Connected health is a multidisciplinary approach focused on health management, prioritizing pa-tient needs in the creation of tools, services, and treatments. This paradigm ensures proactive and efficient care by facilitating the timely exchange of accurate patient information among all stake-holders in the care continuum. The rise of digital technologies and process innovations promises to enhance connected health by integrating various healthcare data sources. This integration aims to personalize care, predict health outcomes, and streamline patient management, though challeng-es remain, particularly in data architecture, application interoperability, and security. Data analytics can provide critical insights for informed decision-making and health co-creation, but solutions must prioritize end-users, including patients and healthcare professionals. This perspective was explored through an agile System Development Lifecycle in an EU-funded project aimed at developing an integrated AI-generated solution for managing cancer patients undergoing immunotherapy. This paper contributes with a collaborative digital framework integrating stakeholders across the care continuum, leveraging federated big data analytics and artificial intelligence for improved decision-making while ensuring privacy. Analytical capabilities, such as treatment recommendations and adverse event predictions, were validated using real-life data, achieving 70%-90% accuracy in a pilot study with the medical partners, demonstrating the framework's effectiveness.

翻译：互联健康是一种专注于健康管理的多学科方法，在创建工具、服务和治疗方案时优先考虑患者需求。该范式通过促进护理连续体中所有利益相关者之间及时交换准确的患者信息，确保护理的主动性和高效性。数字技术和流程创新的兴起有望通过整合各种医疗数据源来增强互联健康。这种整合旨在实现个性化护理、预测健康结果并简化患者管理，尽管挑战依然存在，特别是在数据架构、应用互操作性和安全性方面。数据分析可以为知情决策和健康共同创造提供关键见解，但解决方案必须优先考虑最终用户，包括患者和医疗专业人员。这一观点通过一个欧盟资助项目中的敏捷系统开发生命周期进行了探索，该项目旨在开发一个集成的AI生成解决方案，用于管理接受免疫治疗的癌症患者。本文提出了一种协同数字框架，该框架整合了护理连续体中的利益相关者，利用联邦大数据分析和人工智能来改进决策，同时确保隐私。诸如治疗建议和不良事件预测等分析能力已使用真实数据进行了验证，在与医疗合作伙伴进行的试点研究中达到了70%-90%的准确率，证明了该框架的有效性。