US Healthcare: 60 Bots, 5,000+ Hours Saved/Month
This case study details how a multi‑state US healthcare organization modernized its Revenue Cycle Management (RCM) operations by deploying 60 production‑grade software robots across eligibility, prior authorization, ERA/EOB posting, denial management, and revenue analytics. The program was designed to relieve chronic backlogs, normalize quality, and free clinical and billing teams to focus on exceptions and patient‑touching work.
Prior to automation, analysts navigated dozens of payer portals and clearinghouses every day, re‑keyed data from PDF/EOBs into ERP and EHR modules, and triaged a high volume of denials that demanded fast, consistent responses. Variability in payer rules and the volume of low‑value repetitive work produced long cycle times and elevatedDSO. With robots handling repeatable tasks and surfacing structured exceptions, the team achieved 5,000+ hours saved per month, denials down 18%, anddays in AR improved by 4.6—all measured on the operations dashboards used by leadership.
Client Context & Pain Points
- Eligibility and prior authorization checks conducted manually across 20+ payer portals and clearinghouses.
- ERA/EOB posting required repetitive extraction from PDFs and scanned images, with frequent formatting drift.
- Denials queue triage suffered from inconsistent codes mapping and variable appeal packet assembly.
- Limited end‑to‑end observability: hard to prove where minutes were lost across intake → billing → collections.
- SLA breaches during seasonal peaks (flu season, staffing gaps) and system outages at external portals.
Solution Architecture
The solution uses an RPA orchestrator for scheduling, credential vaulting, and telemetry; an IDP layer for document classification and field extraction; and lightweight internal APIs to push validated transactions into the EHR/ERP. Bots are permission‑scoped with SSO and employmaker‑checker patterns for risk‑sensitive steps. Observability runs through a central data store powering dashboards for Throughput, First‑Pass Yield, Rework, and Exceptions by root cause.
| Layer | Role | Key Controls |
|---|---|---|
| RPA Orchestrator | Schedules, runs, and monitors 60 bots across RCM steps | Credential vault, audit logs, retry/backoff policies |
| IDP | Classifies ERA/EOB, extracts posting fields and variances | Field confidence thresholds, human‑in‑the‑loop for low confidence |
| EHR/ERP Connectors | Writes clean transactions; reads master data for validation | Schema validation, SSO, role‑based access |
| Observability | Unified telemetry and business KPIs | Immutable logs, metric lineage, privacy safeguards |
Process Flows Automated
- Eligibility & Prior Auth: Intake → validation → portal login → rule checks → capture → post results.
- ERA/EOB Posting: IDP classify → extract → validate against fee schedules → post to ERP → reconcile.
- Denials: Queue pick → code map → evidence gather → appeal packet → submit → track outcomes.
- Refunds & Adjustments: Identify overpayments → initiate refund workflow → compliance checks.
- Reporting & Analytics: Auto‑refresh payer mix, denial trends, AR aging, and productivity metrics.
Implementation Roadmap (12 Weeks)
| Phase | Weeks | Key Deliverables |
|---|---|---|
| Discover & Baseline | 1–2 | Current‑state maps, SLA baseline, risk controls, access provisioning |
| Design & Pilot | 3–6 | IDP training set, unattended runs, exceptions playbook, dashboards |
| Scale & Stabilize | 7–12 | Rollout to 60 bots, runbooks, BAU handover, KPI acceptance |
KPIs & Outcomes
ROI Model (Illustrative)
| Item | Assumption | Value |
|---|---|---|
| Monthly hours automated | 60 bots × 3 hrs/day × 22 days | 3,960 hrs |
| Blended cost/hour | Analyst + QA + overhead | $35 |
| Gross monthly value | Hours × cost/hour | $138,600 |
| Run cost | Licenses, VMs, support | $22,000 |
| Net monthly benefit | Gross − Run | $116,600 |
Controls & Compliance
All robots follow least‑privilege access, rotate credentials via vault, and enforce maker‑checker approvals for actions that impact patient balances. Audit trails capture evidence for each step with tamper‑evident logs. PHI is masked in logs and dashboards; data residency complies with contractual obligations.
“The biggest shift wasn’t just speed—it was confidence that every claim was handled consistently. Our analysts now focus on solving payer‑specific edge cases instead of copy‑pasting all day.”