Colling Craig, Mueller Christoph, Perera Gayan, Funnell Nicola, Sauer Justin, Harwood Daniel, Stewart Robert, Bishara Delia
Biomedical Research Centre (BRC), Institute of Psychiatry, Psychology and Neuroscience, London, UK.
Mental Health of Older Adults and Dementia Clinical Academic Group (SLaM), South London and Maudsley NHS Foundation Trust, London, UK.
BMJ Open Qual. 2020 Mar;9(1). doi: 10.1136/bmjoq-2019-000778.
The use of antipsychotic drugs in dementia has been reported to be associated with increased risk of cerebrovascular events and mortality. There is an international drive to reduce the use of these agents in patients with dementia and to improve the safety of prescribing and monitoring in this area.
The aim of this project was to use enhanced automated regular feedback of information from electronic health records to improve the quality of antipsychotic prescribing and monitoring in people with dementia.
The South London and Maudsley NHS Foundation Trust (SLaM) incorporated antipsychotic monitoring forms into its electronic health records. The SLaM Clinical Record Interactive Search (CRIS) platform provides researcher access to de-identified health records, and natural language processing is used in CRIS to derive structured data from unstructured free text, including recorded diagnoses and medication. Algorithms were thus developed to ascertain patients with dementia receiving antipsychotic treatment and to determine whether monitoring forms had been completed. We used two improvement plan-do-study-act cycles to improve the accuracy of the algorithm for automated evaluation and provided monthly feedback on team performance.
A steady increase in antipsychotic monitoring form completion was observed across the study period. The percentage of our sample with a completed antipsychotic monitoring form more than doubled from October 2017 (22%) to January 2019 (58%).
'Real time' monitoring and regular feedback to teams offer a time-effective approach, complementary to standard audit methods, to enhance the safer prescribing of high risk drugs.
据报道,在痴呆症患者中使用抗精神病药物与脑血管事件风险增加和死亡率上升有关。国际上正在努力减少这些药物在痴呆症患者中的使用,并提高该领域处方和监测的安全性。
本项目的目的是利用电子健康记录中增强的自动化定期信息反馈,以提高痴呆症患者抗精神病药物处方和监测的质量。
南伦敦和莫兹利国民保健服务基金会信托基金(SLaM)将抗精神病药物监测表格纳入其电子健康记录。SLaM临床记录交互式搜索(CRIS)平台为研究人员提供了访问去识别化健康记录的权限,并且在CRIS中使用自然语言处理从非结构化自由文本中提取结构化数据,包括记录的诊断和用药情况。因此开发了算法来确定接受抗精神病治疗的痴呆症患者,并确定监测表格是否已填写。我们使用了两个改进的计划-执行-研究-行动循环来提高自动评估算法的准确性,并每月提供团队绩效反馈。
在整个研究期间,观察到抗精神病药物监测表格填写情况稳步增加。我们样本中填写了抗精神病药物监测表格的比例从2017年10月(22%)到2019年1月(58%)增加了一倍多。
对团队进行“实时”监测和定期反馈提供了一种省时的方法,是对标准审计方法的补充,可加强高风险药物的安全处方。