Table_2_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.

物理治疗学是一门专注于改善因大脑、脊髓、周围神经、肌肉、骨骼、关节、韧带和肌腱等不同医学状况影响的患者功能恢复的医学专科。健康的社会决定因素（SDH）在确定治疗过程和患者功能恢复结果中发挥着关键作用。大数据和精准医疗已被应用于其他领域，并在一定程度上应用于物理治疗学以预测患者结果，然而仍存在许多挑战。健康的社会决定因素与物理治疗学结果之间的相互作用因护理的不同阶段而高度可变，且在急性护理阶段更为有利的患者特征在门诊环境中可能不那么有利。此外，健康的社会决定因素影响着患者可获取的治疗或干预程序，从而决定了结果。本篇观点文章描述了利用现有数据集结合新型数据，如运动、步态模式和患者感知结果，通过人工智能方法进行分析的效用，以确定针对个别患者的最佳治疗方案，以期达到最大化的功能能力。