A Model of Clinical Alarm Errors in Hospital
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although there has been much attention paid recently to clinical alarms, research has primarily focused on particular aspects of the clinical alarm problem, such as how to reduce nuisance alarms. This paper takes a broad view of clinical alarms and develops a model of errors in alarm handling and how they affect patients directly. Based on reports in the literature, I estimate that alarms that should sound by current standards do not sound about 9% of the time. Additionally, about 3% of alarms that are clinically significant are ignored, either intentionally or because they were inaudible. However, these errors produce a very low rate of reported alarm-related deaths and other adverse effects (on the order of a couple adverse effects per 10 million alarm errors). While it is not yet possible to estimate the probabilities of clinical alarms having an adverse impact on patients other than the patient whose alarm is sounding, such indirect adverse effects likely occur at a low level as a result of disruption of staff workflow, creation of a noisy hospital environment, and contribution to communication difficulties. Consideration of alarms should include not only the patient connected to the device that is sounding, but also the impact of the alarm on other patients in the vicinity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it