The development of the Primary Care Clusters Multidimensional Assessment (PCCMA): A mixed-methods study

Stanciu, Marian Andrei; Law, Rebecca‐Jane; Myres, Paul; Parsonage, Rachel; Hiscock, Julia; Williams, Nefyn; Wilkinson, Clare

doi:10.1016/j.healthpol.2019.12.004

Cited by 11 publications

(25 citation statements)

References 19 publications

(27 reference statements)

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“…Similar to findings from a study involving Welsh cluster leads, 28 some participants were also uncertain about how clusters will evolve. Uncertainty for these Welsh leads related to future funding arrangements for their clusters, but the participants in this study were concerned more with tensions around expectation for CQLs to ‘police’ or collaboratively engage with their cluster members.…”

Section: Discussionmentioning

confidence: 55%

“… 29 The internally driven collaboration observed in this study is arguably a firm foundation on which clusters can improve their integration with local public health and social care agencies, structures, and processes. 28 , 30…”

Section: Discussionmentioning

confidence: 99%

“…Since 2017/2018, all Scottish GP practices have been members of one of 147 clusters, of differing sizes depending on local circumstances and geography. A cluster typically includes approximately four to eight practices with 20,000-40,000 registered patients, 5 regularly meeting to identify quality issues important in the locality, working collaboratively to meet the healthcare needs of their population (further information about these clusters can be found in the supplementary file).…”

Section: Introductionmentioning

confidence: 99%

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Collaborative improvement in Scottish GP clusters after the Quality and Outcomes Framework: a qualitative study

et al. 2021

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Background: Scotland abolished the Quality and Outcomes Framework (QOF) in April 2016, prior to implementing a new Scottish GP contract in April 2018. Since 2016, groups of practices (GP clusters) have been incentivised to meet regularly, to plan and organise quality improvement (QI) as part of this new direction in primary care policy. Aim: To understand the organisation and perceived impact of GP clusters, including how they use quantitative data for improvement. Design/Setting/Methods: Thematic analysis, of semi-structured interviews with key stakeholders (n=17) and observations of GP cluster meetings (n=6) in two clusters. Results: There was uncertainty whether GP clusters should focus on activities generated internally or externally by the wider healthcare system (e.g. from Scottish Health Boards), although the two clusters we observed generally generated their own ideas and issues. Clusters operated with variable administrative/managerial and data support, and variable baseline leadership experience and QI skills. Qualitative approaches formed the focus of collaborative learning in cluster meetings, through sharing and discussion of member practices’ own understandings and experiences. We observed less evidence of data analytics being championed in these meetings, partly because of barriers accessing the analytics data and existing data quality. Conclusion: Cluster development would benefit from more consistent training and support for cluster leads in small group facilitation, leadership and QI expertise, and data analytics access and capacity. Whilst GP clusters are up and running, their impact is likely to be limited without further investment in developing capacity in these areas.

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Section: Discussionmentioning

confidence: 55%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Collaborative improvement in Scottish GP clusters after the Quality and Outcomes Framework: a qualitative study

et al. 2021

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“…Representativeness was examined by comparison with national GP data. 16 The survey was broadly representative in terms of gender, age, rurality, and deprivation (Supplementary Table 1).…”

Section: Participants and Representativenessmentioning

confidence: 99%

“…A recent study from Wales reported on the development of a primary care clusters multidimensional assessment (PCCMA) which had been piloted on 38 CQLs, consisting of 53 indicators across 11 systemic dimensions of primary care. 16 Reliability and validity analysis was absent, presumably due to the small sample size. It is noteworthy that the bespoke items in the current study overlap with items and concepts in nine of the 11 PCCMA dimensions.…”

Section: Comparison With Existing Literaturementioning

confidence: 99%

Progress of GP clusters 2 years after their introduction in Scotland: findings from the Scottish School of Primary Care national GP survey

2020

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Background GP Clusters commenced across Scotland in 2016 to improve the quality of care of local populations Aim To determine GPs views on GP clusters, and the robustness of bespoke questions Design & setting Cross-sectional national survey of work satisfaction of GPs working in Scotland, conducted in the latter half of 2018 Method Analysis of bespoke questions on GP Clusters completed by a) Quality Leads (QLs), and b) all other GPs in a nationally representative sample of GPs. Results QLs reported that Clusters were meeting regularly and were friendly and well organised but not always productive. Support for cluster activity (data, health intelligence, analysis, quality improvement methods, advice, leadership, and evaluation) was sub-optimal. Factor analysis identified two separate constructs (cluster meetings and cluster support), which were minimally influenced (<2%) by GP and practice characteristics. Non-Quality Leads (75% of all GPs) were generally satisfied with the two-way communication with the cluster quality leads, but the great majority (> 70%) reported no positive changes in various aspects of quality improvement. Factor analysis of these items indicated two constructs (cluster communication and cluster quality improvement) which were minimally affected by GP and practice characteristics. Conclusion GP Clusters are ‘up and running’ in Scotland but are at an early stage in terms of perceived impact, and appear to be in need of more support in order to improve quality of care. The bespoke questions developed on clusters have robust construct validity, suitable for future surveys.

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Data driven or data informed? How general practitioners use data to evaluate their own and colleagues’ clinical work in clusters

Haase,

Bearman,

Brodersen

et al. 2023

Sociology Health & Illness

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In contemporary policy discourses, data are presented as key assets for improving health‐care quality: policymakers want health care to become ‘data driven’. In this article, we focus on a particular example of this ambition, namely a new Danish national quality development program for general practitioners (GPs) where doctors are placed in so‐called ‘clusters’. In these clusters, GPs are obliged to assess their own and colleagues’ clinical quality with data derived from their own clinics—using comparisons, averages and benchmarks. Based on semi‐structured interviews with Danish GPs and drawing on Science and Technology Studies, we explore how GPs understand these data, and what makes them trust—or question—a data analysis. The GPs describe how they change clinical practices based on these discussions of data. So, when and how do data for quality assurance come to influence their perceptions of quality? By exploring these issues, we carve out a role for a sociological engagement with evidence in everyday medical practices. In conclusion, we suggest a need to move from the aim of being data driven to one of being data informed.

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The development of the Primary Care Clusters Multidimensional Assessment (PCCMA): A mixed-methods study

Cited by 11 publications

References 19 publications

Collaborative improvement in Scottish GP clusters after the Quality and Outcomes Framework: a qualitative study

Collaborative improvement in Scottish GP clusters after the Quality and Outcomes Framework: a qualitative study

Progress of GP clusters 2 years after their introduction in Scotland: findings from the Scottish School of Primary Care national GP survey

Data driven or data informed? How general practitioners use data to evaluate their own and colleagues’ clinical work in clusters

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