Academic Unit for Ageing & Stroke Research, Bradford Teaching Hospitals NHS Foundation Trust, University of Leeds, Bradford, United Kingdom.
Faculty of Medicine and Health, School of Medicine, University of Leeds, Leeds, United Kingdom.
PLoS One. 2024 Aug 30;19(8):e0299770. doi: 10.1371/journal.pone.0299770. eCollection 2024.
Structured medication reviews (SMRs), introduced in the United Kingdom (UK) in 2020, aim to enhance shared decision-making in medication optimisation, particularly for patients with multimorbidity and polypharmacy. Despite its potential, there is limited empirical evidence on the implementation of SMRs, and the challenges faced in the process. This study is part of a larger DynAIRx (Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity) project which aims to introduce Artificial Intelligence (AI) to SMRs and develop machine learning models and visualisation tools for patients with multimorbidity. Here, we explore how SMRs are currently undertaken and what barriers are experienced by those involved in them.
Qualitative focus groups and semi-structured interviews took place between 2022-2023. Six focus groups were conducted with doctors, pharmacists and clinical pharmacologists (n = 21), and three patient focus groups with patients with multimorbidity (n = 13). Five semi-structured interviews were held with 2 pharmacists, 1 trainee doctor, 1 policy-maker and 1 psychiatrist. Transcripts were analysed using thematic analysis.
Two key themes limiting the effectiveness of SMRs in clinical practice were identified: 'Medication Reviews in Practice' and 'Medication-related Challenges'. Participants noted limitations to the efficient and effectiveness of SMRs in practice including the scarcity of digital tools for identifying and prioritising patients for SMRs; organisational and patient-related challenges in inviting patients for SMRs and ensuring they attend; the time-intensive nature of SMRs, the need for multiple appointments and shared decision-making; the impact of the healthcare context on SMR delivery; poor communication and data sharing issues between primary and secondary care; difficulties in managing mental health medications and specific challenges associated with anticholinergic medication.
SMRs are complex, time consuming and medication optimisation may require multiple follow-up appointments to enable a comprehensive review. There is a need for a prescribing support system to identify, prioritise and reduce the time needed to understand the patient journey when dealing with large volumes of disparate clinical information in electronic health records. However, monitoring the effects of medication optimisation changes with a feedback loop can be challenging to establish and maintain using current electronic health record systems.
结构药物审查(SMRs)于 2020 年在英国(UK)推出,旨在增强药物优化方面的共同决策,特别是针对患有多种疾病和多种药物的患者。尽管它具有潜力,但关于 SMR 的实施及其面临的挑战,经验证据有限。这项研究是 DynAIRx(人工智能在多种疾病中的动态处方优化和护理整合)项目的一部分,旨在将人工智能引入 SMR,并为患有多种疾病的患者开发机器学习模型和可视化工具。在这里,我们探讨了 SMR 目前是如何进行的,以及参与其中的人所面临的障碍。
2022-2023 年期间进行了定性焦点小组和半结构化访谈。与医生、药剂师和临床药理学家进行了 6 次焦点小组讨论(n=21),与患有多种疾病的患者进行了 3 次患者焦点小组讨论(n=13)。与 2 名药剂师、1 名实习医生、1 名政策制定者和 1 名精神科医生进行了 5 次半结构化访谈。使用主题分析对转录本进行分析。
确定了限制 SMR 在临床实践中有效性的两个关键主题:“实践中的药物审查”和“与药物相关的挑战”。参与者指出了 SMR 在实践中的效率和有效性的限制,包括用于识别和优先考虑 SMR 患者的数字工具稀缺;邀请患者进行 SMR 并确保他们参加的组织和患者相关挑战;SMR 的时间密集性质,需要多次预约和共同决策;医疗保健环境对 SMR 交付的影响;初级保健和二级保健之间沟通和数据共享问题差;管理精神健康药物的困难以及与抗胆碱能药物相关的特定挑战。
SMRs 很复杂,耗时且药物优化可能需要多次随访预约,以实现全面审查。需要一个处方支持系统来识别、优先排序和减少处理电子健康记录中大量不同临床信息时理解患者旅程所需的时间。然而,使用当前的电子健康记录系统建立和维护监测药物优化变化效果的反馈循环可能具有挑战性。