Dawoodbhoy Fatema Mustansir, Delaney Jack, Cecula Paulina, Yu Jiakun, Peacock Iain, Tan Joseph, Cox Benita
Imperial College London Business School, London, UK.
Imperial College School of Medicine, South Kensington Campus, London, SW7 2BU, UK.
Heliyon. 2021 May 12;7(5):e06993. doi: 10.1016/j.heliyon.2021.e06993. eCollection 2021 May.
Growing demand for mental health services, coupled with funding and resource limitations, creates an opportunity for novel technological solutions including artificial intelligence (AI). This study aims to identify issues in patient flow on mental health units and align them with potential AI solutions, ultimately devising a model for their integration at service level.
Following a narrative literature review and pilot interview, 20 semi-structured interviews were conducted with AI and mental health experts. Thematic analysis was then used to analyse and synthesise gathered data and construct an enhanced model.
Predictive variables for length-of-stay and readmission rate are not consistent in the literature. There are, however, common themes in patient flow issues. An analysis identified several potential areas for AI-enhanced patient flow. Firstly, AI could improve patient flow by streamlining administrative tasks and optimising allocation of resources. Secondly, real-time data analytics systems could support clinician decision-making in triage, discharge, diagnosis and treatment stages. Finally, longer-term, development of solutions such as digital phenotyping could help transform mental health care to a more preventative, personalised model.
Recommendations were formulated for NHS trusts open to adopting AI patient flow enhancements. Although AI offers many promising use-cases, greater collaborative investment and infrastructure are needed to deliver clinically validated improvements. Concerns around data-use, regulation and transparency remain, and hospitals must continue to balance guidelines with stakeholder priorities. Further research is needed to connect existing case studies and develop a framework for their evaluation.
对心理健康服务的需求不断增长，再加上资金和资源的限制，为包括人工智能（AI）在内的新型技术解决方案创造了机会。本研究旨在确定心理健康科室患者流程中的问题，并将其与潜在的人工智能解决方案相结合，最终设计出一个在服务层面整合这些方案的模型。
在进行叙述性文献综述和试点访谈之后，对人工智能和心理健康专家进行了20次半结构化访谈。然后采用主题分析法对收集到的数据进行分析和综合，并构建一个改进模型。
文献中关于住院时间和再入院率的预测变量并不一致。然而，患者流程问题存在一些共同主题。一项分析确定了几个人工智能可优化患者流程的潜在领域。首先，人工智能可以通过简化行政任务和优化资源分配来改善患者流程。其次，实时数据分析系统可以在分诊、出院、诊断和治疗阶段支持临床医生的决策。最后，从长远来看，数字表型等解决方案的开发有助于将心理健康护理转变为更具预防性、个性化的模式。
为愿意采用人工智能改善患者流程的英国国家医疗服务体系（NHS）信托机构制定了建议。尽管人工智能提供了许多有前景的应用案例，但需要更多的合作投资和基础设施来实现经过临床验证的改进。对数据使用、监管和透明度的担忧依然存在，医院必须继续在指导方针与利益相关者的优先事项之间取得平衡。需要进一步的研究来连接现有的案例研究，并开发一个评估框架。
