Evidence map›Paper›PMID 39213435›Full record

ArticlePloS one2024

A qualitative exploration of barriers to efficient and effective structured medication reviews in primary care: Findings from the DynAIRx study.

Aseel S Abuzour, Samantha A Wilson, Alan A Woodall, Frances S Mair, Andrew Clegg, Eduard Shantsila, Mark Gabbay, Michael Abaho, Asra Aslam, Danushka Bollegala and 15 more

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Antipsychotic management in general practice: serial cross-sectional study (2011-2020).The British journal of general practice : the journal of the Royal College of General Practitioners · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

25 authors.

Aseel S AbuzourAcademic Unit for Ageing & Stroke Research, Bradford Teaching Hospitals NHS Foundation Trust, University of Leeds, Bradford, United Kingdom.ORCID 0000-0002-4073-4346
Samantha A WilsonInstitute of Population Health, University of Liverpool, Liverpool, United Kingdom.
Alan A WoodallInstitute of Population Health, University of Liverpool, Liverpool, United Kingdom.
Frances S MairGeneral Practice and Primary Care, School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom.ORCID 0000-0001-9780-1135
Andrew CleggAcademic Unit for Ageing & Stroke Research, Bradford Teaching Hospitals NHS Foundation Trust, University of Leeds, Bradford, United Kingdom.
Eduard ShantsilaInstitute of Population Health, University of Liverpool, Liverpool, United Kingdom.
Mark GabbayInstitute of Population Health, University of Liverpool, Liverpool, United Kingdom.
Michael AbahoInstitute of Population Health, University of Liverpool, Liverpool, United Kingdom.
Asra AslamFaculty of Medicine and Health, School of Medicine, University of Leeds, Leeds, United Kingdom.ORCID 0000-0002-2654-4255
Danushka BollegalaDepartment of Computer Science, University of Liverpool, Liverpool, United Kingdom.
Harriet CantDivision of Informatics, Imaging & Data Science, University of Manchester, Manchester, United Kingdom.ORCID 0009-0001-0235-3518
Alan GriffithsNIHR Applied Research Collaboration North West Coast, United Kingdom.
Layik HamaLeeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom.ORCID 0000-0003-1912-4890
Gary LeemingInstitute of Population Health, University of Liverpool, Liverpool, United Kingdom.ORCID 0000-0002-5554-5302
Emma LoInstitute of Population Health, University of Liverpool, Liverpool, United Kingdom.
Simon MaskellDepartment of Electrical Engineering and Electronics, University of Liverpool, Liverpool, United Kingdom.
Maurice O'ConnellDivision of Informatics, Imaging & Data Science, University of Manchester, Manchester, United Kingdom.ORCID 0000-0001-9658-495X
Olusegun PopoolaMerseycare NHS Foundation Trust, Liverpool, United Kingdom.ORCID 0000-0002-7160-3146
Samuel ReltonFaculty of Medicine and Health, School of Medicine, University of Leeds, Leeds, United Kingdom.
Roy A RuddleLeeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom.
Pieta SchofieldInstitute of Population Health, University of Liverpool, Liverpool, United Kingdom.ORCID 0000-0002-6398-2537
Matthew SperrinDivision of Informatics, Imaging & Data Science, University of Manchester, Manchester, United Kingdom.
Tjeerd Van StaaDivision of Informatics, Imaging & Data Science, University of Manchester, Manchester, United Kingdom.
Iain BuchanInstitute of Population Health, University of Liverpool, Liverpool, United Kingdom.ORCID 0000-0003-3392-1650
Lauren E WalkerCentre for Experimental Therapeutics, University of Liverpool, Liverpool, United Kingdom.ORCID 0000-0003-1354-0826

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionStructured 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.

methodsQualitative 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.

resultsTwo 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.

conclusionSMRs 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.

Indexed as

Focus GroupsPolypharmacyPrimary Health CareAdultAgedArtificial IntelligenceFemaleHumansMaleMiddle AgedMultimorbidityQualitative ResearchUnited Kingdom

Identifiers

PMID39213435
PMCPMC11364411

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.