Reducing emergency hospital admissions: a population health complex intervention of an enhanced model of primary care and compassionate communities
Bibliographic record
Abstract
Background Reducing emergency admissions to hospital has been a cornerstone of healthcare policy. Little evidence exists to show that systematic interventions across a population have achieved this aim. The authors report the impact of a complex intervention over a 44-month period in Frome, Somerset, on unplanned admissions to hospital. Aim To evaluate a population health complex intervention of an enhanced model of primary care and compassionate communities on population health improvement and reduction of emergency admissions to hospital. Design and setting A cohort retrospective study of a complex intervention on all emergency admissions in Frome Medical Practice, Somerset, compared with the remainder of Somerset, from April 2013 to December 2017. Method Patients were identified using broad criteria, including anyone giving cause for concern. Patient-centred goal setting and care planning combined with a compassionate community social approach was implemented broadly across the population of Frome. Results There was a progressive reduction, by 7.9 cases per quarter (95% confidence interval [CI] = 2.8 to 13.1, P = 0.006), in unplanned hospital admissions across the whole population of Frome during the study period from April 2013 to December 2017, a decrease of 14.0%. At the same time, there was a 28.5% increase in admissions per quarter within Somerset, with a rise in the number of unplanned admissions of 236 per quarter (95% CI = 152 to 320, P <0.001). Conclusion The complex intervention in Frome was associated with highly significant reductions in unplanned admissions to hospital, with a decrease in healthcare costs across the whole population of Frome.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".