Regulatory Science, DEEP Measures Oy, Helsinki, Finland.
Product Development Regulatory, F. Hoffmann-La Roche Ltd, Basel, Switzerland.
Expert Rev Pharmacoecon Outcomes Res. 2024 Jul;24(6):731-741. doi: 10.1080/14737167.2024.2334347. Epub 2024 May 15.
Over the last decade increasing examples indicate opportunities to measure patient functioning and its relevance for clinical and regulatory decision making via endpoints collected through digital health technologies. More recently, we have seen such measures support primary study endpoints and enable smaller trials. The field is advancing fast: validation requirements have been proposed in the literature and regulators are releasing new guidances to review these endpoints. Pharmaceutical companies are embracing collaborations to develop them and working with academia and patient organizations in their development. However, the road to validation and regulatory acceptance is lengthy. The full value of digital endpoints cannot be unlocked until better collaboration and modular evidence frameworks are developed enabling re-use of evidence and repurposing of digital endpoints.
This paper proposes a solution by presenting a novel modular evidence framework -the Digital Evidence Ecosystem and Protocols (DEEP)- enabling repurposing of measurement solutions, re-use of evidence, application of standards and also facilitates collaboration with health technology assessment bodies.
The integration of digital endpoints in healthcare, essential for personalized and remote care, requires harmonization and transparency. The proposed novel stack model offers a modular approach, fostering collaboration and expediting the adoption in patient care.
在过去的十年中,越来越多的例子表明,可以通过数字健康技术收集的终点来衡量患者的功能及其对临床和监管决策的相关性。最近,我们已经看到这些措施支持主要研究终点,并使试验规模更小。该领域发展迅速:文献中提出了验证要求,监管机构发布了新的指南来审查这些终点。制药公司正在通过合作来开发这些终点,并在其开发过程中与学术界和患者组织合作。然而,验证和监管部门的认可之路还很漫长。只有建立更好的协作和模块化证据框架,实现证据的重复使用和数字终点的重新应用,才能充分发挥数字终点的价值。
本文通过提出一个新的模块化证据框架——数字证据生态系统和协议(DEEP),提出了一种解决方案,该框架能够重新应用测量解决方案、重复使用证据、应用标准,并促进与卫生技术评估机构的合作。
数字端点在医疗保健中的整合对于个性化和远程护理至关重要,需要实现协调和透明。所提出的新型堆叠模型提供了一种模块化方法,促进了协作,并加速了在患者护理中的采用。