V3Qtor AG/Healthcare/Personalised medicine
Treatment Optimizer
Pharmacogenomic profile, medical history and lifestyle analysed to rank treatments by predicted effectiveness and side-effect risk, with alternatives presented to the clinician who decides.
Per patienttreatment matched to the evidence
- Practice
- Decision & Risk Sciences
- Sector
- Healthcare, personalised medicine
- Systems involved
- AI Governance
- Why it matters
- The number is an engagement target1.
What it changes in the P&L
Cost per outcome. The right treatment first is one fewer cycle, one fewer admission.
How the first weeks go
- Fit the data you haveDistributions, regressions, time series: we start with what the system records and say where the data is too thin to decide.
- Put a confidence interval on the decisionA rate, a risk score, a treatment match: each comes with its uncertainty and with the rule that says when a person decides instead.
- Calibrate on a rhythmBack-tests run weekly, drift is reported before anyone asks, and the model version is logged with every output.
What stays
Treatment Optimizer stays in the ward’s rhythm once it runs. The weekly review reads from it and its evidence files into the audit trail. Hand-over comes when the number has held for a quarter.
Talk to us about Treatment Optimizer.
Tell us where it would run and what it would have to change. We will come back with what we would look at first, what the first quarter produces, and what it would take.
Notes and sources
- 1Illustrative. Anonymised engagement scenarios; figures indicate modeled or targeted impact and are not audited outcomes.