Some conditions are already known to be associated with an increased risk of osteoporotic fractures. Other conditions may also be significant indicators of increased risk. The aim of the current study was to identify conditions for inclusion in a fracture prediction model (fracture risk evaluation model [FREM]) for automated case finding of high-risk individuals of hip or major osteoporotic fractures (MOFs). We included the total population of Denmark aged 45+ years (N = 2,495,339). All hospital diagnoses from 1998 to 2012 were used as possible conditions; the primary outcome was MOFs during 2013. Our cohort was split randomly 50/50 into a development and a validation dataset for deriving and validating the predictive model. We applied backward selection on ICD-10 codes (International Classification of Diseases and Related Health Problems, 10th Revision) by logistic regression to develop an age-adjusted and sex-stratified model. The FREM for MOFs included 38 and 43 risk factors for women and men, respectively. Testing FREM for MOFs in the validation cohort showed good accuracy; it produced receiver-operating characteristic (ROC) curves with an area under the ROC curve (AUC) of 0.750 (95% CI, 0.741 to 0.795) and 0.752 (95% CI, 0.743 to 0.761) for women and men, respectively. The FREM for hip fractures included 32 risk factors for both genders and showed an even higher accuracy in the validation cohort as AUCs of 0.874 (95% CI, 0.869 to 0.879) and 0.851 (95% CI, 0.841 to 0.861) for women and men were found, respectively. We have developed and tested a prediction model (FREM) for identifying men and women at high risk of MOFs or hip fractures by using solely existing administrative data. The FREM could be employed either at the point of care integrated into electronic patient record systems to alert physicians or deployed centrally in a national case-finding strategy where patients at high fracture risk could be invited to a focused DXA program.