Abuzour, A.S. orcid.org/0000-0002-4073-4346, Wilson, S.A., Woodall, A.A. et al. (22 more authors) (2024) A qualitative exploration of barriers to efficient and effective structured medication reviews in primary care: Findings from the DynAIRx study. PLOS ONE, 19 (8). e0299770. ISSN 1932-6203
Abstract
Introduction
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.
Methods
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.
Results
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.
Conclusion
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 Abuzour et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
Keywords: | Health Services and Systems; Health Sciences; Clinical Research; Health Services; Patient Safety; Networking and Information Technology R&D (NITRD); Machine Learning and Artificial Intelligence; Organisation and delivery of services; Management and decision making; Individual care needs; Generic health relevance; Good Health and Well Being; Humans; Primary Health Care; Focus Groups; Male; Female; Polypharmacy; Qualitative Research; United Kingdom; Multimorbidity; Artificial Intelligence; Middle Aged; Aged; Adult |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Health Sciences (Leeds) > Academic Unit of Elderly Care and Rehabilitation (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Computation Science & Engineering |
Funding Information: | Funder Grant number NIHR National Inst Health Research NIHR203986 |
Depositing User: | Symplectic Publications |
Date Deposited: | 10 Sep 2024 10:44 |
Last Modified: | 10 Sep 2024 10:44 |
Published Version: | https://journals.plos.org/plosone/article?id=10.13... |
Status: | Published |
Publisher: | Public Library of Science |
Identification Number: | 10.1371/journal.pone.0299770 |
Related URLs: | |
Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:216980 |