Multisite analysis of the timing and outcomes of unplanned transfers from subacute to acute care
Bibliographic record
Abstract
Objective The aim of the present study was to examine the timing and outcomes of patients requiring an unplanned transfer from subacute to acute care. Methods Subacute care in-patients requiring unplanned transfer to an acute care facility within four Victorian health services from 1 January to 31 December 2010 were included in the study. Data were collected using retrospective audit. The primary outcome was transfer within 24 h of subacute care admission. Results In all, 431 patients (median age 81 years) had unplanned transfers; of these, 37.8% had a limitation of medical treatment (LOMT) order. The median subacute care length of stay was 43 h: 29.0% of patients were transferred within 24 h and 83.5% were transferred within 72 h of subacute care admission. Predictors of transfer within 24 h were comorbidity weighting (odds ratio (OR) 1.1, P = 0.02) and LOMT order (OR 2.1, P < 0.01). Hospital admission occurred in 87.2% of patients and 15.4% died in hospital. Predictors of in-hospital mortality were comorbidity weighting (OR 1.2, P < 0.01) and the number of physiological abnormalities in the 24 h preceding transfer (OR 1.3, P < 0.01). Conclusions There is a high rate of unplanned transfers to acute care within 24 h of admission to subacute care. Unplanned transfers are associated with high hospital admission and in-hospital mortality rates. What is known about the topic? Subacute care is becoming a high acuity environment where many patients are at significant risk of clinical deterioration. Systems for recognising and responding to deteriorating patients are well developed in acute care, but still developing in subacute care. What does this paper add? This is the first Australian multisite study of clinical deterioration in patients situated in subacute care facilities. One-third of unplanned transfers occur within 24 h of admission to subacute care. Patients who require unplanned transfer from subacute to acute care have unexpectedly high hospital admission rates and high in-hospital mortality rates. The frequency and completeness of physiological monitoring preceding transfer was low. What are the implications for practitioners? Patients in subacute care require regular physiological assessment and early escalation of care if there are physiological abnormalities. Risk of clinical deterioration should be a factor in the decision to admit patients to subacute care after an acute illness or injury. There is a need to improve systems for recognising and responding to deteriorating patients in subacute care settings.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".