- A critical care model scores each active patient continuously from vital signs, monitor readings and chart data, and flags a change in status for review.
- Each alert carries the signals that drove it, such as heart rhythm, oxygen saturation or temperature, so the nurse can check it at the bedside.
- The same data can surface patients likely to need a longer or more costly stay, so case management can plan ahead.
- H2O makes no clinical efficacy claim: alerts change where your care team starts, not who decides.
Give one care team a way to see who needs a look first
Your critical care unit produces a constant stream of monitor readings, vital signs and chart entries. The important change is in there, but so are the routine readings, and each alarm that turns out to be nothing costs a nurse's attention.
Your team asks the same questions for every patient, shift after shift: has the patient's condition deteriorated, is the heart rhythm abnormal, is oxygen saturation below target or is temperature rising? A single monitor alarm answers one reading at a time. What your team needs is a view that combines readings over time for each patient and says who needs a look first.
How critical care management works
Critical care management is trained on your own unit's history: time series models learn how status changes develop, and classification models score each patient for review.
Step 1: connect the history
Pull vital signs, monitor readings, labs and chart events for past critical care stays from the EMR and monitoring systems you already run. The model sits on top of those systems; it does not replace them.
Step 2: build and compare models
H2O Driverless AI builds time series and classification models with automatic feature engineering and stacked ensembles.
Step 3: add signal detection
Anomaly detection flags readings that break from a patient's own pattern, not only from a fixed threshold.
Step 4: explain each alert
Reason codes show which signals drove each score, so the nurse can check them against the patient.
Step 5: route in shadow mode first
Run alerts beside your current monitoring before anyone acts on them. Your unit's protocol decides who is notified and what the next step is, and you retrain as patient mix and practice change.
Where the same data helps case management
The chart and stay history used for alerts can also train a model that scores which patients are likely to need a longer or more costly stay, so case management can plan discharge and resources earlier.