Centre for Health Informatics, The University of Manchester, Manchester, UK
Chadderton South Health Centre, Oldham, UK.
BMJ Open. 2023 Aug 22;13(8):e076296. doi: 10.1136/bmjopen-2023-076296.
This project applies a Learning Healthcare System (LHS) approach to antibiotic prescribing for common infections in primary care. The approach involves iterations of data analysis, feedback to clinicians and implementation of quality improvement activities by the clinicians. The main research question is, can a knowledge support system (KSS) intervention within an LHS implementation improve antibiotic prescribing without increasing the risk of complications?
A pragmatic cluster randomised controlled trial will be conducted, with randomisation of at least 112 general practices in North-West England. General practices participating in the trial will be randomised to the following interventions: periodic practice-level and individual prescriber feedback using dashboards; or the same dashboards plus a KSS. Data from large databases of healthcare records are used to characterise heterogeneity in antibiotic uses, and to calculate risk scores for clinical outcomes and for the effectiveness of different treatment strategies. The results provide the baseline content for the dashboards and KSS. The KSS comprises a display within the electronic health record used during the consultation; the prescriber (general practitioner or allied health professional) will answer standard questions about the patient's presentation and will then be presented with information (eg, patient's risk of complications from the infection) to guide decision making. The KSS can generate information sheets for patients, conveyed by the clinicians during consultations. The primary outcome is the practice-level rate of antibiotic prescribing (per 1000 patients) with secondary safety outcomes. The data from practices participating in the trial and the dashboard infrastructure will be held within regional shared care record systems of the National Health Service in the UK.
Approved by National Health Service Ethics Committee IRAS 290050. The research results will be published in peer-reviewed journals and also disseminated to participating clinical staff and policy and guideline developers.
ISRCTN16230629.
本项目将学习型医疗保健系统(LHS)应用于初级保健中常见感染的抗生素处方。该方法包括数据分析的迭代、向临床医生提供反馈以及由临床医生实施质量改进活动。主要研究问题是,在 LHS 实施中,知识支持系统(KSS)干预是否可以在不增加并发症风险的情况下改善抗生素处方?
将进行一项实用的集群随机对照试验,对英格兰西北部至少 112 家全科诊所进行随机分组。参与试验的全科诊所将随机分为以下干预组:使用仪表板定期进行实践层面和个体医生反馈;或相同的仪表板加 KSS。来自医疗记录大型数据库的数据用于描述抗生素使用的异质性,并计算临床结果风险评分以及不同治疗策略的有效性。结果为仪表板和 KSS 提供了基线内容。KSS 由电子健康记录中的显示器组成,在咨询期间使用;开处方的医生(全科医生或联合健康专业人员)将回答有关患者表现的标准问题,然后将向他们提供有关患者感染并发症风险的信息(例如),以指导决策。KSS 可以为患者生成信息表,由临床医生在咨询期间传达。主要结局是抗生素处方率(每千名患者),次要安全结局。参与试验的实践和仪表板基础设施的数据将保留在英国国民保健系统的区域共享护理记录系统中。
经英国国民保健服务伦理委员会 IRAS 290050 批准。研究结果将发表在同行评议期刊上,并分发给参与的临床工作人员以及政策和指南制定者。
ISRCTN86472017。