Hemkens Lars G
Basel Institute for Clinical Epidemiology and Biostatistics (ceb), Department of Clinical Research, University Hospital Basel, Spitalstrasse 12, 4031, Basel, Schweiz.
Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, USA.
Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2021 Oct;64(10):1269-1277. doi: 10.1007/s00103-021-03413-x. Epub 2021 Sep 15.
Digital health applications promise to improve patient health and medical care. This analysis provides a brief overview of evidence-based benefit assessment and the challenges to the underlying evidence as prerequisites for optimal patient-oriented decision making. Classical concepts in study design, recent developments, and innovative approaches are described with the aim of highlighting future areas of development in innovative study designs and strategic evaluation concepts for digital health applications. A special focus is on pragmatic study designs.Evidence-based benefit assessment has fundamental requirements and criteria regardless of the type of treatments evaluated. Reliable evidence is essential. Fast, efficient, reliable, and practice-relevant evaluation of digital health applications is not achieved by turning to nonrandomized trials, but rather by better pragmatic randomized trials. They are feasible and combine the characteristics of digital health applications, classical methodological concepts, and new approaches to study conduct. Routinely collected data, low-contact study conduct (remote trials, virtual trials), and digital biomarkers promote useful randomized real-world evidence as solid evidence base for digital health applications. Continuous learning evaluation with randomized designs embedded in routine care is key to sustainable and efficient benefit assessment of digital health applications and may be crucial for strategic improvement of healthcare.
数字健康应用有望改善患者健康状况和医疗服务。本分析简要概述了基于证据的效益评估以及基础证据面临的挑战，这些是实现以患者为导向的最佳决策的先决条件。描述了研究设计中的经典概念、最新进展和创新方法，旨在突出数字健康应用创新研究设计和战略评估概念的未来发展领域。特别关注实用的研究设计。无论所评估的治疗类型如何，基于证据的效益评估都有基本要求和标准。可靠的证据至关重要。对数字健康应用进行快速、高效、可靠且与实践相关的评估，不能依靠非随机试验，而应通过更好的实用随机试验来实现。它们是可行的，并且结合了数字健康应用的特点、经典的方法学概念以及新的研究实施方法。常规收集的数据、低接触式研究实施（远程试验、虚拟试验）和数字生物标志物有助于产生有用的随机真实世界证据，作为数字健康应用的坚实证据基础。将随机设计嵌入常规护理中的持续学习评估是数字健康应用可持续和高效效益评估的关键，可能对医疗保健的战略改进至关重要。
