Why don't hospital staff activate the rapid response system (RRS)? How frequently is it needed and can the process be improved?
Bibliographic record
Abstract
BACKGROUND: The rapid response system (RRS) is a process of accessing help for health professionals when a patient under their care becomes severely ill. Recent studies and meta-analyses show a reduction in cardiac arrests by a one-third in hospitals that have introduced a rapid response team, although the effect on overall hospital mortality is less clear. It has been suggested that the difficulty in establishing the benefit of the RRS has been due to implementation difficulties and a reluctance of clinical staff to call for additional help. This assertion is supported by the observation that patients continue to have poor outcomes in our institution despite an established RRS being available. In many of these cases, the patient is often unstable for many hours or days without help being sought. These poor outcomes are often discovered in an ad hoc fashion, and the real numbers of patients who may benefit from the RRS is currently unknown. This study has been designed to answer three key questions to improve the RRS: estimate the scope of the problem in terms of numbers of patients requiring activation of the RRS; determine cognitive and socio-cultural barriers to calling the Rapid Response Team; and design and implement solutions to address the effectiveness of the RRS. METHODS: The extent of the problem will be addressed by establishing the incidence of patients who meet abnormal physiological criteria, as determined from a point prevalence investigation conducted across four hospitals. Follow-up review will determine if these patients subsequently require intensive care unit or critical care intervention. This study will be grounded in both cognitive and socio-cultural theoretical frameworks. The cognitive model of situation awareness will be used to determine psychological barriers to RRS activation, and socio-cultural models of interprofessional practice will be triangulated to inform further investigation. A multi-modal approach will be taken using reviews of clinical notes, structured interviews, and focus groups. Interventions will be designed using a human factors analysis approach. Ongoing surveillance of adverse outcomes and surveys of the safety climate in the clinical areas piloting the interventions will occur before and after implementation.
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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 | 0.056 | 0.271 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".