Analysis of the Impact of the Birmingham OwnHealth Program on Secondary Care Utilization and Cost: A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: This study aimed to assess the impact of the Birmingham OwnHealth(®) program (a partnership among the National Health Service [NHS] Birmingham East and North, formerly Birmingham East and North PCT, as the commissioner, Pfizer Health Solutions [Tadworth, United Kingdom] as the primary contractor, and NHS Direct as a subcontractor) on the number of unscheduled secondary care spells and the cost of care for patients with long-term conditions. This article reports a retrospective cohort study conducted at the NHS Birmingham East and North. SUBJECTS AND METHODS: Adults with at least 1 of 10 defined long-term conditions were eligible for inclusion. Patients in the OwnHealth program were compared with those in a matched comparison group from a population who were eligible but did not enroll in the program. The main outcome measures were the difference in the number of secondary care spells (defined as the experience between hospital admission and discharge) between the OwnHealth group and the comparison group and the difference in the cost of care (calculated from the cost of activities during secondary care spells). RESULTS: The mean number of secondary care spells per person per year in the OwnHealth group was 0.61 (standard deviation [SD] 1.35) compared with 0.84 (SD 1.49) in the comparison group (p<0.0005). This constituted a 27% reduction in secondary care spells per person per year. The mean cost of secondary care spells per person per year in the OwnHealth group was $1,305 (SD $3,138) compared with $1,678 (SD $3,485) in the comparison group (p<0.0005). DISCUSSION: This difference in costs constituted a 27% reduction in utilization and 22% reduction in cost of secondary care with the OwnHealth program. CONCLUSIONS: Telehealth intervention can reduce the cost of secondary care of some patients with long-term conditions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".