Hybrid DBSCAN - K-Means Clustering for Financial Loss Identification in INA-CBG Claims Based on Medical Treatment Patterns
-
Muhammad Fajar Dianqori
*
Universitas Islam Indonesia, Yogyakarta,
Indonesia
- Dhomas Hatta Fudholi Universitas Islam Indonesia, Yogyakarta, Indonesia
- Galih Aryo Utomo Rumah Sakit Islam Yogyakarta PDHI, Yogyakarta, Indonesia
- Irving Vitra Paputungan Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Malaysia (*) Corresponding Author
Abstract
Hospital financial deficits due to INA-CBG claim discrepancies pose a critical challenge to healthcare sustainability in Indonesia. The difference between hospital operating costs and INA-CBG rates often results in significant financial deficits, which can threaten the sustainability of healthcare providers, especially hospitals. However, existing studies lack a systematic approach to identify distinct patterns of financial losses based on clinical treatment characteristics. This study aims to identify clusters of patients with different financial loss characteristics using a hybrid DBSCAN-K-Means clustering approach based on medical procedure frequency patterns. The DBSCAN algorithm was employed to detect and separate noise from data, while K-Means was used to identify medical treatment patterns. The data were obtained from electronic medical records of inpatients during the 2023–2024 period at a private hospital (N = 6,021 cases). The final clustering results revealed two main clusters with a highly significant difference in deficits between clusters (p = 6.21 × 10⁻³⁸, Cliff's Delta = −0.216). Cluster 0 represents patients with intensive care who have a higher frequency of routine procedures, with an average deficit of 1.51 times (51.3% greater) and an average length of stay of 1.76 times (76% longer) than Cluster 1. Cluster 1 represents patients with a focus on obstetrics/neonatology with a predominance of Doppler procedures. Meanwhile, the noise cluster (13.39%) represents more extreme cases with an average loss of −7.82 million IDR and high mortality. Of the total 315 treatment features, 114 were confirmed to be statistically significant. This study contributes a novel hybrid clustering framework for identifying financial loss patterns in INA-CBG claims, providing actionable insights for hospital management to optimize service utilization, adjust procedure fees for complex cases, and strengthen financial risk management strategies.
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