dosiema, Vernanda; Corina, Intan; Ratnafuri, Miranti; Adi Wijayanti, Fallah; Setyawan, Dedy; Januar Prakarsa, Egar; Pali'padang, Sarman; Wardanu, Gilang Yoga; Surosa, Lia Yulianti; Langenbrunner, Jack; Blake, John (Jurnal Jaminan Kesehatan Nasional, 2025)
Coding medical records using classification systems can cause inconsistencies, sometimes leading to claim disputes. In January 2021, 440,749 disputed cases were reported, with a total disputed cost amounting to Rp873,111,325,287 for referral healthcare facility claims under the National Health Insurance (JKN). In May 2021, BPJS Kesehatan established the Clinical Coding Expert/Tim Ahli Pengodean Klinis (TAPK) for enhancing clinical coding expertise. The objective of this study was to examine the impact of establishing TAPK. We hypothesize that TAPK affects the decrease in disputed cases in 12 regions of Indonesia. We used data from the JKN Program prior to the establishment of TAPK in January 2021 as controls. We followed up on 86,272 cases for two years after its implementation (as of April 2023). Paired data from 12 regions were compared using descriptive statistics, inferential statistics (paired sample t-test), and boxplot visualization. We also described the knowledge management of clinical coding through Knowledge Spiral of Nonaka and Takeuchi. The finding showed a decrease in the number of claim dispute cases in April 2023 compared to January 2021 (by 80.43%). The average claim cases decreased from 36,729.08 (Before TAPK) to 7,189.33 (After TAPK; t = 2.620, p = 0.0238). TAPK has contributed to standardized coding practices in hospitals across regions. This study reinforces the importance of action to improve competence and organizational learning in TAPK through Knowledge Spiral Model and recommends that TAPK be more widely known to all JKN’s healthcare facilities. Future research should optimize AI-driven clinical coding while ensuring human oversight.