The current healthcare environment in America is driven by the concepts of quality, cost containment, and value. In this environment, primary hip and knee arthroplasty procedures have been targeted for cost containment through quality improvement initiatives intended to reduce the incidence of costly complications and readmissions. Accordingly, risk prediction tools have been developed in an attempt to quantify the patient-specific assessment of risk. Risk prediction tools may be useful for the informed consent process, for enhancing risk mitigation efforts, and for risk-adjusting data used for reimbursement and the public reporting of outcomes. The evaluation of risk prediction tools involves statistical measures such as discrimination and calibration to assess accuracy and utility. Furthermore, prediction tools are tuned to the source dataset from which they are derived, require validation with external datasets, and should be recalibrated over time. However, a high-quality, externally validated risk prediction tool for adverse outcomes after primary total joint arthroplasty remains an elusive goal.