A new approach to alarm management: mitigating failure-prone systems
Bibliographic record
Abstract
Alarm management that effectively reduces alarm fatigue and improves patient safety has yet to be convincingly demonstrated. The leaders of our newly constructed children’s hospital envisioned and created a hospital department dedicated to tackling this daunting task. The Clinical Logistics Center (CLC) is the hospital’s hub where all of its monitoring technology is integrated and tracked twenty-four hours a day, seven days a week by trained paramedics. Redundancy has been added to the alarm management process through automatic escalation of alarms from bedside staff to CLC staff in a timely manner. The paramedic alerting the bedside staff to true alarms based on good signal quality and confirmed by direct visual confirmation of the patient through bedside cameras distinguishes true alarms from nuisance/false alarms in real time. Communication between CLC and bedside staff occurs primarily via smartphone texts to avoid disruption of clinical activities. The paramedics also continuously monitor physiologic variables for early indicators of clinical deterioration, which leads to early interventions through mechanisms such as rapid response team activation. Hands-free voice communication via room intercoms facilitates CLC logistical support of the bedside staff during acute clinical crises/resuscitations. Standard work is maintained through protocol-driven process steps and serial training of both bedside and CLC staff. This innovative approach to prioritize alarms for the bedside staff is a promising solution to improving alarm management.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".