Real-time assistance in suicide prevention helplines using a deep learning-based recommender system: A randomized controlled trial

IF 3.7 2区 医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS International Journal of Medical Informatics Pub Date : 2024-12-17 DOI:10.1016/j.ijmedinf.2024.105760
Salim Salmi , Saskia Mérelle , Nikki van Eijk , Renske Gilissen , Rob van der Mei , Sandjai Bhulai
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Abstract

Objective

To evaluate the effectiveness and usability of an AI-assisted tool in providing real-time assistance to counselors during suicide prevention helpline conversations.

Methods

In this RCT, the intervention group used an AI-assisted tool, which generated suggestions based on sentence embeddings (i.e. BERT) from previous successful counseling sessions. Cosine similarity was used to present the top 5 chat situation to the counsellors. The control group did not have access to the tool (care as usual). Both groups completed a questionnaire assessing their self-efficacy at the end of each shift. Counselors' usage of the tool was evaluated by measuring frequency, duration and content of interactions.

Results

In total, 48 counselors participated in the experiment: 27 counselors in the experimental condition and 21 counselors in the control condition. Together they rated 188 shifts. No significant difference in self-efficacy was observed between the two groups (p=0.36). However, counselors that used the AI-assisted tool had marginally lower response time and used the tool more often during conversations that had a longer duration. A deeper analysis of usage showed that the tool was frequently used in inappropriate situations, e.g. after the counselor had already provided a response to the help-seeker, defeating the purpose of the information. When the tool was employed appropriately (64 conversations), it provided usable information in 53 conversations (83%). However, counselors used the tool less frequently at optimal moments, indicating their potential lack of proficiency with using AI-assisted tools during helpline conversations or initial trust issues with the system.

Conclusion

The study demonstrates benefits and pitfalls of integrating AI-assisted tools in suicide prevention for improving counselor support. Despite the lack of significant impact on self-efficacy, the support tool provided usable suggestions and the frequent use during long conversations suggests counsellors may wish to use the tool in complex or challenging interactions.
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基于深度学习的推荐系统在自杀预防热线中的实时帮助:一项随机对照试验。
目的:评估人工智能辅助工具在自杀预防热线对话期间为咨询师提供实时帮助的有效性和可用性。方法:在本随机对照试验中,干预组使用人工智能辅助工具,该工具根据之前成功的咨询会话中的句子嵌入(即BERT)生成建议。余弦相似度用于向辅导员呈现前5名的聊天情况。对照组没有使用工具(照例小心)。在每班结束时,两组人都完成了一份评估自我效能的问卷。通过测量互动的频率、持续时间和内容来评估辅导员对该工具的使用情况。结果:共有48名辅导员参与实验,其中实验组27名,对照组21名。他们总共评估了188个班次。两组患者自我效能感差异无统计学意义(p=0.36)。然而,使用人工智能辅助工具的咨询师的响应时间略短,并且在持续时间较长的对话中更频繁地使用该工具。对使用情况的深入分析表明,该工具经常在不适当的情况下使用,例如,在咨询师已经向寻求帮助的人提供了回应之后,违背了信息的目的。当适当地使用该工具(64次对话)时,它在53次对话(83%)中提供了可用的信息。然而,咨询师在最佳时刻使用该工具的频率较低,这表明他们在帮助热线对话或与系统的初始信任问题中可能缺乏使用人工智能辅助工具的熟练程度。结论:该研究展示了将人工智能辅助工具整合到自杀预防中以改善咨询师支持的好处和缺陷。尽管对自我效能没有显著的影响,但支持工具提供了有用的建议,并且在长时间的对话中频繁使用表明咨询师可能希望在复杂或具有挑战性的互动中使用该工具。
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来源期刊
International Journal of Medical Informatics
International Journal of Medical Informatics 医学-计算机:信息系统
CiteScore
8.90
自引率
4.10%
发文量
217
审稿时长
42 days
期刊介绍: International Journal of Medical Informatics provides an international medium for dissemination of original results and interpretative reviews concerning the field of medical informatics. The Journal emphasizes the evaluation of systems in healthcare settings. The scope of journal covers: Information systems, including national or international registration systems, hospital information systems, departmental and/or physician''s office systems, document handling systems, electronic medical record systems, standardization, systems integration etc.; Computer-aided medical decision support systems using heuristic, algorithmic and/or statistical methods as exemplified in decision theory, protocol development, artificial intelligence, etc. Educational computer based programs pertaining to medical informatics or medicine in general; Organizational, economic, social, clinical impact, ethical and cost-benefit aspects of IT applications in health care.
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