AI Agent

Revenue Cycle Management AI Agent

Reduce claim denials by 50% and improve collections by 20%

Manual RCM causes 15% denial rates and slow collections. AI achieves 95% clean claim rate with 30% faster payment.

Benefits

Stop Losing Revenue to Denials and Delays

Manual revenue cycle management causes 15% claim denial rates costing millions in lost revenue and rework. Slow collections extend DSO to 45+ days. Staff can't keep pace with claim volume and follow-up. This agent achieves 95% clean claim rate with 30% faster payment.

50%

Denial reduction

95%

Clean claim rate

20%

Revenue improvement

Agentic Flows

Complete RCM Automation

Automated workflow: claim scrubbing with edit validation, electronic submission with attachment handling, ERA/EOB processing and posting, denial management with root cause analysis, AR follow-up with payer and patient outreach, payment posting and reconciliation.

How it works

AI-Powered Revenue Cycle Optimization

The agent scrubs claims pre-submission to catch errors causing denials automatically, submits claims electronically to all payers with proper documentation, tracks claim status and payment with proactive follow-up and appeals, manages patient collections with automated statements and payment plans.

Self-Learning Agents

The agent improves with every task, adapting to outcomes, applying feedback, and self-correcting using Constitutional AI.

Upto 98% Accuracy

As a result of constant feedback loops, Beam AI Agents refine their approach with every cycle, leading to 98% accuracy across flows.

Smart Model Switching

We call it ModelMesh. Each agent selects the right model for the task, balancing speed, accuracy, and cost in real time.

استخراج رقم الطلب، معلومات البائع، معلومات التسليم، وتفاصيل الدفع من نماذج الطلبات المرفوعة.

مكتمل

ID-0E48

استخراج رقم الطلب، معلومات البائع، معلومات التسليم، وتفاصيل الدفع من نماذج الطلبات المرفوعة.

مكتمل

ID-0E48

استخراج رقم الطلب، معلومات البائع، معلومات التسليم، وتفاصيل الدفع من نماذج الطلبات المرفوعة.

مكتمل

ID-0E48

Self-Learning Agents

The agent improves with every task, adapting to outcomes, applying feedback, and self-correcting using Constitutional AI.

Upto 98% Accuracy

As a result of constant feedback loops, Beam AI Agents refine their approach with every cycle, leading to 98% accuracy across flows.

Smart Model Switching

We call it ModelMesh. Each agent selects the right model for the task, balancing speed, accuracy, and cost in real time.

استخراج رقم الطلب، معلومات البائع، معلومات التسليم، وتفاصيل الدفع من نماذج الطلبات المرفوعة.

مكتمل

ID-0E48

استخراج رقم الطلب، معلومات البائع، معلومات التسليم، وتفاصيل الدفع من نماذج الطلبات المرفوعة.

مكتمل

ID-0E48

استخراج رقم الطلب، معلومات البائع، معلومات التسليم، وتفاصيل الدفع من نماذج الطلبات المرفوعة.

مكتمل

ID-0E48

Integrations

Works with Practice Management Systems

Direct integration with Epic, Cerner, athenahealth, Kareo, and AdvancedMD for claims and AR. Connects to clearinghouses for electronic submission. Integrates with payment processors for patient collections and reconciliation.

Implementation

Optimizing RCM in 3 Weeks

Connect your PM system and clearinghouse. Configure scrubbing rules and follow-up workflows. Most practices achieve 95% clean claim rate within 3 weeks of deployment.

Step 1

Agent Discovery

Conduct workshop(s) with relevant stakeholders to identify and prioritize use cases and map the requirements

Step 1

Agent Discovery

Conduct workshop(s) with relevant stakeholders to identify and prioritize use cases and map the requirements

Step 2

Agent Setup

Develop and launch your first agent with basic logic and integration (process scoping and recording, test dataset of 30-50 examples, baseline agent running and testing with target output mapping)

Step 2

Agent Setup

Develop and launch your first agent with basic logic and integration (process scoping and recording, test dataset of 30-50 examples, baseline agent running and testing with target output mapping)

Step 3

Agent Training

Test performance and gather feedback from process users (agent optimization up to 80% against expected output, variable-level accuracy measurement and optimization, integrations setup, and feedback API interfacing)

Step 3

Agent Training

Test performance and gather feedback from process users (agent optimization up to 80% against expected output, variable-level accuracy measurement and optimization, integrations setup, and feedback API interfacing)

Step 4

Agent Live

Extend to more workflows and client teams (live and continuous monitoring, human-in-the loop interfacing, node auto-tuning for automated prompt enhancements, >90% accuracy improvement, weekly business logic improvements)

Step 4

Agent Live

Extend to more workflows and client teams (live and continuous monitoring, human-in-the loop interfacing, node auto-tuning for automated prompt enhancements, >90% accuracy improvement, weekly business logic improvements)

Key Features

Revenue Cycle Features

Claims Scrubbing

Validates claims against payer edits before submission. Fixes errors that cause denials automatically.

Denial Prevention

Analyzes denial patterns and implements preventive measures. Reduces denials by 50% systematically.

Automated Follow-Up

Tracks unpaid claims and follows up automatically. Prioritizes by amount and aging for maximum recovery.

Demo

See Revenue Cycle Management in Action

Watch the agent manage 1,000 claims: scrub and submit, process 950 payments, identify 50 denials, work AR, generate patient statements—achieving $1.2M collections in 30 days.