Early-out self-pay AI lifts collections 29% at a 900-provider physician group
A national physician group was placing early-out self-pay accounts with agencies at 90 days because in-house outreach couldn't keep pace. pronix.ai deployed an agentic early-out program on voice, SMS and email with propensity-to-pay scoring, self-service payment plans and financial-assistance screening — collections lifted 29% and cost-to-collect dropped 38%.
- Client
- Multi-specialty physician group, 900 providers across 6 states
- Industry
- Healthcare Providers
- Platform
- Amazon Connect · Kore.ai Agent Platform · Athenahealth · Salesforce Health Cloud
*Representative outcome; results vary by client, scope and platform configuration.
The challenge
Self-pay balances after insurance had grown to 22% of AR and the in-house early-out team could only touch each account twice before agency placement. Patients wanted digital-first, self-serve options; regulators demanded strict TCPA, healthcare-privacy and state-collection compliance on every contact.
Our approach
Propensity-to-pay segmentation
A model scored each account for likelihood to self-resolve, best channel and best offer — high-propensity accounts went digital-first, hardship signals were routed to counselors before any collections contact.
Compliant multi-channel cadence
Voice, SMS and email orchestrated with consent, quiet-hours and frequency caps enforced by policy — every touch logged with script version and consent state for audit.
Self-service resolution
Patients could view a plain-English statement, split into a payment plan up to 24 months, apply for financial assistance or pay in full — all without agent contact, all inside a healthcare-safe portal.
Counselor copilot for exceptions
When patients called in, a counselor copilot surfaced account context, payer status, plan eligibility and prior offers — average handle time on collections calls fell 34%.
“We are collecting more, from more patients, with fewer complaints — and we placed 40% fewer accounts with agencies last quarter.”
Illustrative case study. Scenarios, metrics, quotes and client details are representative composites based on Pronix engagements and industry benchmarks unless a named client is shown with written consent. Outcomes vary by client, scope, data quality and platform configuration. Nothing on this page is a guarantee, warranty or professional advice. See our Terms of Use for the full disclaimer.
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