Department of Surgery and Perioperative Care, Dell Medical School, University of Texas at Austin, Austin, Texas.
J Orthop Res. 2020 Jul;38(7):1414-1422. doi: 10.1002/jor.24614. Epub 2020 Feb 24.
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.
多达三分之一的全关节置换（TJR）手术可能在一部分对手术结果仍不满意的患者中不恰当地进行，这强调了共同决策的重要性。患者报告的结果测量从患者的角度捕捉身体、情感和社会健康和福祉方面。能够使用不同类型的数据（包括来自患者的报告，例如患者报告的结果）进行高度复杂分析的强大计算机系统可以消除猜测，生成有影响力的指标来更好地为决策过程提供信息。我们创建了一个共同决策工具，该工具可以根据患者报告的结果以及临床和人口统计学数据，生成 TJR 的风险和收益的个性化预测。我们介绍了一项随机对照试验的方案，旨在评估该工具对决策质量、共同决策水平以及患者和流程结果的影响。我们还讨论了该领域的当前概念，并强调了利用患者报告的数据和人工智能在整个护理连续体中提供决策支持的机会。
