The ‘SAFE’ initiative – An innovative approach to safer patient care in a tertiary hospital setting
Bibliographic record
Abstract
Introduction: Out of hours inpatient care within Australia, defined as the hours outside of 0800 to 1600 hours Monday to Friday, is traditionally provided by on-call medical teams, working in silos, supported by onsite junior medical staff. This model can be associated with poor communication both between and within teams, lack of accountability, failure of escalation, and a reactive model of care. International literature reveals that the outcomes of patients admitted to hospital out of hours are poorer, resulting in a discrepancy in mortality between in and out of hours admissions.Methods: We aimed to reduce the discrepancy in mortality between in and out of hours admissions, as well as reducing overall inpatient mortality. Using a resilience engineering approach, we introduced a novel model of out of hours care - the ‘Safety After Hours for Everyone’ (SAFE) Team. This incorporated a departmental model, with clear and robust internal leadership, external accountability, and formal processes for identification, review and follow up of at risk patients, as well as protocolised escalation processes.Results: The introduction of the SAFE model has been associated with a continuous reduction in the overall Hospital Standardised Mortality Ratio (HSMR) from 0.71 to 0.54 (periods January to March 2015 vs January to March 2018. In addition, the SAFE model has been associated with a reduction in out of hours mortality (defined as admissions from 1600 to 0800) from 0.98 to 0.38 (periods January to March 2015 vs January to March 2018). This has been accompanied by a qualitative improvement in the quality of care delivered out of hours, and improved satisfaction with working conditions and training delivered out of hours. Due to a drastic reduction in unplanned Resident Medical Officer (RMO) overtime associated with the introduction of the model, implementation was near cost neutral.Conclusion: The introduction of the SAFE model has been associated with improved hospital outcomes, in conjunction with improved medical and nursing staff experiences, at a low marginal cost. This model has scope to be applied to similar tertiary level hospitals, or modified to fit within most hospital structures. A key component to the success of this model’s innovation, is acknowledgement of the importance of after hours care provision to patients, highlighted by the formation of a department of after hours medicine as part of the SAFE model.
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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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".