Department of Cardiology, CHU Rennes and Inserm, LTSI, University of Rennes, Centre Cardio-Pneumologique, CHU Pontchaillou, Service de Cardiologie et Maladies Vasculaires, 2 Rue Henri le Guilloux, 35000, Rennes, France.
Department of Internal Medicine, Pfizer, Berlin, Germany.
Europace. 2022 Oct 13;24(9):1372-1383. doi: 10.1093/europace/euac052.
Digital technology is now an integral part of medicine. Tools for detecting, screening, diagnosis, and monitoring health-related parameters have improved patient care and enabled individuals to identify issues leading to better management of their own health. Wearable technologies have integrated sensors and can measure physical activity, heart rate and rhythm, and glucose and electrolytes. For individuals at risk, wearables or other devices may be useful for early detection of atrial fibrillation or sub-clinical states of cardiovascular disease, disease management of cardiovascular diseases such as hypertension and heart failure, and lifestyle modification. Health data are available from a multitude of sources, namely clinical, laboratory and imaging data, genetic profiles, wearables, implantable devices, patient-generated measurements, and social and environmental data. Artificial intelligence is needed to efficiently extract value from this constantly increasing volume and variety of data and to help in its interpretation. Indeed, it is not the acquisition of digital information, but rather the smart handling and analysis that is challenging. There are multiple stakeholder groups involved in the development and effective implementation of digital tools. While the needs of these groups may vary, they also have many commonalities, including the following: a desire for data privacy and security; the need for understandable, trustworthy, and transparent systems; standardized processes for regulatory and reimbursement assessments; and better ways of rapidly assessing value.
数字技术现在已经成为医学的一个组成部分。用于检测、筛查、诊断和监测与健康相关参数的工具提高了患者的护理水平，并使个人能够识别导致更好地管理自身健康的问题。可穿戴技术集成了传感器，可以测量身体活动、心率和节律以及血糖和电解质。对于有风险的个体，可穿戴设备或其他设备可能有助于早期发现心房颤动或心血管疾病的亚临床状态，管理高血压和心力衰竭等心血管疾病，以及改变生活方式。健康数据可来自多种来源，即临床、实验室和影像学数据、遗传谱、可穿戴设备、植入式设备、患者生成的测量值以及社会和环境数据。需要人工智能来有效地从不断增加的大量和多样化的数据中提取价值，并帮助解释这些数据。事实上，具有挑战性的不是数字信息的获取，而是智能处理和分析。有多个利益相关者群体参与数字工具的开发和有效实施。虽然这些群体的需求可能有所不同，但它们也有许多共同点，包括以下几点：对数据隐私和安全的需求；对可理解、值得信赖和透明系统的需求；监管和报销评估的标准化流程；以及快速评估价值的更好方法。
