Objectives: The objective of this study was to develop machine learning models to predict health insurance claim costs among older adults in Indonesia. Methods: This study utilized secondary data from the Indonesian National Health Insurance program (Jaminan Kesehatan Nasional [JKN]) spanning 2017 to 2023. Three modeling techniques—linear regression, random forest, and XGBoost—were employed to predict individual claim costs. Model performance was assessed using the root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE). Additionally, variable importance analysis was conducted to identify key predictors. Results: XGBoost with 500 boosting rounds yielded the best performance, with an RMSE of 11 360 283, an R2 of 0.81, and an MAE of 4 485 917, outperforming both linear regression (RMSE, 13 710 035; R2=0.72) and random forest (RMSE, 12 434 238; R2=0.78). Notably, outpatient care was identified as the most consistent predictor across all models. Other significant predictors included length of stay (LOS), diagnosis type (International Classification of Diseases, 10th revision chapter), facility type, facility classification, and severity of illness, particularly for moderate cases. Although LOS and diagnosis type were important predictors, these findings should be interpreted in the context of Indonesia’s fixed Indonesian Case-Based Groups payment system. Conclusions XGBoost provides reliable predictions of claim costs among older adults, capturing clinical, utilization, and structural drivers. These findings can inform targeted interventions, improve chronic disease management, optimize the referral system, and support integration of predictive tools into JKN to enhance responsiveness and promote sustainable, equitable financing.

Eggshell (ES) is a waste material that cannot be consumed with low economic value. ES mainly contains calcium carbonate (CaCO3) and an organic matrix in the form of proteins, glycoproteins, and proteoglycans. Meanwhile, the eggshell membrane (ESM) contains osteopontin collagen, fibronectin, keratin, histones, avian beta defensins, ovocalyxin-36, apolipoproteins, protocadherin, chondroitin sulfate, ovotransferrin, hyaluronic acid, and sialic acid as well as various amino acids. Recently, ES has been widely used in industry, agriculture, food, and medical fields. The potential of ES in the medical field is interesting to discuss, especially in relation to tissue repair. Efforts to reduce the prevalence of wounds that generally originate from acute wounds but become chronic due to various factors that are neglected in their management. In particular, this review will describe the benefits of ES content in repairing body tissues. ES-derived active ingredients such as CaCO3, brushite, and hydroxyapatite exhibit osteoconductive properties that promote bone regeneration. Calcium ions can increase insulin and leptin sensitivity in the liver and can induce repair of acute kidney injury. Meanwhile, ESM contributes positively to neural tissue repair and plays an important role in wound healing, response to external stimuli, defense response, inflammatory response, cell-substrate adhesion, promoting cell growth, migration, differentiation, and tissue remodeling.