Learning from a clinical microsystems quality improvement initiative to promote integrated care across a falls care pathway
Bibliographic record
Abstract
AIM: To identify learning from a clinical microsystems (CMS) quality improvement initiative to develop a more integrated service across a falls care pathway spanning community and hospital services. BACKGROUND: Falls present a major challenge to healthcare providers internationally as populations age. A review of the falls care pathway in Sheffield, United Kingdom, identified that pathway implementation was constrained by inconsistent co-ordination and integration at the hospital-community interface. APPROACH: The initiative utilised the CMS quality improvement approach and comprised three phases. Phase 1 focussed on developing a climate for change through engaging stakeholders across the existing pathway and coaching frontline teams operating as microsystems in quality improvement. Phase 2 involved initiating change by working at the mesosystem level to identify priorities for improvement and undertake tests of change. Phase 3 engaged decision makers at the macrosystem level from across the wider pathway in achieving change identified in earlier phases of the initiative. FINDINGS: The initiative was successful in delivering change in relation to key aspects of the pathway, engaging frontline staff and decision makers from different services within the pathway, and in building quality improvement capability within the workforce. Viewing the pathway as a series of interrelated CMS enabled stakeholders to understand the complex nature of the pathway and to target key areas for change. Particular challenges encountered arose from organisational reconfiguration and cross-boundary working. CONCLUSION: CMS quality improvement methodology may be a useful approach to promoting integration across a care pathway. Using a CMS approach contributed towards clinical and professional integration of some aspects of the service. Recognition of the pathway operating at meso- and macrosystem levels fostered wider stakeholder engagement with the potential of improving integration of care across a range of health and care providers involved in the pathway.
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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 | 0.057 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".