AI aimed at false alarms and alarm fatigue on cardiac monitors — predicting critical arrhythmias in the coronary care unit — a clinical trial (ClinicalTrials.gov)
An observational study applying AI to continuous ECG data from a coronary care unit to predict life-threatening arrhythmias 30 minutes and one day ahead. Reducing alarm fatigue through better detection is part of the aim.
Trial overview (primary data)
- StatusRecruiting
- ConditionsCardiac Arrhythmias, Acute Myocardial Infarction (AMI), Ventricular Tachycardia (VT), Ventricular Fibrillation
- SponsorCatharina Ziekenhuis Eindhoven
- Target enrollment3,000 participants
- Period2023-01-01 〜 2029-04-01
Key points
- A retrospective observational study (target 3,000) applying AI to coronary care unit ECG to predict life-threatening arrhythmias 30 minutes and one day ahead.
- Short-term prediction is intended for timely intervention; long-term prediction for safe ward transfer or earlier discharge.
- Raising sensitivity multiplies false alarms, creating a structural alarm fatigue problem that burdens nurses and disturbs patients.
- Of the 803 AI-related clinical trials this site holds as of 2026-09-02, only 2 mention alarm fatigue.
- Both detection and prediction are objects of evaluation; effectiveness has not been established.
1The side effect of an alarm that rings too often
After a myocardial infarction, life-threatening arrhythmias such as ventricular tachycardia and ventricular fibrillation remain a risk, which is why European Society of Cardiology guidance calls for 48 hours of monitoring. The ECG monitor carries that duty and sounds an alarm when it catches something abnormal. A structural dilemma sits here.
The higher the sensitivity set to avoid missing anything, the more alarms fire on nothing. Alarms that never stop add to nursing workload, dull response over time into what is called alarm fatigue, and undermine patient rest. A safety mechanism ends up eroding safety in another form.
2Predicting on two horizons
| Horizon | What the prediction targets | Intended use |
|---|---|---|
| Short term (30 minutes) | Signs that risk of ventricular tachycardia or fibrillation is rising | Intervene while the team still has time |
| Long term (one day) | Whether risk can be judged low for the day ahead | Safe transfer to a lower-complexity ward, earlier discharge |
| Better detection | Fewer false negative alarms | Reduced alarm fatigue |
What stands out is that the AI is not confined to warning about danger. The 30-minute horizon buys time for intervention; the one-day horizon supports a judgment that a patient no longer needs the coronary care unit. It points in two directions at once — tightening surveillance, and releasing people from it.
With intensive care capacity limited, being able to justify who can be let go carries the same practical weight as finding who needs watching.
3Validating on data that already exists
The study is designed as retrospective observation. Rather than trying a new treatment, it evaluates model performance using continuous ECG accumulated in the monitoring system together with admission records. Coronary care units record ECG without pause, and most of it is never revisited if nothing goes wrong. Of the 803 AI-related clinical trials this site holds as of 2026-09-02, only 2 mention alarm fatigue.
Reusing monitoring data to take the burden on staff itself as the object of study is not yet a widespread idea.
4What has to hold for better accuracy to matter
In evaluating models that predict arrhythmia from continuous ECG, a hit rate alone gives the ward nothing to act on. Alarm fatigue does not lift unless false alarms fall, and monitoring loses its point if misses rise. Placing both detection and prediction among the primary objects of evaluation reflects a recognition that only when both hold at once does the model become usable in practice.
What is described here is the objective and hypothesis of the study; the effectiveness of AI prediction has not been established.
Why it matters
Intensive care monitoring generates data in volume that is never revisited when nothing goes wrong — a textbook case of data left dormant. Reusing it to cut false alarms and justify transfer decisions is a useful example of demonstrating the value of medical AI on axes other than diagnostic accuracy.
FAQ
What is alarm fatigue?
Does the study involve a new treatment?
Sources (primary)
Source: ClinicalTrials.gov (U.S. NIH/NLM, public domain). This site does not provide medical advice. Verify the latest and exact details with the official source. This site is not endorsed or certified by the NIH/NLM.
- ClinicalTrials.gov (study record, original)
- NCT ID: NCT07792694