- A model reads each new ticket's text and fields and predicts its time to resolution and its category, at intake.
- Tickets likely to run long are flagged early, so a lead can assign the right expert before the member or provider calls back.
- Reason codes show which words and fields drove each prediction, so your team can check it.
- Your agents and leads still own every ticket. The model changes the order and the routing, not who decides.
Triage happens by hand, one ticket at a time
Member and provider tickets arrive by portal, email and phone notes. Each one has to be read, categorized and assigned before anyone starts work on it.
When triage is manual, complex tickets sit in the wrong queue and simple ones wait behind them. You find out a ticket was hard only after it has been reopened or escalated.
How support ticket time to resolution works
A regression model predicts hours to close, and a classification model predicts the category and owning team, both trained on your own closed tickets.
Step 1: connect the history
Pull closed tickets with their text, fields, categories, owners and resolution times from the service system you already run.
Step 2: read the text
Natural language processing turns ticket descriptions into features, next to fields such as channel, member type and product.
Step 3: build and compare models
H2O Driverless AI runs automatic feature engineering and ranks candidate models on held-out tickets.
Step 4: score at intake
Each new ticket gets a predicted time to resolution, a category and a suggested owning team.
Step 5: explain and monitor
Interpretability tools show what drove each prediction, and your team watches for model drift when new plans, policies or systems change what tickets say.
Where this fits your service tools
The models sit next to the service system you already run, adding a predicted time, category and owner to each ticket. Tickets often contain member details, so protected health information never has to leave your boundary.
Proof
H2O can be used to predict time to resolution and category for each new ticket, explain each prediction, and help leads route work. No customer result is published for this use case yet.