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DIY 数字医疗中心。

The DIY Digital Medical Centre.

机构信息

Student MSc Health Policy, Department of Surgery and Cancer, Imperial College London, London, UK.

Department of Microbiology, Technical University of Braunschweig, Braunschweig, Germany.

出版信息

Microb Biotechnol. 2017 Sep;10(5):1084-1093. doi: 10.1111/1751-7915.12817. Epub 2017 Aug 25.

DOI:10.1111/1751-7915.12817
PMID:28840966
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5609225/
Abstract

Healthcare systems worldwide are confronted with major economic, organizational and logistical challenges. Historic evolution of health care has led to significant healthcare sector fragmentation, resulting in systemic inefficiencies and suboptimal resource exploitation. To attain a sustainable healthcare model, fundamental, system-wide improvements that effectively network, and ensure fulfilment of potential synergies between sectors, and include and facilitate coherent strategic planning and organisation of healthcare infrastructure are needed. Critically, they must be specifically designed to sustainably achieve peak performance within the current policy environment for cost-control, and efficiency and quality improvement for service delivery. We propose creation of a new healthcare cluster, to be embedded in existing healthcare systems. It consists of (i) local 24/7 walk-in virtually autonomous do-it-yourself Digital Medical Centres performing routine diagnosis, monitoring, prevention, treatment and standardized documentation and health outcome assessment/reporting, which are online interfaced with (ii) regional 24/7 eClinician Centres providing on-demand clinical supervision/assistance to Digital Medical Centre patients. Both of these are, in turn, online interfaced with (iii) the National Clinical Informatics Centre, which houses the national patient data centre (cloud) and data analysis units that conduct patient- and population-level, personalized and predictive(-medicine) intervention optimization analyses. The National Clinical Informatics Centre also interfaces with biomedical research and prioritizes and accelerates the translation of new discoveries into clinical practice. The associated Health Policy Innovation and Evaluation Centre rapidly integrates new findings with health policy/regulatory discussions. This new cluster would synergistically link all health system components in a circular format, enable not only access by all arms of the health service to latest patient data, but also automatic algorithm analysis and prediction of clinical development of individual patients, reduce bureaucratic burden on medical professionals by enabling a greater level of focus of their expertise on non-routine medical tasks, lead to automatic translation of aggregate patient data/new knowledge into medical practice, and orient future evolution of health systems towards greater cohesion/integration and hence efficiency. A central plank of the proposed concept is increased emphasis on reduction of disease incidence and severity, to diminish both patient suffering and treatment costs. This will be achieved at the individual and population levels, through (i) significantly improved access to medical services, (ii) stronger focus on primary and secondary prevention and early treatment measures, and disease susceptibility prediction via personalized medicine, involving inter alia genome analysis at birth and periodic analysis of microbiomes and biomarkers, and integration with other patient health and epidemiology parameters, (iii) improved surveillance and (iv) intervention outcome benchmarking. The dMCs will become drivers of innovation and integrative evolution in health systems, of disease reduction and efficiency gains, and thus major contributors to development of sustainability of health care.

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f971/5609225/072304e8fea7/MBT2-10-1084-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f971/5609225/283292ffac1d/MBT2-10-1084-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f971/5609225/072304e8fea7/MBT2-10-1084-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f971/5609225/283292ffac1d/MBT2-10-1084-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f971/5609225/072304e8fea7/MBT2-10-1084-g002.jpg
摘要

全球的医疗体系都面临着重大的经济、组织和后勤挑战。医疗保健的历史演变导致了医疗部门的严重碎片化,从而导致了系统效率低下和资源利用不佳。为了实现可持续的医疗模式,需要进行根本性的、全系统的改进,有效地联网,并确保各部门之间实现潜在的协同作用,包括并促进医疗基础设施的连贯战略规划和组织。至关重要的是,它们必须专门设计,以在当前的成本控制政策环境中实现可持续的最佳绩效,并提高服务提供的效率和质量。我们建议创建一个新的医疗保健集群,嵌入现有的医疗保健系统中。它由以下三个部分组成:(i) 当地的 24/7 步行式、几乎自主的自助式数字医疗中心,进行常规诊断、监测、预防、治疗和标准化的文档记录以及健康结果评估/报告,这些中心与(ii) 24/7 的区域电子临床医生中心在线连接,为数字医疗中心的患者提供按需临床监督/协助。这两者反过来又与(iii) 国家临床信息中心在线连接,该中心设有国家患者数据中心(云)和数据分析单元,用于进行患者和人群层面的个性化和预测(医学)干预优化分析。国家临床信息中心还与生物医学研究接口,并优先考虑和加速将新发现转化为临床实践。相关的卫生政策创新和评估中心将迅速将新发现纳入卫生政策/监管讨论。这个新集群将以循环格式协同连接所有医疗系统组件,不仅使卫生服务的所有部门都能获得最新的患者数据,还能自动进行算法分析和预测个体患者的临床发展,通过使医疗专业人员更专注于非常规医疗任务来减轻他们的官僚负担,将汇总的患者数据/新知识自动转化为医疗实践,并使卫生系统的未来发展朝着更大的凝聚力/整合方向发展,从而提高效率。拟议概念的一个核心要点是更加重视减少疾病的发病率和严重程度,以减轻患者的痛苦和治疗成本。这将通过以下方式在个人和人群层面上实现:(i) 显著改善医疗服务的获取途径,(ii) 更加注重初级和二级预防以及早期治疗措施,以及通过个性化医学进行疾病易感性预测,包括出生时的基因组分析以及定期分析微生物组和生物标志物,并与其他患者健康和流行病学参数集成,(iii) 加强监测,(iv) 干预结果基准测试。dMC 将成为医疗系统创新和综合发展的驱动因素,也是减少疾病和提高效率的驱动因素,因此也是医疗保健可持续性发展的主要贡献者。

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