Introduction: Given the recent impressive digital transformation worldwide, the importance of data has reached a new dimension. It is, therefore, provocative to ask whether data can save healthcare systems from bankruptcy.Areas covered: We reviewed published examples in the search for the evidence on how the growing amount of data could change the way we used to assess the value of healthcare technologies, ensuring a more holistic approach in the decision-making process while reducing the waste in the healthcare.Expert opinion: The growing amount of data will continue to provide a multitude of valuable insights that can save healthcare systems from bankruptcy. Electronic medical records, IoT, wearables, and mobile applications generate constant data streams that can be utilized endlessly thanks to methodological advancements such as SNA, unsupervised and supervised machine learning, and natural language programming. However, interoperability across these multiple data sources still pose a challenge for the future development of data-driven healthcare. Already today however, decision makers can utilize Big Data to develop conditional coverage schemes for very expensive and complicated health technologies suitable for personalized healthcare. More advanced payers may utilize even data analytics even further and develop AI-based pricing schemes.

中文翻译：

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“没有数据，您就是另一个有意见的人”。

简介：鉴于最近全球范围内令人印象深刻的数字化转型，数据的重要性已达到一个新的高度。因此，质疑数据是否可以使医疗保健系统免于破产是极具挑战性的。涵盖的领域：我们回顾了已发表的示例，以寻找证据表明数据量的增长如何改变我们用来评估医疗保健技术价值的方式，确保在决策过程中采用更全面的方法，同时减少医疗保健方面的浪费。专家意见：不断增长的数据量将继续提供大量有价值的见解，可以使医疗保健系统免于破产。电子病历，物联网，可穿戴设备和移动应用程序会产生持续不断的数据流，这得益于SNA，无监督和有监督的机器学习以及自然语言编程。但是，跨多个数据源的互操作性仍然对数据驱动的医疗保健的未来发展构成挑战。但是，如今决策者已经可以利用大数据为适用于个性化医疗的非常昂贵和复杂的医疗技术开发有条件的覆盖计划。更高级的付款人甚至可以进一步利用数据分析，并开发基于AI的定价方案。决策者可以利用大数据为适用于个性化医疗的非常昂贵和复杂的医疗技术开发有条件的覆盖计划。更高级的付款人甚至可以进一步利用数据分析，并开发基于AI的定价方案。决策者可以利用大数据为适用于个性化医疗的非常昂贵和复杂的医疗技术开发有条件的覆盖计划。更高级的付款人甚至可以进一步利用数据分析，并开发基于AI的定价方案。