What stops hospital clinical staff from following protocols? An analysis of the incidence and factors behind the failure of bedside clinical staff to activate the rapid response system in a multi-campus Australian metropolitan healthcare service
Bibliographic record
Abstract
OBJECTIVE: To explore the causes of failure to activate the rapid response system (RRS). The organisation has a recognised incidence of staff failing to act when confronted with a deteriorating patient and leading to adverse outcomes. DESIGN: A multi-method study using the following: a point prevalence survey to determine the incidence of abnormal simple bedside observations and activation of the rapid response team by clinical staff; a prospective audit of all patients experiencing a cardiac arrest, unplanned intensive care unit admission or death over an 8-week period; structured interviews of staff to explore cognitive and sociocultural barriers to activating the RRS. SETTING: Southern Health is a comprehensive healthcare network with 570 adult in-patient beds across four metropolitan teaching hospitals in the south-eastern sector of Melbourne. MEASUREMENTS: Frequency of physiological instability and outcomes within the in-patient hospital population. Qualitative data from staff interviews were thematically coded. RESULTS: The incidence of physiological instability in the acute adult population was 4.04%. Nearly half of these patients (42%) did not receive an appropriate clinical response from the staff, despite most (69.2%) recognising their patient met physiological criteria for activating the RRS, and being 'quite', or 'very' concerned about their patient (75.8%). Structured interviews with 91 staff members identified predominantly sociocultural reasons for failure to activate the RRS. CONCLUSIONS: Despite an organisational commitment to the RRS, clinical staff act on local cultural rules within the clinical environment that are usually not explicit. Better understanding of these informal rules may lead to more appropriate activation of the RRS.
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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 | 0.010 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".