BOTTOM LINE UPFRONT
Across the Epic RCM lifecycle – from design and implementation to stabilization and optimization – early decisions and post-go-live drift often leave valuable functionality underutilized. Significant performance and margin improvements can often be unlocked through targeted optimization, governance, and activation of existing capabilities.
Epic is one of the largest capital investments a health system makes. It is also one of the most underutilized.
The average health system uses less than half of the Epic RCM functionality it purchased. It starts during implementation, when build decisions are deprioritized and scope gets cut, then compounds after go-live as configurations drift and staff create workarounds for capabilities that were paid for but never fully activated. The gap between what Epic can do and what it is actually doing widens quietly.
And that gap has a cost. Denied claims, repetitive processes, and recoverable margin accumulate on the wrong side of the ledger every month. The share of providers reporting initial denial rates above 10% has surged from 30% in 2022 to 41% in 2025. Payers are deploying AI to scrutinize claims faster than most RCM teams can respond, and the volume reflects it.
The organizations positioned to keep pace are the ones with a fully built, actively governed Epic environment. The question is how to get there without starting your implementation from scratch.
The go-live trap
The failure mode is hard to see because the system still functions. Revenue Guardian, Claims Manager, and Resolute are licensed but not activated in any meaningful sense. Claims go out, cash comes in, and no one measures what’s being left on the table.
When organizations do close the gap, it can mean 1 to 3% in net revenue, 5 to 15 days of A/R improvement, and cost-to-collect reductions of up to 30%. For most organizations, that recovery has been sitting in the existing system the entire time.
The sequenced path to get there
The methodology below is structured and repeatable, but the execution adapts to each organization’s workflows, priorities, and capacity for change. What it provides is a proven order of operations, applied consistently and tailored to each organization’s constraints:
- Assess and baseline. Module utilization, workflow mapping, charge capture review, KPI benchmarking against peers. The goal is a specific account of where the existing build is producing leakage, what each gap is worth, and which ones to address first. Without that baseline, configuration work produces activity without direction.
- Configure and activate. Dormant Epic functionality gets turned on: billing and claims optimization, eligibility and authorization automation, charging rules and edit build-out. This converts existing investment into working capital. Gains are typically measurable within 60 to 90 days.
- Optimize and monitor. Configuration alone does not sustain performance. Denial root cause dashboards, workqueue priority scoring, and payer-specific edit rules are what shift teams from working every item to working the right ones. That’s where cost-to-collect actually moves. The diagnostic and the remedy need to come from the same team. An insight about denial patterns has limited value if it cannot be translated into a specific build change.
- Scale with Epic AI. Epic’s AI Assistant, Penny, is live. Autonomous coding for emergency and radiology arrives in late 2026. Agent Factory is on the near horizon. The organizations that capture value from these tools will be the ones whose underlying Epic builds are already ready. That readiness comes from the three steps above. Layering AI onto an underbuilt environment produces the same problem as the original go-live: the technology performs only as well as the foundation beneath it.
Practical adoption means starting with the use cases where Epic AI has the clearest ROI (autonomous coding, prior auth, denial prediction), and building the workflow integration and staff enablement around each one before scaling. In a regulated environment, that also means governance: audit trails, documented decision logic, and a clear answer to who is accountable when the model produces the wrong output. Getting that right from the start is what turns AI adoption into measurable business value.
What the right expertise looks like
Closing the gap requires Epic-certified professionals with deep experience across the full Epic RCM module set, working alongside people who understand where the leakage is and how revenue cycle work actually gets done
Most engagements separate those capabilities across vendors. The diagnostic goes to one team, the configuration to another, the analytics to a third. That handoff structure is where insights and actions go to stall. One team across all three is what closes the loop between what the data shows and what the build actually changes.
The margin is already there
The recoverable opportunity in most Epic environments is larger than organizations expect, and it doesn’t require new technology to capture. It requires finishing the work that started at implementation.
Accordion partners with hospitals and health systems across the full Epic RCM lifecycle: design, implementation, stabilization, and optimization. Our team brings operations, analytics, and Epic build expertise into a single engagement, with no handoffs between the diagnosis and the fix.