The Helpful or Hindering Effects of In-Hospital Patient Monitor Alarms on Nurses
Bibliographic record
Abstract
Patient monitors generate alarms to signal changes in vital signs. Some research suggests these alarms can improve patient safety. Other reports caution that these systems generate false alarms and create nursing workflow interruptions. These findings require contextualization by qualitatively investigating the lived experiences of nurses working with these monitors. Research into the dynamics involved in nursing responses to alarms can provide insights for monitor development and implementation. This study's purposes were (1) to describe the frequency of alarms generated by patient monitors and nursing responses and (2) to report nurses' explanations of the impact of alarms on workflow and strategies for responding to alarms. Forty-nine hours of observations and 14 interviews were conducted at a Canadian medical center. Four hundred forty-six monitor alarms (1 every 6.59 minutes) were observed. Of these, 70% had no immediate response from nurses. Furthermore, 34 red alarms (potential life-threatening) were observed, with 41% having no immediate response. Nurses reported feeling overloaded by alarm frequency. They described learning to interpret alarm data and developing workaround strategies (eg, ignoring alarms). Paradoxically, alarms prompted nurses to regularly consider and interpret patient information. We suggest the interpretive work associated with workarounds may hold benefits mitigating the potential harms of ignoring alarms.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".