One of the most persistent frustrations I encountered while overseeing RCM teams was not a lack of data. It was too much of it in the wrong format, spread across systems that did not talk to each other, requiring hours of manual work to pull into a report that was already a week old by the time anyone read it.
The result was a team that was reactive by necessity. You could not get ahead of a problem if you did not know it existed until it had already been compounding for 30 days. Decisions got made on instinct and experience rather than on current, reliable numbers, and when things went wrong, the investigation into why took longer than it should have.
Operational intelligence is the practice of changing that dynamic. It means having the right metrics in front of the right people at the right time, and building the workflows to act on what those metrics tell you.
The gap between average and top-performing revenue cycles is significant, and Kodiak Solutions' 2024 KPI benchmarking data shows that it shows up across nearly every metric.[1] Top performers are not just doing the same things slightly better. They have cleaner visibility into their own operations, and they use that visibility to catch problems earlier and resolve them faster.
The Metrics That Actually Tell You Something
The challenge with RCM reporting is that there are dozens of metrics you could track, and tracking all of them produces noise rather than insight. The ones that consistently tell the clearest story about the health of a revenue cycle are a handful of core indicators, each of which surfaces a different category of problem.
Days in Accounts Receivable (A/R)
This is the foundational measure of how efficiently your practice is converting rendered services into collected revenue. A number trending upward is an early warning signal, not just a financial metric. It reflects something upstream, whether that is claim submission delays, payer processing slowdowns, or denial backlogs building before they become visible in other reports.[2]
First-Pass Resolution Rate (FPRR)
This measures the percentage of claims paid on first submission without any rework. It is one of the clearest indicators of upstream process quality. A first-pass rate below 90% means a meaningful share of your team's time is being spent on work that should have been done correctly the first time, and the causes are almost always traceable to specific points in the front-end workflow.[3]
A/R Over 90 Days
Receivables that have aged past 90 days are significantly harder and more expensive to collect, and many will ultimately be written off. Monitoring this percentage and breaking it down by payer type surfaces patterns that are not visible in aggregate numbers. A spike in 90-day A/R from a specific payer is often a signal of a payer behavior change that your team needs to investigate and respond to.[2]
Net Collection Rate (NCR)
The net collection rate measures the percentage of collectible revenue that is actually being collected, after accounting for contractual adjustments. A rate below 90% typically reflects either inadequate denial management, lost charge capture, or both. This metric is more meaningful than gross collection rate because it focuses on what was actually recoverable, not what was billed.[3]
"Unexplained variations in healthcare are common. They do not have to be in your revenue cycle."
Kodiak Solutions, Revenue Cycle KPI Benchmarking Report, 2025 [1]
Turning Data Into Action
Having the right metrics is only useful if there is a system for reviewing them and acting on what they show. This is the part that most practices underinvest in. Reports get generated but not reviewed on a consistent schedule, or they get reviewed without clear ownership of what happens when a number falls outside its target range.
Operational intelligence works when it is embedded in a regular rhythm. A weekly review of key denial trends, a monthly deep-dive on A/R aging, a quarterly look at first-pass resolution rates by payer, and a clear escalation path when something moves in the wrong direction. The cadence matters almost as much as the metrics themselves, because it is what converts data from a passive observation into an active management tool.
It also matters that the reporting is current. One of the most common barriers I saw was a practice relying on reports that were being run manually, once a month, by one person who had the know-how to pull them. That kind of reporting structure cannot support proactive decision-making. By the time the report is in someone's hands, the problem it reveals is already several weeks old.
A Real-World Scenario
Consider a specialty practice that had been running at a 45-day A/R average for over a year without understanding why it was holding consistently above the HFMA benchmark of 30 to 40 days. Their reporting was monthly, manually compiled, and broken out only at the aggregate level. Nothing in their current view was specific enough to identify the source of the problem.
When they shifted to a weekly dashboard with A/R broken out by payer, aging bucket, and denial reason category, a pattern became visible within the first two weeks. One commercial payer was accounting for a disproportionate share of claims sitting in the 61-to-90-day bucket, and the denial reason was clustering around a single issue: requests for additional documentation on a specific procedure type.
That payer had quietly updated their documentation requirements several months earlier without notifying providers. The fix was a targeted update to the documentation workflow for that procedure type and a follow-up appeal process for the backlog of affected claims. Within 60 days, A/R days had dropped by nine days overall, with most of the improvement attributable to that single payer issue they had not been able to see before.
Where to Start This Week
Building operational intelligence does not require a new platform or a major technology investment to get started. It starts with getting your current data organized and visible in a way that supports weekly decision-making.
Identify which of the four core metrics above you are currently tracking and how often. For any metric that is being reviewed monthly or less frequently, ask whether that cadence is actually giving your team enough lead time to respond to problems before they compound.
Check whether your current A/R reporting is broken out by payer. Aggregate A/R numbers hide patterns. A practice running at 38 days overall might have one payer group sitting at 65 days, pulling the average up and masking a problem that has a specific, solvable cause.
Define ownership for each metric. Every number on your dashboard should have a person who is responsible for reviewing it and a process for escalating it when it falls outside its target range. Metrics without ownership tend to become decorative rather than functional.
Schedule a standing weekly review with the right people in the room. This does not need to be long. A 30-minute weekly review of denial trends, A/R movement, and first-pass resolution rate is enough to catch most emerging problems before they become expensive ones. The consistency is what makes it valuable.
Operational intelligence is not about having the most sophisticated analytics platform. It is about building a practice-wide habit of looking at the right numbers on a regular cadence and connecting what those numbers show to specific actions. That habit, sustained over time, is what separates practices that are managing their revenue cycle from practices that are being managed by it.
In Issue #04, we will look at how workflow design specifically affects accounts receivable performance, and the specific workflow changes that tend to produce the most measurable improvement in A/R days.
- Kodiak Solutions (May 2025). Revenue Cycle KPI Benchmarking Quarterly Report. Kodiak Solutions is a healthcare analytics company that analyzes claims and revenue cycle data from more than 2,100 hospitals and 300,000 physicians across the U.S. Their quarterly benchmarking reports are among the most comprehensive and widely cited performance data sources in the industry. kodiaksolutions.io →
- MD Clarity (2025). RCM Benchmarks: How Close Does Your MSO Come to These Ideal Measurements? MD Clarity is an RCM software and analytics company specializing in managed service organizations and physician groups. This article consolidates benchmark data from HFMA, MGMA, and Kodiak Solutions into a practical reference for evaluating revenue cycle performance across key KPIs. mdclarity.com →
- Human Medical Billing (August 2025). Essential Medical Billing KPIs for 2025. Human Medical Billing is a U.S.-based billing and revenue cycle services provider with over two decades of operational experience. This article provides definitions, benchmarks, and improvement strategies for the core KPIs that drive revenue cycle performance in 2025. humanmedicalbilling.com →
- Experian Health (September 2025). State of Claims 2025. Experian Health's annual State of Claims survey is one of the most comprehensive industry benchmarks available for revenue cycle management, drawing on responses from 250 healthcare professionals across provider organizations of varying sizes. experian.com →
- HFMA (December 2024). Hospital Financial and Revenue Cycle Benchmarks. The Healthcare Financial Management Association is the leading professional organization for healthcare finance executives, with over 100,000 members. Their benchmarking data for Days in A/R, first-pass resolution rate, and related metrics is considered the standard reference for the industry. hfma.org →
Ready to see your revenue cycle clearly?
The ROI platform gives you the visibility and reporting tools to move from reactive management to proactive decision-making.