Department of Psychosis Studies, Division of Academic Psychiatry, Institute of Psychiatry Psychology and Neuroscience, London, UK.
NIHR Maudsley Biomedical Research Centre, South London and Maudsley Mental Health NHS Trust, London, UK.
BMJ Open. 2023 Feb 10;13(2):e067254. doi: 10.1136/bmjopen-2022-067254.
People often experience significant difficulties in receiving mental healthcare due to insufficient resources, stigma and lack of access to care. Remote care technology has the potential to overcome these barriers by reducing travel time and increasing frequency of contact with patients. However, the safe delivery of remote mental healthcare requires evidence on which aspects of care are suitable for remote delivery and which are better served by in-person care. We aim to investigate clinical and demographic associations with remote mental healthcare in a large electronic health record (EHR) dataset and the degree to which remote care is associated with differences in clinical outcomes using natural language processing (NLP) derived EHR data.
Deidentified EHR data, derived from the South London and Maudsley (SLaM) National Health Service Foundation Trust Biomedical Research Centre (BRC) Case Register, will be extracted using the Clinical Record Interactive Search tool for all patients receiving mental healthcare between 1 January 2019 and 31 March 2022. First, data on a retrospective, longitudinal cohort of around 80 000 patients will be analysed using descriptive statistics to investigate clinical and demographic associations with remote mental healthcare and multivariable Cox regression to compare clinical outcomes of remote versus in-person assessments. Second, NLP models that have been previously developed to extract mental health symptom data will be applied to around 5 million documents to analyse the variation in content of remote versus in-person assessments.
The SLaM BRC Case Register and Clinical Record Interactive Search (CRIS) tool have received ethical approval as a deidentified dataset (including NLP-derived data from unstructured free text documents) for secondary mental health research from Oxfordshire REC C (Ref: 18/SC/0372). The study has received approval from the SLaM CRIS Oversight Committee. Study findings will be disseminated through peer-reviewed, open access journal articles and service user and carer advisory groups.
由于资源不足、污名化和获得护理的机会有限，人们在接受精神保健方面经常遇到重大困难。远程护理技术有可能通过减少旅行时间和增加与患者的接触频率来克服这些障碍。然而，安全提供远程精神保健需要证据来证明哪些护理方面适合远程提供，哪些方面更适合面对面护理。我们旨在通过自然语言处理 (NLP) 从电子健康记录 (EHR) 数据中提取的 EHR 数据，调查大型 EHR 数据集中心理远程保健的临床和人口统计学关联，以及远程护理与临床结果差异的关联程度。
将使用 Clinical Record Interactive Search 工具从南伦敦和莫兹利 (SLaM) 国民保健服务基金会信托生物医学研究中心 (BRC) 病例登记处提取 2019 年 1 月 1 日至 2022 年 3 月 31 日期间接受精神保健的所有患者的匿名 EHR 数据。首先，使用描述性统计数据对大约 80000 名患者的回顾性纵向队列数据进行分析，以调查远程精神保健的临床和人口统计学关联，并使用多变量 Cox 回归比较远程与面对面评估的临床结果。其次，将应用以前开发的用于提取精神健康症状数据的 NLP 模型对大约 500 万份文件进行分析，以分析远程与面对面评估内容的差异。
SLaM BRC 病例登记处和 Clinical Record Interactive Search (CRIS) 工具已获得牛津 REC C（参考号：18/SC/0372）的伦理批准，作为二级精神健康研究的匿名数据集（包括来自非结构化自由文本文档的 NLP 衍生数据）。该研究已获得 SLaM CRIS 监督委员会的批准。研究结果将通过同行评议、开放获取期刊文章和服务用户和护理人员咨询小组进行传播。
