Kyriazis Dimosthenis, Autexier Serge, Brondino Iván, Boniface Michael, Donat Lucas, Engen Vegard, Fernandez Rafael, Jimenez-Peris Ricardo, Jordan Blanca, Jurak Gregor, Kiourtis Athanasios, Kosmidis Thanos, Lustrek Mitja, Maglogiannis Ilias, Mantas John, Martinez Antonio, Mavrogiorgou Argyro, Menychtas Andreas, Montandon Lydia, Nechifor Cosmin-Septimiu, Nifakos Sokratis, Papageorgiou Alexandra, Patino-Martinez Marta, Perez Manuel, Plagianakos Vassilis, Stanimirovic Dalibor, Starc Gregor, Tomson Tanja, Torelli Francesco, Traver-Salcedo Vicente, Vassilacopoulos George, Wajid Usman
University of Piraeus, Piraeus, Greece.
Deutsches Forschungszentrum für Künstliche Intelligenz, Bremen, Germany.
Stud Health Technol Inform. 2017;238:19-23.
Today's rich digital information environment is characterized by the multitude of data sources providing information that has not yet reached its full potential in eHealth. The aim of the presented approach, namely CrowdHEALTH, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants. HHRs are transformed into HHRs clusters capturing the clinical, social and human context of population segments and as a result collective knowledge for different factors. The proposed approach also seamlessly integrates big data technologies across the complete data path, providing of Data as a Service (DaaS) to the health ecosystem stakeholders, as well as to policy makers towards a "health in all policies" approach. Cross-domain co-creation of policies is feasible through a rich toolkit, being provided on top of the DaaS, incorporating mechanisms for causal and risk analysis, and for the compilation of predictions.
当今丰富的数字信息环境的特点是有众多数据源提供信息,这些信息在电子健康领域尚未充分发挥其潜力。所提出的方法,即“众包健康”(CrowdHEALTH)的目标是引入一种全新的整体健康记录(HHR)范式,其中包含所有健康决定因素。HHR被转化为HHR集群,以捕捉人群细分的临床、社会和人文背景,并由此形成针对不同因素的集体知识。所提出的方法还在整个数据路径中无缝集成大数据技术,向健康生态系统利益相关者以及政策制定者提供数据即服务(DaaS),以实现“所有政策中的健康”方针。通过在DaaS之上提供的丰富工具包,跨领域的政策共同创建是可行的,该工具包纳入了因果和风险分析机制以及预测编制机制。