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Medication management needs information and communications technology-based approaches, including telehealth and artificial intelligence

机译:药物管理需要信息和通信技术的方法,包括远程医疗和人工智能

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Life expectancy is rising in most parts of the world as is the prevalence of chronic diseases. Suboptimal adherence to long-term medications is still rather the norm than the exception, although it is well known that suboptimal adherence compromises the therapeutic effectiveness. Information and communications technology provides new concepts for improving adherence to medications. These so-called telehealth concepts or services help to implement closed-loop healthcare paradigms and to establish collaborative care networks involving all stakeholders relevant to optimising the overall medication therapy. Together with data from Electronic Health Records and Electronic Medical Records, these networks pave the way to data-driven decision support systems. Recent advances in machine learning, predictive analytics, and artificial intelligence allow further steps towards fully autonomous telehealth systems. This might bring advances in the future: disburden healthcare professionals from repetitive tasks, enable them to timely react to critical situations, and offer a comprehensive overview of the patients' medication status. Advanced analytics can help to assess whether patients have taken their medications as prescribed, to improve adherence via automatic reminders. Ultimately, all relevant data sources need to be collated into a basis for data-driven methods, with the goal to assist healthcare professionals in guiding patients to obtain the best possible health status, with a reasonable resource utilisation and a risk-adjusted safety and privacy approach. This paper summarises the state-of-the-art of telehealth and artificial intelligence applications in medication management. It focuses on 3 major aspects: latest technologies, current applications, and patient related issues.
机译:随着慢性病的流行,世界大部分地区的预期寿命都在增加。长期服用药物的次优依从性仍然是常态,而不是例外,尽管众所周知,次优依从性会损害治疗效果。信息和通信技术为改善药物依从性提供了新概念。这些所谓的远程医疗概念或服务有助于实施闭环医疗模式,并建立涉及所有利益相关者的合作医疗网络,以优化整体药物治疗。这些网络与来自电子健康记录和电子医疗记录的数据一起,为数据驱动的决策支持系统铺平了道路。机器学习、预测分析和人工智能方面的最新进展使完全自主的远程医疗系统得以进一步发展。这可能会在未来带来进步:解除医疗专业人员重复性任务的负担,使他们能够及时应对危急情况,并提供患者用药状态的全面概述。高级分析可以帮助评估患者是否按照规定服药,通过自动提醒提高依从性。最终,需要将所有相关数据源整理成数据驱动方法的基础,目的是帮助医疗专业人员指导患者获得最佳健康状态,合理利用资源,并采取风险调整后的安全和隐私方法。本文总结了远程医疗和人工智能在药物管理中的应用现状。它关注3个主要方面:最新技术、当前应用和与患者相关的问题。

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