Cecula Paulina, Yu Jiakun, Dawoodbhoy Fatema Mustansir, Delaney Jack, Tan Joseph, Peacock Iain, Cox Benita
Imperial College London Business School, London, UK.
Imperial College School of Medicine, South Kensington Campus, London, SW7 2BU, UK.
Heliyon. 2021 Apr 15;7(4):e06626. doi: 10.1016/j.heliyon.2021.e06626. eCollection 2021 Apr.
Despite a growing body of research into both Artificial intelligence and mental health inpatient flow issues, few studies adequately combine the two. This review summarises findings in the fields of AI in psychiatry and patient flow from the past 5 years, finds links and identifies gaps for future research.
The OVID database was used to access Embase and Medline. Top journals such as JAMA, Nature and The Lancet were screened for other relevant studies. Selection bias was limited by strict inclusion and exclusion criteria.
3,675 papers were identified in March 2020, of which a limited number focused on AI for mental health unit patient flow. After initial screening, 323 were selected and 83 were subsequently analysed. The literature review revealed a wide range of applications with three main themes: diagnosis (33%), prognosis (39%) and treatment (28%). The main themes that emerged from AI in patient flow studies were: readmissions (41%), resource allocation (44%) and limitations (91%). The review extrapolates those solutions and suggests how they could potentially improve patient flow on mental health units, along with challenges and limitations they could face.
Research widely addresses potential uses of AI in mental health, with some focused on its applicability in psychiatric inpatients units, however research rarely discusses improvements in patient flow. Studies investigated various uses of AI to improve patient flow across specialities. This review highlights a gap in research and the unique research opportunity it presents.
尽管对人工智能和心理健康住院患者流程问题的研究越来越多，但很少有研究能充分将两者结合起来。本综述总结了过去5年精神病学领域人工智能和患者流程方面的研究结果，找出两者之间的联系，并确定未来研究的空白。
使用OVID数据库访问Embase和Medline。筛选了《美国医学会杂志》《自然》和《柳叶刀》等顶级期刊以查找其他相关研究。通过严格的纳入和排除标准限制选择偏倚。
2020年3月共识别出3675篇论文，其中仅有少数关注用于心理健康单元患者流程的人工智能。初步筛选后，选定323篇，随后分析了83篇。文献综述揭示了广泛的应用，主要有三个主题：诊断（33%）、预后（39%）和治疗（28%）。人工智能在患者流程研究中出现的主要主题有：再入院（41%）、资源分配（44%）和局限性（91%）。本综述推断出这些解决方案，并提出它们如何有可能改善心理健康单元的患者流程，以及它们可能面临的挑战和局限性。
研究广泛探讨了人工智能在心理健康方面的潜在用途，一些研究关注其在精神科住院病房的适用性，然而研究很少讨论对患者流程的改善。各项研究调查了人工智能在改善各专科患者流程方面的各种用途。本综述突出了研究中的空白以及其所呈现的独特研究机会。
