Au Rhoda, Ritchie Marina, Hardy Spencer, Ang Ting Fang Alvin, Lin Honghuang
Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA.
Framingham Heart Study, National Heart, Lung, and Blood Institute, Boston, MA 01702, USA.
Adv Geriatr Med Res. 2019;1. doi: 10.20900/agmr20190003. Epub 2019 Jun 5.
Efforts to provide patients with individualized treatments have led to tremendous breakthroughs in healthcare. However, a precision medicine approach alone will not offset the rapid increase in prevalence and burden of chronic non-communicable illnesses that is continuing to pervade the world's aging population. With rapid advances in technology, it is now possible to collect digital metrics to assess, monitor and detect chronic disease indicators, much earlier in the disease course, potentially redefining what was previously considered asymptomatic to pre-symptomatic. Data science and artificial intelligence can drive the discovery of digital biomarkers before the emergence of overt clinical symptoms, thereby transforming the current healthcare approach from one centered on precision medicine to a more comprehensive focus on precision health, and by doing so enable the possibility of preventing disease altogether. Presented herein are the challenges to the current healthcare model and the proposition of first steps for reversing the prevailing intractable trend of rising healthcare costs and poorer health quality.
为患者提供个性化治疗的努力已在医疗保健领域带来了巨大突破。然而，仅靠精准医疗方法并不能抵消慢性非传染性疾病患病率和负担的快速上升，这种上升趋势仍在全球老龄化人口中蔓延。随着技术的飞速发展，现在有可能收集数字指标，以便在疾病进程的更早阶段评估、监测和检测慢性病指标，这有可能重新定义以前被认为是无症状的状态为症状前状态。数据科学和人工智能可以在明显临床症状出现之前推动数字生物标志物的发现，从而将当前以精准医疗为中心的医疗方法转变为更全面地关注精准健康，并借此实现完全预防疾病的可能性。本文介绍了当前医疗保健模式面临的挑战，以及扭转医疗成本不断上升和健康质量下降这一普遍棘手趋势的初步措施建议。
