Table_1_Social Determinants of Health in Physiatry: Challenges and Opportunities for Clinical Decision Making and Improving Treatment Precision.DOCX

Physiatry is a medical specialty focused on improving functional outcomes in patients with a variety of medical conditions that affect the brain, spinal cord, peripheral nerves, muscles, bones, joints, ligaments, and tendons. Social determinants of health (SDH) play a key role in determining therapeutic process and patient functional outcomes. Big data and precision medicine have been used in other fields and to some extent in physiatry to predict patient outcomes, however many challenges remain. The interplay between SDH and physiatry outcomes is highly variable depending on different phases of care, and more favorable patient profiles in acute care may be less favorable in the outpatient setting. Furthermore, SDH influence which treatments or interventional procedures are accessible to the patient and thus determine outcomes. This opinion paper describes utility of existing datasets in combination with novel data such as movement, gait patterning and patient perceived outcomes could be analyzed with artificial intelligence methods to determine the best treatment plan for individual patients in order to achieve maximal functional capacity.

康复医学是一门专注于改善受大脑、脊髓、周围神经、肌肉、骨骼、关节、韧带和肌腱等不同医学条件影响的患者的功能结果的医学专科。健康的社会决定因素（SDH）在确定治疗过程和患者功能结果方面发挥着关键作用。大数据和精准医疗在其他领域已被广泛应用，在一定程度上也应用于康复医学以预测患者结果，然而仍存在许多挑战。健康的社会决定因素与康复医学结果之间的相互作用在不同护理阶段高度可变，而在急性护理阶段中较为有利的患者特征可能在门诊环境中则不那么有利。此外，健康的社会决定因素影响患者可获取的治疗方法或干预程序，从而决定结果。本文探讨了现有数据集与新型数据（如运动、步态模式及患者感知结果）的结合使用，通过人工智能方法分析，以确定针对个别患者的最佳治疗方案，以期达到最大的功能能力。