Implementation of INEWS to Improve Patient Outcomes in a Community Hospital
Bibliographic record
Abstract
Abstract Background It is imperative to identify at risk older patients with clinical deterioration as soon as possible. The Irish National Early Warning System (INEWS)1 is used to detect clinical deterioration at an early stage based on each patient's vital sign records. It can then initiate a communication mechanism between nurses and doctors to enable early patient care. Data on the effectiveness of the INEWS in patients admitted to community rehabilitation wards, the barriers and difficulties encountered during implementation, are still scarce, despite the development and implementation in acute settings. There are not enough standardised tool available to identify patients' deterioration for community hospitals. Methods This pilot quality improvement programme was conducted in a community hospital's rehabilitation section. Data were gathered seven weeks after the deployment of INEWS and fourteen weeks before. Issues were studied to determine the underlying cause, created a driver diagram to comprehend the factors that lead to unexpected outcomes, educated and gathered feedback from the clinical care teams involved, measured adherence to the INEWS during the six-week pilot project implementation period, and assessed results by looking back at the deteriorating patient data and INEWS compliance using Plan, Do, Study, Act cycles. Results During the pre-INEWS introduction phase (14 weeks), there were 11.9 events per week (7.8 events with infection or septic workups, 9.6 events with onsite management, 1.0 acute care transfers, 0.28 per week unexpected deaths). In the post-INEWS piloting phase (6 weeks), there were 3.6 events per week (2.5 infection or septic workup, 1.8 onsite management and 1.1 acute care transfers per week with no unexpected deaths). Conclusion Conclusion Early detection and rapid management through the INEWS can enhance patient care; however, increased stakeholder engagement in escalation response may enhance patient outcome in resource limited community setting. Reference https://www2.healthservice.hse.ie/organisation/qps-improvement/deteriorating-patient-improvement-programme-partnership-dpipp/
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 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".