The AI that learns
from every hospital.
Built exclusively for critical clinical environments. It learns the behavior of each equipment and warns before the problem happens. No threshold to configure, no rules to write.
A fixed threshold does not protect a hospital. Adaptive AI does.
Manually configured rules fail when the load changes, the shift changes, the seasonality changes. Each equipment has a unique behavior. Only AI trained in the clinical context can tell a real anomaly from noise.
The old systems had so many false alarms that the team started to ignore them. With DROME, when the alert arrives, we know it is real. And it always arrives with time to act.
Three capabilities. Zero configuration.
There is no threshold to tune, no rule to write and no AI specialist to hire. The system learns on its own and warns when something is wrong.
Recognizes normal
The AI learns how each equipment behaves, hour by hour, day by day. When something departs from the pattern, it notices before the alarm sounds.
Sees the future
It does not wait for the limit to be crossed. It projects where the temperature is heading and warns while there is still time to act, with time and probability.
Explains why
Along with the alert, the system points to the most probable cause. The head pharmacist (RP) arrives on site knowing what to look for.
Continuously improves
Every labeled event feeds the learning. The more the system operates, the more accurate it becomes for that specific equipment.
Clinical AI. Not generic.
Anomaly detection
Specific baseline per equipment and shift. Automatically adapts to load changes. Drastically reduces false alarms.
- Individual baseline per equipment
- Adapts to seasonality changes
- 82% reduction in false alarms
Prediction with ETA
Each prediction comes with an ETA, probability and confidence interval. It is not a binary alarm, it is a quantified estimate.
- ETA with confidence interval
- Breach probability
- Calculated urgency level
Explainability
For each prediction, the system explains which variables contributed most to the alert. The head pharmacist (RP) understands why.
- Feature importance per prediction
- Variable contribution chart
- Auditable report
Per-equipment learning
Each piece of equipment has its own model, trained on its own readings. Every event and every recorded justification feeds the learning.
- Individual model per equipment
- Events and justifications on record
- Continuous retraining
No configuration
The model learns the pattern of each equipment automatically. There is no threshold to configure, no rules to define. It detects peaks from day one.
- Zero initial configuration
- Learns from historical data
- Improves with each event
Multichannel integration
Predictions notify all subscribers at the same time: push in the DROME app (iOS, Android, web), WhatsApp, SMS, voice call and email.
- Push in the DROME app
- WhatsApp, SMS, voice call and email
- Webhook for your own systems
Auditable predictions, a complete evidence trail.
Each prediction is stored with a timestamp, the variables used, the probability and the action taken. ANVISA and ONA auditors find not just the alarm history, but the history of predictions and decisions.
What teams ask most.
Direct answers for head pharmacists (RP), technical directors and hospital IT teams.
Want to see the Artificial Intelligence module in action?
Book a demo with our team and see live predictions in your own environment.