Up to one-third of total joint replacement (TJR) procedures may be performed inappropriately in a subset of patients who remain dissatisfied with their outcomes, stressing the importance of shared decision-making. Patient-reported outcome measures capture physical, emotional, and social aspects of health and wellbeing from the patient's perspective. Powerful computer systems capable of performing highly sophisticated analysis using different types of data, including patient-derived data, such as patient-reported outcomes, may eliminate guess work, generating impactful metrics to better inform the decision-making process. We have created a shared decision-making tool which generates personalized predictions of risks and benefits from TJR based on patient-reported outcomes as well as clinical and demographic data. We present the protocol for a randomized controlled trial designed to assess the impact of this tool on decision quality, level of shared decision-making, and patient and process outcomes. We also discuss current concepts in this field and highlight opportunities leveraging patient-reported data and artificial intelligence for decision support across the care continuum.