Abstract：
BACKGROUND: This review aims to synthesise the literature on the efficacy, evolution, and challenges of implementing Clincian Decision Support Systems (CDSS) in the realm of mental health, addiction, and concurrent disorders.
METHODS: Following PRISMA guidelines, a systematic review and meta-analysis were performed. Searches conducted in databases such as MEDLINE, Embase, CINAHL, PsycINFO, and Web of Science through 25 May 2023, yielded 27,344 records. After necessary exclusions, 69 records were allocated for detailed synthesis. In the examination of patient outcomes with a focus on metrics such as therapeutic efficacy, patient satisfaction, and treatment acceptance, meta-analytic techniques were employed to synthesise data from randomised controlled trials.
RESULTS: A total of 69 studies were included, revealing a shift from knowledge-based models pre-2017 to a rise in data-driven models post-2017. The majority of models were found to be in Stage 2 or 4 of maturity. The meta-analysis showed an effect size of -0.11 for addiction-related outcomes and a stronger effect size of -0.50 for patient satisfaction and acceptance of CDSS.
CONCLUSIONS: The results indicate a shift from knowledge-based to data-driven CDSS approaches, aligned with advances in machine learning and big data. Although the immediate impact on addiction outcomes is modest, higher patient satisfaction suggests promise for wider CDSS use. Identified challenges include alert fatigue and opaque AI models.
CONCLUSIONS: CDSS shows promise in mental health and addiction treatment but requires a nuanced approach for effective and ethical implementation. The results emphasise the need for continued research to ensure optimised and equitable use in healthcare settings.
METHODS: Following PRISMA guidelines, a systematic review and meta-analysis were performed. Searches conducted in databases such as MEDLINE, Embase, CINAHL, PsycINFO, and Web of Science through 25 May 2023, yielded 27,344 records. After necessary exclusions, 69 records were allocated for detailed synthesis. In the examination of patient outcomes with a focus on metrics such as therapeutic efficacy, patient satisfaction, and treatment acceptance, meta-analytic techniques were employed to synthesise data from randomised controlled trials.
RESULTS: A total of 69 studies were included, revealing a shift from knowledge-based models pre-2017 to a rise in data-driven models post-2017. The majority of models were found to be in Stage 2 or 4 of maturity. The meta-analysis showed an effect size of -0.11 for addiction-related outcomes and a stronger effect size of -0.50 for patient satisfaction and acceptance of CDSS.
CONCLUSIONS: The results indicate a shift from knowledge-based to data-driven CDSS approaches, aligned with advances in machine learning and big data. Although the immediate impact on addiction outcomes is modest, higher patient satisfaction suggests promise for wider CDSS use. Identified challenges include alert fatigue and opaque AI models.
CONCLUSIONS: CDSS shows promise in mental health and addiction treatment but requires a nuanced approach for effective and ethical implementation. The results emphasise the need for continued research to ensure optimised and equitable use in healthcare settings.
摘要：
背景：这篇综述旨在综合有关功效的文献，进化,以及在心理健康领域实施诊所决策支持系统(CDSS)的挑战，上瘾,和并发疾病。
方法：遵循PRISMA指南，我们进行了系统评价和荟萃分析.在MEDLINE等数据库中进行的搜索，Embase,CINAHL,PsycINFO,到2023年5月25日，WebofScience产生了27344条记录。在必要的排除之后，69条记录被分配用于详细的合成。在检查患者结果时，重点关注治疗效果等指标，患者满意度,和治疗验收，采用荟萃分析技术综合来自随机对照试验的数据.
结果：共纳入69项研究，揭示了从2017年前的基于知识的模型到2017年后的数据驱动模型的兴起的转变。发现大多数模型处于成熟的第2或第4阶段。荟萃分析显示，成瘾相关结局的效应大小为-0.11，患者满意度和接受CDSS的效应大小为-0.50。
结论：结果表明，从基于知识的CDSS方法向数据驱动的CDSS方法转变，与机器学习和大数据的进步相一致。尽管对成瘾结果的直接影响不大，更高的患者满意度表明更广泛使用CDSS的前景。识别的挑战包括警报疲劳和不透明的AI模型。
结论：CDSS在心理健康和成瘾治疗方面显示出希望，但需要采取微妙的方法来有效和道德地实施。结果强调需要继续研究，以确保在医疗机构中得到优化和公平的使用。
方法：遵循PRISMA指南，我们进行了系统评价和荟萃分析.在MEDLINE等数据库中进行的搜索，Embase,CINAHL,PsycINFO,到2023年5月25日，WebofScience产生了27344条记录。在必要的排除之后，69条记录被分配用于详细的合成。在检查患者结果时，重点关注治疗效果等指标，患者满意度,和治疗验收，采用荟萃分析技术综合来自随机对照试验的数据.
结果：共纳入69项研究，揭示了从2017年前的基于知识的模型到2017年后的数据驱动模型的兴起的转变。发现大多数模型处于成熟的第2或第4阶段。荟萃分析显示，成瘾相关结局的效应大小为-0.11，患者满意度和接受CDSS的效应大小为-0.50。
结论：结果表明，从基于知识的CDSS方法向数据驱动的CDSS方法转变，与机器学习和大数据的进步相一致。尽管对成瘾结果的直接影响不大，更高的患者满意度表明更广泛使用CDSS的前景。识别的挑战包括警报疲劳和不透明的AI模型。
结论：CDSS在心理健康和成瘾治疗方面显示出希望，但需要采取微妙的方法来有效和道德地实施。结果强调需要继续研究，以确保在医疗机构中得到优化和公平的使用。
