Background <p>Unplanned 30-day readmissions in discharged older adults often reflect unmet needs and fragmented care transitions. Existing prediction models frequently lack geriatric specificity, clinical interpretability, and applicability to decentralised European healthcare systems. This study aimed to develop a clinically interpretable risk model for older adults using routine discharge data. Findings may provide a basis for targeted interventions.</p> Methods <p>We conducted a retrospective cohort study using electronic health record (EHR) data from a Swiss multi-site hospital network. Adults aged ≥ 65 years discharged alive in 2024 were eligible, excluding planned readmission, oncology/palliative cases, in-hospital deaths, and records with missing predictors. Of 15,635 discharges, 12,814 met inclusion criteria. 9,429 (60.3%) were retained for complete-case analysis. A multivariable logistic regression model with backward selection predicted unplanned 30-day readmission. Model performance was evaluated by AUC, calibration, decision curve analysis (DCA), and bootstrap validation, with a Fine-Gray model addressing competing risk of mortality.</p> Results <p>Among 9,429 patients (mean age 78.0 years; 45.5% female), 1,845 (19.6%) had an unplanned 30-day readmission. Independent predictors were polypharmacy (OR 1.83), home care (OR 1.61), male sex (OR 1.42), low fall risk (vs. high; OR 1.41), moderate care intensity (vs. high; OR 1.30), comorbidity count (OR 1.12/diagnosis), younger age (OR 0.99/year), and shorter stay (OR 0.98/day). The model showed an AUC of 0.716 (95% CI 0.706–0.726), but calibration was poor, with risk overestimation at low predicted probabilities. At a 10% threshold, sensitivity was 7.5%, specificity 98.1%, positive predictive value 49.5%, and negative predictive value 81.4%. Compared with LACE index (AUC 0.707, 95% CI 0.694–0.720), performance improved modestly but significantly (<i>p</i> &lt; 0.01).</p> Conclusions <p>Nearly one in five older adults was readmitted within 30 days, underscoring the burden of rehospitalisation and its link to quality-of-care. The model identified clinically relevant, EHR-available predictors, including medical complexity and care dependency. Despite acceptable discrimination, calibration was suboptimal and sensitivity low, limiting immediate clinical application. Future refinement and external validation may enhance utility. This model may provide a pragmatic starting point for risk-stratified discharge planning research and guiding transitional care interventions. </p> Trial registration <p>Study protocol was registered on the Open Science Framework (OSF) (<a href="https://osf.io/vkte2/">https://osf.io/vkte2/</a>; 10 May 2025).</p>