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首页> 外文期刊>BMC Medical Informatics and Decision Making >Considering patient safety in autonomous e-mental health systems – detecting risk situations and referring patients back to human care
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Considering patient safety in autonomous e-mental health systems – detecting risk situations and referring patients back to human care

机译：在自主电子精神卫生系统中考虑患者安全–检测风险情况并将患者转回人类护理

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Digital health interventions can fill gaps in mental healthcare provision. However, autonomous e-mental health (AEMH) systems also present challenges for effective risk management. To balance autonomy and safety, AEMH systems need to detect risk situations and act on these appropriately. One option is sending automatic alerts to carers, but such ‘auto-referral’ could lead to missed cases or false alerts. Requiring users to actively self-refer offers an alternative, but this can also be risky as it relies on their motivation to do so. This study set out with two objectives. Firstly, to develop guidelines for risk detection and auto-referral systems. Secondly, to understand how persuasive techniques, mediated by a virtual agent, can facilitate self-referral. In a formative phase, interviews with experts, alongside a literature review, were used to develop a risk detection protocol. Two referral protocols were developed – one involving auto-referral, the other motivating users to self-refer. This latter was tested via crowd-sourcing (n?=?160). Participants were asked to imagine they had sleeping problems with differing severity and user stance on seeking help. They then chatted with a virtual agent, who either directly facilitated referral, tried to persuade the user, or accepted that they did not want help. After the conversation, participants rated their intention to self-refer, to chat with the agent again, and their feeling of being heard by the agent. Whether the virtual agent facilitated, persuaded or accepted, influenced all of these measures. Users who were initially negative or doubtful about self-referral could be persuaded. For users who were initially positive about seeking human care, this persuasion did not affect their intentions, indicating that a simply facilitating referral without persuasion was sufficient. This paper presents a protocol that elucidates the steps and decisions involved in risk detection, something that is relevant for all types of AEMH systems. In the case of self-referral, our study shows that a virtual agent can increase users’ intention to self-refer. Moreover, the strategy of the agent influenced the intentions of the user afterwards. This highlights the importance of a personalised approach to promote the user’s access to appropriate care.
机译：数字健康干预措施可以填补精神保健服务方面的空白。但是，自主电子心理健康（AEMH）系统也对有效的风险管理提出了挑战。为了平衡自主性和安全性，AEMH系统需要检测风险情况并采取适当措施。一种选择是向护理人员发送自动警报，但是这种“自动转诊”可能会导致漏诊或误报。要求用户主动进行自我推荐是一种替代方法，但这也可能会有风险，因为它依赖于他们这样做的动机。这项研究提出了两个目标。首先，制定风险检测和自动转介系统的准则。其次，要了解由虚拟代理人介导的说服技术如何促进自我推荐。在形成阶段，与专家的访谈以及文献综述被用于制定风险检测方案。开发了两种引荐协议–一种涉及自动引荐，另一种则鼓励用户进行自我引荐。后者通过众包测试（n？=？160）。要求参与者想象他们在睡觉时出现了严重程度和用户寻求帮助的立场不同的睡眠问题。然后，他们与虚拟代理聊天，后者直接促进了推荐，试图说服用户，或者接受他们不需要帮助。交谈后，参与者对自己的自我推荐，与座席再次聊天以及被座席听到的感觉进行了评分。虚拟代理是促进，说服还是接受，都会影响所有这些措施。可以说服最初对自我推荐持否定态度或怀疑态度的用户。对于最初对寻求人文关怀表示肯定的用户，这种说服并没有影响他们的意图，这表明在没有说服力的情况下简单地促进转介就足够了。本文提出了一种协议，该协议阐明了风险检测中涉及的步骤和决策，这与所有类型的AEMH系统都相关。在自我推荐的情况下，我们的研究表明，虚拟代理可以提高用户自我推荐的意愿。而且，代理的策略随后影响了用户的意图。这突出了个性化方法对促进用户获得适当护理的重要性。

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