PDF(Pubmed)
Abstract：
BACKGROUND: Most mental health care providers face the challenge of increased demand for psychotherapy in the absence of increased funding or staffing. To overcome this supply-demand imbalance, care providers must increase the efficiency of service delivery.
OBJECTIVE: In this study, we examined whether artificial intelligence (AI)-enabled digital solutions can help mental health care practitioners to use their time more efficiently, and thus reduce strain on services and improve patient outcomes.
METHODS: In this study, we focused on the use of an AI solution (Limbic Access) to support initial patient referral and clinical assessment within the UK\'s National Health Service. Data were collected from 9 Talking Therapies services across England, comprising 64,862 patients.
RESULTS: We showed that the use of this AI solution improves clinical efficiency by reducing the time clinicians spend on mental health assessments. Furthermore, we found improved outcomes for patients using the AI solution in several key metrics, such as reduced wait times, reduced dropout rates, improved allocation to appropriate treatment pathways, and, most importantly, improved recovery rates. When investigating the mechanism by which the AI solution achieved these improvements, we found that the provision of clinically relevant information ahead of clinical assessment was critical for these observed effects.
CONCLUSIONS: Our results emphasize the utility of using AI solutions to support the mental health workforce, further highlighting the potential of AI solutions to increase the efficiency of care delivery and improve clinical outcomes for patients.
OBJECTIVE: In this study, we examined whether artificial intelligence (AI)-enabled digital solutions can help mental health care practitioners to use their time more efficiently, and thus reduce strain on services and improve patient outcomes.
METHODS: In this study, we focused on the use of an AI solution (Limbic Access) to support initial patient referral and clinical assessment within the UK\'s National Health Service. Data were collected from 9 Talking Therapies services across England, comprising 64,862 patients.
RESULTS: We showed that the use of this AI solution improves clinical efficiency by reducing the time clinicians spend on mental health assessments. Furthermore, we found improved outcomes for patients using the AI solution in several key metrics, such as reduced wait times, reduced dropout rates, improved allocation to appropriate treatment pathways, and, most importantly, improved recovery rates. When investigating the mechanism by which the AI solution achieved these improvements, we found that the provision of clinically relevant information ahead of clinical assessment was critical for these observed effects.
CONCLUSIONS: Our results emphasize the utility of using AI solutions to support the mental health workforce, further highlighting the potential of AI solutions to increase the efficiency of care delivery and improve clinical outcomes for patients.
摘要：
背景：在没有增加资金或人员配备的情况下，大多数精神卫生保健提供者面临着对心理治疗需求增加的挑战。为了克服这种供需不平衡，护理提供者必须提高服务提供的效率。
目的：在本研究中，我们研究了支持人工智能（AI）的数字解决方案是否可以帮助精神卫生保健从业者更有效地利用他们的时间，从而减轻服务压力，改善患者预后。
方法：在本研究中，我们专注于使用AI解决方案(LimbicAccess)来支持英国国家卫生服务机构的初始患者转诊和临床评估.数据来自英格兰的9家谈话疗法服务机构，包括64,862名患者。
结果：我们表明，使用此AI解决方案可以通过减少临床医生在心理健康评估上花费的时间来提高临床效率。此外，我们在几个关键指标中发现使用AI解决方案的患者的预后有所改善，例如减少等待时间，降低辍学率，改善对适当治疗途径的分配，and,最重要的是,提高回收率。在调查AI解决方案实现这些改进的机制时，我们发现,在临床评估之前提供临床相关信息对于这些观察到的效应至关重要.
结论：我们的结果强调了使用AI解决方案来支持精神卫生劳动力的效用，进一步强调AI解决方案在提高医疗服务效率和改善患者临床结局方面的潜力.
目的：在本研究中，我们研究了支持人工智能（AI）的数字解决方案是否可以帮助精神卫生保健从业者更有效地利用他们的时间，从而减轻服务压力，改善患者预后。
方法：在本研究中，我们专注于使用AI解决方案(LimbicAccess)来支持英国国家卫生服务机构的初始患者转诊和临床评估.数据来自英格兰的9家谈话疗法服务机构，包括64,862名患者。
结果：我们表明，使用此AI解决方案可以通过减少临床医生在心理健康评估上花费的时间来提高临床效率。此外，我们在几个关键指标中发现使用AI解决方案的患者的预后有所改善，例如减少等待时间，降低辍学率，改善对适当治疗途径的分配，and,最重要的是,提高回收率。在调查AI解决方案实现这些改进的机制时，我们发现,在临床评估之前提供临床相关信息对于这些观察到的效应至关重要.
结论：我们的结果强调了使用AI解决方案来支持精神卫生劳动力的效用，进一步强调AI解决方案在提高医疗服务效率和改善患者临床结局方面的潜力.
