Exploring interhospital transfers and partnerships in the hospital sector in New South Wales, Australia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objective The aim of the present study was to explore characteristics of interhospital transfers (IHT) and sharing of care among hospitals in New South Wales (NSW), Australia. Methods Data were extracted from patient-level linked hospital administrative datasets for separations from all NSW acute care hospitals from 1 July 2013 to 30 June 2015. Patient discharge and arrival information was used to identify IHTs. Characteristics of patients and related hospitals were then analysed. Results Transfer-in patients accounted for 3.9% of all NSW admitted patients and, overall, 7.3% of NSW admissions were associated with transfers (IHT rate). Patients with injuries and circulatory system diseases had the highest IHT rate, accounting for one-third of all IHTs. Patients were more often transferred to larger than smaller hospitals (61% vs 29%). Compared with private hospitals, public hospitals had a higher IHT rate (8.4% vs 5.1%) and a greater proportion of transfer-out IHTs (52% vs 28%). Larger public hospitals had lower IHT rates (3-8%) compared with smaller public hospitals (13-26%). Larger public hospitals received and retransferred higher proportions of IHT patients (52-58% and 11% respectively) than their smaller counterparts (26-30% and 2-3% respectively). Less than one-quarter of IHTs were between the public and private sectors or between government health regions. The number of interacting hospitals and their interactions varied across hospital peer groups. Conclusion NSW IHTs were often to hospitals with greater speciality services. The patterns of interhospital interactions could be affected by organisational and regional preferences. What is known about the topic? IHTs aim to provide efficient and effective care. Nonetheless, information on transfers and the sharing of care among hospitals in an Australian setting is lacking. Studies of transfers and hospital partnership patterns will inform efforts to improve patient-centred transfers and hospital accountability in terms of end outcomes for patients. What does this paper add? Transfer-in patients accounted for 3.9% of all NSW admissions; they were often (61%) transferred to hospitals with greater speciality services. The number of IHTs and sharing of care among hospitals varied across hospital peer groups, and could have been affected by organisational and regional preferences. What are the implications for practitioners? The findings of the present study suggest that different patterns of IHTs may not only have resulted from clinical priorities, but that organisational and regional preferences are also likely to be influential factors. Patient-centred IHTs and the development of guidelines need to be pursued to enhance the care and functionality of healthcare. Patient sharing should be acknowledged in hospital and regional performance profiling.
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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) | 0.001 | 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 | 0.000 |
| 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 it