OpenNashPrepared for Vincent Miceli

Vincent Miceli / VP, AI and Automation / Point C

We did the homework
on Point C.

Vincent - you have already built practical healthcare AI. We found three Point C workflows where AI automation can absorb hiring pressure and free your team for higher-value work.

You built Benefit Chatbot Charlie with real member data
Your call-center work cut training and handle time
Point C is hiring around plan build, claims, stop loss, and EDI
Where it comes fromestimate
Plan-build packets18-26 hrs/mo
Claims packet prep24-38 hrs/mo
Stop-loss data cleanup18-26 hrs/mo
Three specific painsS.02

Three places we would start.

Pick one workflow. We automate the prep work at no charge for 14 days and show whether it can delay a hire, reduce rework, or move people to higher-value queues.

Pain 01 / Plan config

Plan setup is carrying hire-level pressure.

Problem

Point C is recruiting around plan building, configuration, testing, and auto-adjudication quality.

Solution

We automate the prep: plan-change packets, test cases, rule diffs, and exception notes before a reviewer opens the queue.

Pain 02 / Claims

Claims examiners should not hunt for context.

Problem

The Claims Examiner role names missing information, appeals, subrogation, coding checks, and stop-loss terms.

Solution

We prepare the claim packet, flag missing inputs, summarize plan context, and let examiners spend time on decisions.

Pain 03 / Stop loss and EDI

Stop-loss and EDI handoffs leak time.

Problem

Public roles name data scrubbing, carrier coordination, network reporting, Azure portals, CRM, Argus EDI, and X12 flows.

Solution

We clean incoming data, prepare handoffs, update trackers, and route exceptions before they become another status meeting.

Vincent, give us 30 minutes.

Bring the ugliest claims, plan-build, or EDI queue. We will make it worth your time: automation map, hire-pressure math, and a 14-day no-charge start.