Barriers and facilitators to integrating care: experiences from the English Integrated Care Pilots
Bibliographic record
Abstract
BACKGROUND: In 2008, the English Department of Health appointed 16 'Integrated Care Pilots' which used a range of approaches to provide better integrated care. We report qualitative analyses from a three-year multi-method evaluation to identify barriers and facilitators to successful integration of care. THEORY AND METHODS: Data were analysed from transcripts of 213 in-depth staff interviews, and from semi-structured questionnaires (the 'Living Document') completed by staff in pilot sites at six points over a two-year period. Emerging findings were therefore built from 'bottom up' and grounded in the data. However, we were then interested in how these findings compared and contrasted with more generic analyses. Therefore after our analyses were complete we then systematically compared and contrasted the findings with the analysis of barriers and facilitators to quality improvement identified in a systematic review by Kaplan et al. (2010) and the analysis of more micro-level shapers of behaviour found in Normalisation Process Theory (May et al. 2007). Neither of these approaches claims to be full blown theories but both claim to provide mid-range theoretical arguments which may be used to structure existing data and which can be undercut or reinforced by new data. RESULTS AND DISCUSSION: Many barriers and facilitators to integrating care are those of any large-scale organisational change. These include issues relating to leadership, organisational culture, information technology, physician involvement, and availability of resources. However, activities which appear particularly important for delivering integrated care include personal relationships between leaders in different organisations, the scale of planned activities, governance and finance arrangements, support for staff in new roles, and organisational and staff stability. We illustrate our analyses with a 'routemap' which identifies questions that providers may wish to consider when planning interventions to improve the integration of care.
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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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".