Department of Family Medicine and Public Health, University of California, San Diego, San Diego, CA, United States.
Department of Physical Medicine and Rehabilitation, University of Florida, Gainesville, FL, United States.
Front Public Health. 2021 Nov 11;9:738253. doi: 10.3389/fpubh.2021.738253. eCollection 2021.
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.
物理医学是一门医学专业，专注于改善各种影响大脑、脊髓、周围神经、肌肉、骨骼、关节、韧带和肌腱的医学状况的患者的功能结果。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起着关键作用。大数据和精准医学已在其他领域得到应用，并在一定程度上应用于物理医学，以预测患者的结果，但仍存在许多挑战。SDH 与物理医学结果之间的相互作用因护理的不同阶段而高度变化，急性护理中更有利的患者特征在门诊环境中可能不太有利。此外，SDH 影响患者可获得的治疗或介入程序，从而决定结果。本观点文章描述了现有数据集的实用性，结合新型数据，如运动、步态模式和患者感知结果，可以使用人工智能方法进行分析，以确定针对个体患者的最佳治疗计划，从而实现最大功能能力。
