<![CDATA[Delayed post-discharge follow-up among geriatric patients may compromise continuity of care and increase the risk of adverse health outcomes. Early identification of patients at high risk is essential to facilitate timely interventions. This study aimed to develop and validate a prediction model for delayed follow-up visits among hospitalized geriatric patients. A cross-sectional study was conducted among 263 geriatric patients discharged from a general hospital in Medan, Indonesia. Digital health literacy, family support, and medication regimen complexity were assessed using validated instruments. Multivariable logistic regression was performed to identify independent predictors of delayed follow-up, and model performance was evaluated using the area under the receiver operating characteristic curve (AUROC) and the Hosmer–Lemeshow goodness-of-fit test. Delayed post-discharge follow-up occurred in 34.6% of participants. Low digital health literacy (adjusted OR = 2.87), poor family support (adjusted OR = 1.93), and high medication regimen complexity (adjusted OR = 3.76) were independently associated with delayed follow-up. The final prediction model demonstrated good discrimination with an AUROC of 0.821 (95% CI: 0.769–0.873) and satisfactory calibration (Hosmer–Lemeshow p = 0.617), indicating good predictive performance. Low digital health literacy, poor family support, and high medication regimen complexity are significant predictors of delayed post-discharge follow-up among geriatric patients. The proposed prediction model may serve as a practical screening tool to identify patients requiring targeted discharge planning and transitional care interventions, thereby improving continuity of care and supporting healthy ageing.]]>
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