Background Medication harm has negative clinical and economic consequences, contributing to hospitalisation, morbidity and mortality. The incidence ranges from four to 14%, of which up to 50% of events may be preventable. A predictive model for identifying high-risk inpatients can guide a timely and systematic approach to prioritisation. Aim To develop and internally validate a risk prediction model, for prioritisation of hospitalised patients, at risk of medication harm. Methods A retrospective cohort study was conducted in general medical and geriatric specialties at an Australian hospital, over six months. Medication harm was identified using International Classification of Disease (ICD-10) codes and the hospital’s incident database. Sixty-eight variables, including medications and laboratory results, were extracted from the hospital’s databases. Multivariable logistic regression was used to develop the final risk model. Performance was evaluated using area under the receiver operative characteristic curve (AuROC) and clinical utility was determined using decision curve analysis. Results The study cohort included 1982 patients median age 74 years, of which 136 (7%) experienced ≥1 adverse medication event(s). The model included: length of stay, hospital re-admission within 12 months, venous or arterial thrombosis &/or embolism, ≥ 8 medications, serum sodium < 126 mmol/L, INR > 3, anti-psychotic, antiarrhythmic and immunosuppressant medications, and history of medication allergy. Validation gave an AuROC of 0.70 (95% CI: 0.65-0.74). Decision curve analysis identified that the AIME may be clinically useful to help guide decision making in practice. Conclusion We have developed a risk prediction model with reasonable performance. Future steps include external validation.