Specialist medication monitoring and prescribing in primary care: case study of shared care agreements in Northern England, UK
Bibliographic record
Abstract
INTRODUCTION: Shared care agreements (SCAs) in the UK enable general practitioners (GPs) in primary care to take over the monitoring and prescribing of specialist medications for patients under agreed protocols. While SCAs are intended to improve access and continuity of care, concerns regarding their implementation and adherence to safety protocols persist. This study aims to explore the mechanisms, challenges and risks associated with SCAs, focusing on their impact on patient safety and primary care capacity. METHODS: A case-study approach was employed to investigate the implementation of SCAs, incorporating mixed methods to provide a comprehensive understanding. Data triangulation included document analysis of policies, cross-sectional review of medication monitoring and prescribing practices across 37 GP practices, and key informant interviews with stakeholders. Logic and dark logic models were iteratively developed to map the intended and unintended outcomes of SCAs. RESULTS: The monitoring and prescribing review revealed 32.3% of prescribed medications under SCAs lacked up-to-date monitoring data, with attention-deficit/hyperactivity disorder medications showing the highest rates of non-compliance. Interviews highlighted systemic challenges, including unclear responsibilities, inadequate patient involvement, fragmented communication between primary and secondary care, and insufficient integration of digital systems. These gaps contribute to patient safety risks, particularly for high-risk medications requiring stringent monitoring. CONCLUSIONS: SCAs hold potential for improving care continuity but face significant operational and systemic barriers that undermine their safety and effectiveness. Findings evidence the need for clearer role delineation, robust communication frameworks, enhanced patient engagement and integrated digital solutions. Policy-makers and healthcare leaders must address these challenges to ensure SCAs deliver on their promise of seamless, safe and sustainable care. Future research should focus on incorporating the perspectives of secondary care providers and pharmacists to develop more inclusive solutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".