Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.
Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada; Schulich Heart Program, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada.
Can J Cardiol. 2022 Feb;38(2):259-266. doi: 10.1016/j.cjca.2021.08.015. Epub 2021 Aug 28.
Innovations in health care are growing exponentially, resulting in improved quality of and access to care, as well as rising societal costs of care and variable reimbursement. In recent years, digital health technologies and artificial intelligence have become of increasing interest in cardiovascular medicine owing to their unique ability to empower patients and to use increasing quantities of data for moving toward personalised and precision medicine. Health technology assessment agencies evaluate the money spent on a health care intervention or technology to attain a given clinical impact and make recommendations for reimbursement considerations. However, there is a scarcity of economic evaluations of cardiovascular digital health technologies and artificial intelligence. The current health technology assessment framework is not equipped to address the unique, dynamic, and unpredictable value considerations of these technologies and highlight the need to better approach the digital health technologies and artificial intelligence health technology assessment process. In this review, we compare digital health technologies and artificial intelligence with traditional health care technologies, review existing health technology assessment frameworks, and discuss challenges and opportunities related to cardiovascular digital health technologies and artificial intelligence health technology assessment. Specifically, we argue that health technology assessments for digital health technologies and artificial intelligence applications must allow for a much shorter device life cycle, given the rapid and even potentially continuously iterative nature of this technology, and thus an evidence base that maybe less mature, compared with traditional health technologies and interventions.
医疗保健领域的创新正在呈指数级增长，这导致医疗质量和可及性得到改善，同时也导致医疗成本和可变报销额上升。近年来，由于数字健康技术和人工智能具有赋予患者权力的独特能力，并且能够利用越来越多的数据来实现个性化和精准医疗，因此它们在心血管医学领域越来越受到关注。医疗技术评估机构评估用于医疗保健干预或技术的支出，以实现特定的临床效果，并就报销考虑因素提出建议。然而，对心血管数字健康技术和人工智能的经济评估却很少。当前的医疗技术评估框架无法解决这些技术独特、动态和不可预测的价值问题，这突显了需要更好地处理数字健康技术和人工智能的医疗技术评估流程。在这篇综述中，我们将数字健康技术和人工智能与传统医疗技术进行了比较，回顾了现有的医疗技术评估框架，并讨论了与心血管数字健康技术和人工智能医疗技术评估相关的挑战和机遇。具体而言，我们认为，鉴于数字健康技术和人工智能应用的快速发展，甚至可能是连续迭代的性质，因此与传统医疗技术和干预措施相比，其证据基础可能不太成熟，因此针对数字健康技术和人工智能应用的医疗技术评估必须允许设备的生命周期更短。
