PURPOSE: To devise, validate, and test a software model that improves how clinicians calculate individual risk for osteoporotic fracture and expected treatment benefit. METHODS: We developed a simple model of seven easily ascertained items plus bone mineral density (BMD) that calculates absolute fracture risk and expected absolute risk reduction after treatment. Baseline clinical variables and longitudinal fracture data from two large osteoporosis cohort studies validated the model’s accuracy in predicting fracture risk. We then surveyed 298 clinicians to evaluate the likelihood they would prescribe alendronate in three hypothetical cases, first given the clinical data alone and then with model-derived data on fracture risk and expected treatment benefit. RESULTS: We found a strong linear relationship with the model’s predicted fracture risk and observed fracture rates in two large observational cohorts but the model overestimated risk 2-3 fold. The model predicted a 1:200 5-year risk for spinal fracture and a 1:40 risk for nonspinal fracture in an index case of a younger, thin, osteopenic woman. Given this hypothetical history with BMD t-scores, 26% of clinicians were likely to prescribe alendronate; when also given model-calculated 5-year fracture risks with or without treatment, only 13% were likely to prescribe alendronate (p 0.001). For 2 other osteoporosis patients in whom risk was much higher, further information on fracture risk and expected treatment benefit did not alter prescribing. CONCLUSIONS: Reporting absolute fracture risk with and without treatment promises to be most useful in women with osteopenia, a common clinical dilemma in younger postmenopausal women.