Stephenson Callum, Eadie Jazmin, Holmes Christina, Asadpour Kimia, Gutierrez Gilmar, Kumar Anchan, Jagayat Jasleen, Patel Charmy, Sajid Saad, Knyahnytskyi Oleksandr, Yang Megan, Reshetukha Taras, Moi Christina, Barrett Tricia, Shirazi Amirhossein, Verter Vedat, Soares Claudio N, Omrani Mohsen, Alavi Nazanin
Department of Psychiatry, Queen's University, Kingston, ON, Canada.
OPTT Inc., Toronto, ON, Canada.
Can J Psychiatry. 2025 Aug 1:7067437251355641. doi: 10.1177/07067437251355641.
ObjectivesThis study aimed to implement an artificial intelligence-assisted psychiatric triage program, assessing its impact on efficiency and resource optimization.MethodsThis project recruited patients on the waitlist for psychiatric evaluation at an outpatient hospital. Participants ( = 101) completed a digital triage module that used natural language processing and machine learning to recommend a care intensity level and a disorder-specific digital psychotherapy program. A psychiatrist also assessed the same information, and the decisions for care intensity and psychotherapy programs were compared with the artificial intelligence recommendations.ResultsThe overall wait time to receive care decreased by 71.43% due to this initiative. Additionally, participants received psychological care within three weeks after completing the triage module. In 71.29% of the cases, the artificial intelligence-assisted triage program and the psychiatrist suggested the same treatment intensity and psychotherapy program. Additionally, 63.29% of participants allocated to lower-intensity treatment plans by the AI-assisted triage program did not require psychiatric consultation later.ConclusionsUsing artificial intelligence to expedite psychiatric triaging is a promising solution to address long wait times for mental health care. With future accuracy refinements, this could be a valuable tool to implement in hospital settings to assist care teams and improve mental health care. This could result in increased care capacity and improved workflow and decision-making.
目标
本研究旨在实施一项人工智能辅助的精神科分诊计划,评估其对效率和资源优化的影响。
方法
该项目招募了在门诊医院等待精神科评估的患者。参与者(n = 101)完成了一个数字分诊模块,该模块使用自然语言处理和机器学习来推荐护理强度级别和特定疾病的数字心理治疗方案。一名精神科医生也评估了相同的信息,并将护理强度和心理治疗方案的决策与人工智能的建议进行了比较。
结果
由于这一举措,接受护理的总体等待时间减少了71.43%。此外,参与者在完成分诊模块后的三周内接受了心理护理。在71.29%的病例中,人工智能辅助分诊计划和精神科医生建议了相同的治疗强度和心理治疗方案。此外,由人工智能辅助分诊计划分配到低强度治疗计划的参与者中,63.29%的人后来不需要精神科会诊。
结论
使用人工智能加快精神科分诊是解决心理健康护理长时间等待问题的一个有前景的解决方案。随着未来准确性的提高,这可能成为医院环境中实施的一个有价值的工具,以协助护理团队并改善心理健康护理。这可能会提高护理能力,改善工作流程和决策。