Accelerate your claims processes so staff can work smarter, not harder
AI is changing the healthcare revenue cycle, but solutions vary widely. Leaders need systems that scale, manage risk, and deliver outcomes-without adding complexity to already stretched teams.
Ensemble's end-to-end agentic AI approach is built on two petabytes of data, 80 million annual claims, and the hands-on expertise of 15,000 associates who train and supervise the models inside daily workflows. The result: automation that thinks in outcomes, not just tasks.
What makes agentic orchestration different from traditional RCM automation
- Goal-driven vs. task-driven: Agents work across the claim lifecycle-from eligibility to collections-to achieve a defined outcome, not just complete a single step.
- Coordinated agents: Multiple specialized agents plan, execute, and hand off work. They escalate edge cases to people and learn from the resolution.
- Continuous learning in production: Feedback from 15,000 associates and patterns across 80 million claims sharpen decisions over time.
- Built-in governance: Guardrails, audit trails, and payer-specific rules help keep performance consistent and compliant.
Real use cases you can ship this quarter
- Eligibility and benefits checks: Verify coverage, extract benefit details, and flag issues before claim creation.
- Prior authorization triage: Identify PA needs, assemble documentation, and route to the right queue with justification.
- Denial prediction and prevention: Predict denial risk at pre-bill, suggest fixes, and boost first-pass yield.
- Claim status and follow-up: Monitor payer portals, interpret responses, and trigger next actions automatically.
- Underpayment detection: Compare expected vs. actual reimbursements and generate appeal packages.
- Patient financial engagement: Personalize outreach, payment plans, and timing to improve collections without overburdening staff.
Human + machine: how work actually gets done
Agents handle the repeatable work; people handle judgment, exceptions, and relationships. Associates review low-confidence items, provide corrections, and the system learns from each decision. This tight loop improves accuracy and protects outcomes while keeping teams focused on the highest-value work.
What leadership should measure
- Claim touch rate and cost to collect
- Days in A/R and payer turnaround time
- Clean claim rate and first-pass yield
- Denial rate, avoidable write-offs, and net collection rate
- Productivity per FTE and case-mix-adjusted outcomes
Implementation path that respects your team's time
- Pick 1-2 high-friction workflows with clear KPIs and enough volume.
- Baseline your metrics, then pilot with human-in-the-loop controls.
- Set confidence thresholds, routing rules, and audit reporting up front.
- Integrate with your EHR, clearinghouse, and payer portals using APIs or bridge automations.
- Expand by outcome: add adjacent steps once ROI and quality targets hold.
- Communicate wins early and often to build trust across clinical and revenue teams.
Why scale matters here
With two petabytes of data and tens of millions of claims flowing through the system each year, models see payer behavior at depth and at speed. That pattern recognition, paired with deep operational expertise across hundreds of hospitals, is why agentic orchestration can move the needle on cost, speed, and accuracy.
Hear practical guidance and real results from Jim Gaffney, Chief Strategy Officer at Ensemble, as he shares how agentic AI orchestration-paired with disciplined operations-drives measurable ROI across the revenue cycle.
Further reading: See an overview of revenue cycle principles from HFMA here.
If your team is building AI capability across roles, explore role-based training options here.
Topics: Analytics, Artificial Intelligence, Interoperability
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