开发数字心理健康干预安全评估流程并获得监管部门批准:案例研究与学术指南

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Rayan Taher, Charlotte L. Hall, Aislinn D Gomez Bergin, Neha Gupta, Clare Heaysman, Pamela Jacobsen, Thomas Kabir, Nayan Kalnad, Jeroen Keppens, Che-Wei Hsu, Philip McGuire, Emmanuelle Peters, Sukhi Shergill, Daniel Stahl, Ben Wensley Stock, Jenny Yiend
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引用次数: 0

摘要

近年来,数字心理健康领域呈指数级增长。虽然证据基础已大幅增加,但其在医疗和护理服务中的应用却因一些挑战而放缓,其中包括研究人员对如何通过强制性监管审批途径缺乏了解。本文详细介绍了一个团队在使用新型数字心理健康干预方法开展研究时获得必要批准所必须采取的步骤。我们以一项名为 STOP(妄想症的成功治疗)的数字心理健康干预随机对照试验为例。方法部分解释了获得监管部门批准(MHRA 无异议通知)所需的两个主要目标,以及 STOP 试验中每个目标所涉及的详细步骤。首先,必须证明数字心理健康干预的现有安全性。这可以参考文献综述、任何可行性/试点安全性数据,并要求制定风险管理计划。其次,需要制定详细计划,进一步评估数字心理健康干预措施的安全性。为此,我们介绍了 STOP 研究开发的不良事件分类框架,以及基于该框架的不良事件数据收集工具。我们介绍了文献综述结果、与安全相关的可行性研究结果以及 STOP 的完整风险管理计划,其中涉及 26 种可能的危害,并包括为量化精神病患者在没有治疗师直接支持的情况下接受数字干预时所涉及的典型风险的概率和严重程度而开发的 6 点量表。我们还介绍了数字治疗设备的不良事件分类框架和不良事件检查表(可评估 15 种不同类别的不良事件),该框架和检查表是在此基础上构建的,并在 STOP 试验中使用。本文分享的示例可为从事数字心理健康领域工作的学者和专业人士提供指导。当提出对数字心理健康干预进行临床调查时,它为监管机构的安全评估要求提供了启示。本文还介绍了一些方法、量表和工具,这些方法、量表和工具可以很容易地应用于其他类似的研究,希望这些方法、量表和工具能够帮助该领域的其他研究人员为数字心理健康产品寻求监管批准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Developing a process for assessing the safety of a digital mental health intervention and gaining regulatory approval: a case study and academic’s guide
The field of digital mental health has followed an exponential growth trajectory in recent years. While the evidence base has increased significantly, its adoption within health and care services has been slowed by several challenges, including a lack of knowledge from researchers regarding how to navigate the pathway for mandatory regulatory approval. This paper details the steps that a team must take to achieve the required approvals to carry out a research study using a novel digital mental health intervention. We used a randomised controlled trial of a digital mental health intervention called STOP (Successful Treatment of Paranoia) as a worked example. The methods section explains the two main objectives that are required to achieve regulatory approval (MHRA Notification of No Objection) and the detailed steps involved within each, as carried out for the STOP trial. First, the existing safety of digital mental health interventions must be demonstrated. This can refer to literature reviews, any feasibility/pilot safety data, and requires a risk management plan. Second, a detailed plan to further evaluate the safety of the digital mental health intervention is needed. As part of this we describe the STOP study’s development of a framework for categorising adverse events and based on this framework, a tool to collect adverse event data. We present literature review results, safety-related feasibility study findings and the full risk management plan for STOP, which addressed 26 possible hazards, and included the 6-point scales developed to quantify the probability and severity of typical risks involved when a psychiatric population receives a digital intervention without the direct support of a therapist. We also present an Adverse Event Category Framework for Digital Therapeutic Devices and the Adverse Events Checklist—which assesses 15 different categories of adverse events—that was constructed from this and used in the STOP trial. The example shared in this paper serves as a guide for academics and professionals working in the field of digital mental health. It provides insights into the safety assessment requirements of regulatory bodies when a clinical investigation of a digital mental health intervention is proposed. Methods, scales and tools that could easily be adapted for use in other similar research are presented, with the expectation that these will assist other researchers in the field seeking regulatory approval for digital mental health products.
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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