Evidence map›Paper›PMID 34281587›Full record

ArticleImplementation science : IS2021

Data-driven approach for tailoring facilitation strategies to overcome implementation barriers in community pharmacy.

Lydia Moussa, Shalom Benrimoj, Katarzyna Musial, Simon Kocbek, Victoria Garcia-Cardenas

Open access · goldAbstract read
In one paragraph

Article in Implementation science : IS, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
2.6field-weighted citation impact, top 10% of its field
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

16 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.

  1. Tailored interventions to address determinants of practice.The Cochrane database of systematic reviews · 2026
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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

5 authors at 2 institutions in 2 countries.

Lydia MoussaGraduate School of Health, Faculty of Health, University of Technology Sydney, PO Box 123, Broadway, Sydney, New South Wales, 2007, Australia.
Shalom BenrimojPharmaceutical Care Research Group, Faculty of Pharmacy, University of Granada, Campus Universitario de Cartuja s/n, 18071, Granada, Spain.
Katarzyna MusialAdvanced Analytics Institute, School of Computer Science, Faculty of Engineering and IT, University of Technology Sydney, PO Box 123, Broadway, Sydney, New South Wales, 2007, Australia.
Simon KocbekAdvanced Analytics Institute, School of Software, Faculty of Engineering and IT, University of Technology Sydney, PO Box 123, Broadway, Sydney, New South Wales, 2007, Australia.
Victoria Garcia-CardenasGraduate School of Health, Faculty of Health, University of Technology Sydney, PO Box 123, Broadway, Sydney, New South Wales, 2007, Australia. victoria.garciacardenas@uts.edu.au.ORCID 0000-0003-3770-4557
University of Technology Sydney · AUUniversidad de Granada · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundImplementation research has delved into barriers to implementing change and interventions for the implementation of innovation in practice. There remains a gap, however, that fails to connect implementation barriers to the most effective implementation strategies and provide a more tailored approach during implementation. This study aimed to explore barriers for the implementation of professional services in community pharmacies and to predict the effectiveness of facilitation strategies to overcome implementation barriers using machine learning techniques.

methodsSix change facilitators facilitated a 2-year change programme aimed at implementing professional services across community pharmacies in Australia. A mixed methods approach was used where barriers were identified by change facilitators during the implementation study. Change facilitators trialled and recorded tailored facilitation strategies delivered to overcome identified barriers. Barriers were coded according to implementation factors derived from the Consolidated Framework for Implementation Research and the Theoretical Domains Framework. Tailored facilitation strategies were coded into 16 facilitation categories. To predict the effectiveness of these strategies, data mining with random forest was used to provide the highest level of accuracy. A predictive resolution percentage was established for each implementation strategy in relation to the barriers that were resolved by that particular strategy.

resultsDuring the 2-year programme, 1131 barriers and facilitation strategies were recorded by change facilitators. The most frequently identified barriers were a 'lack of ability to plan for change', 'lack of internal supporters for the change', 'lack of knowledge and experience', 'lack of monitoring and feedback', 'lack of individual alignment with the change', 'undefined change objectives', 'lack of objective feedback' and 'lack of time'. The random forest algorithm used was able to provide 96.9% prediction accuracy. The strategy category with the highest predicted resolution rate across the most number of implementation barriers was 'to empower stakeholders to develop objectives and solve problems'.

conclusionsResults from this study have provided a better understanding of implementation barriers in community pharmacy and how data-driven approaches can be used to predict the effectiveness of facilitation strategies to overcome implementation barriers. Tailored facilitation strategies such as these can increase the rate of real-time implementation of innovations in healthcare, leading to an industry that can confidently and efficiently adapt to continuous change.

Indexed as

PharmaciesAustraliaDelivery of Health CareHealth FacilitiesHumansPharmacistsChange facilitationChange managementDeterminantsFacilitation strategiesImplementation factorsMachine learningOrganisational changePharmacy practiceRandom forestTailored interventions

Identifiers

PMID34281587
PMCPMC8290596
OpenAlexW3136338980

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 OpenQuestion graph.