Building managed primary care practice networks to deliver better clinical care: a qualitative semi-structured interview study
Bibliographic record
Abstract
BACKGROUND: Primary care practices are increasingly working in larger groups. In 2009, all 36 primary care practices in the London borough of Tower Hamlets were grouped geographically into eight managed practice networks to improve the quality of care they delivered. Quantitative evaluation has shown improved clinical outcomes. AIM: To provide insight into the process of network implementation, including the aims, facilitating factors, and barriers, from both the clinical and managerial perspectives. DESIGN AND SETTING: A qualitative study of network implementation in the London borough of Tower Hamlets, which serves a socially disadvantaged and ethnically diverse population. METHOD: Nineteen semi-structured interviews were carried out with doctors, nurses, and managers, and were informed by existing literature on integrated care and GP networks. Interviews were recorded and transcribed, and thematic analysis used to analyse emerging themes. RESULTS: Interviewees agreed that networks improved clinical care and reduced variation in practice performance. Network implementation was facilitated by the balance struck between 'a given structure' and network autonomy to adopt local solutions. Improved use of data, including patient recall and peer performance indicators, were viewed as critical key factors. Targeted investment provided the necessary resources to achieve this. Barriers to implementing networks included differences in practice culture, a reluctance to share data, and increased workload. CONCLUSION: Commissioners and providers were positive about the implementation of GP networks as a way to improve the quality of clinical care in Tower Hamlets. The issues that arose may be of relevance to other areas implementing similar quality improvement programmes at scale.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".