Most healthcare revenue cycles are failing, and they usually fail for three connected reasons. The team is a set of functional silos rather than a team aligned around shared revenue cycle objectives. The culture blames the biller instead of solving problems. And the systems and controls are not in place to reveal where the trouble actually comes from, which is revenue cycle complexity, and 71% of that complexity is generated inside the practice, external to billing. Fix those three, and outcomes follow. Ignore them, and no amount of effort at the billing desk will save you.
Start with what business you are actually in
If your practice depends on third-party payers, you are a revenue cycle business that performs physical therapy for revenue. "Revenue cycle" means everything, every task and function and role, that impacts if, when, and how much you get paid for your provider's time. There is no certainty that you will be paid in full, no certainty of when, and no fixed conversion of time into dollars. That uncertainty is the nature of the business, and it is why every revenue cycle shares the same objectives: convert provider time into the highest potential revenue, collect all of that potential revenue, collect it in the fewest calendar days, and do all of it as cost-effectively as possible. If you want the full model, I laid it out in why most revenue cycles are broken and in the revenue cycle complexity deep dive. This piece is about where it goes wrong.
Failure one: the team is a set of silos, not a team
In most practices, the revenue cycle functions run as islands. Credentialing, verification of benefits, authorization management, patient registration, clinical documentation, and billing are each done by different people, sometimes several people per function, and each function operates without understanding how it affects the others.
Does the clinician writing documentation understand how it lands on the accounts receivable denial specialist weeks later? Does the person registering a new patient understand how each data field they collect becomes a denial risk and a time inflator downstream in billing? Almost never. There is no dataset, no KPIs, no shared information connecting these functions around the common objectives. So not only do the functions not understand each other, they do not understand how they affect the objectives they are all supposed to serve. That is the single biggest structural problem in a revenue cycle, and it is also the biggest opportunity, because every problem is an opportunity. A revenue cycle only works when it operates as one team, with every role defined in the context of the shared objectives and its cross-functional impact.
Failure two: a culture of blame instead of problem-solving
When AR is backlogged and cash is delayed, the finger-pointing starts, and it almost always points in one of two directions: the biller, or the payer. It is as if everyone has only two fingers. And both are wrong 94% of the time. Billers account for about 6% of complexity in the revenue cycle, and even that is an unfair number, because the more complex you make a simple task, the more human errors rise.
Blaming the biller for that is like blaming the fire crew when fires are popping up faster than any crew could ever put them out. There are only so many people you can send. The culture that actually works is collaborative problem-solving: every member of the team understands the others' roles, and the team works together to find where the opportunities to improve are, implement better practices, and prevent the things that keep the objectives out of reach. That culture does not happen by accident. It has to be designed, and it has to be fed by data that everyone can see, because you cannot collaborate around a problem you cannot measure.
Failure three: systems and controls that cannot see the problem
Every business runs on team, culture, and systems and controls. In a revenue cycle, "controls" means the information: the data and intelligence that tell you whether things are actually working, and that catch the wheels before they come off. Most practices do not have them.
That shows up in two ways. First, workflows live in people's heads instead of being documented, mapped, and measured, so the day your best biller wins the lottery and never comes back, the knowledge base for that workflow walks out the door with them. That is fatal to stability and scalability. Second, without data, complexity stays invisible, and you cannot manage what you cannot measure. Our entire data platform is built to be the controls layer, telling the story through data of how every function is really performing. As Peter Drucker put it, nothing is less productive than making more efficient what should not be done at all. You cannot see what should not be done at all until you measure it.
What silos, blame, and weak controls all feed: complexity
Underneath all three failures is one engine: revenue cycle complexity, and the way it drives billing time demand. Billing time demand is the combination of your claim volume and the complexity inside that volume, and complexity, not volume, is the driver. I have 1,000-visit-a-month clients whose billing takes more time than a 10,000-visit-a-month client, because the smaller practice carries far more complexity per claim.
The numbers are stark. Grouping clients by how many claims carry a complexity issue, our groups run from about 4% to 8% to 15% to 30% to over 60%, each level roughly double the last. Comparing the 60% group to the 4% group, total billing time per visit rises about 375%, so a two-FTE billing operation becomes an eight-FTE operation for the same work. Almost all of that lands in accounts receivable, where time rises about 650%, and inside AR the worst of it is denial management, the hardest and most expensive skill in billing, where time rises about 1,500%, or 15 times. And where does the complexity come from? After eight years of data, 71% is generated inside the practice but external to billing, with patient registration the single largest source, 10% comes from payers, 13% from systems, and 6% from billing itself. The full breakdown is in the revenue cycle complexity pillar. The industry has spent years calling payer friction "administrative burden," but the data says the administrative burden is mostly self-inflicted.
Why fixed time supply turns complexity into failure
If billing time demand is a variable, and complexity makes it wildly variable, then billing time supply has to be a variable too. Only then can the outcome hold steady. But there are three time-supply models, and two of them are fixed. An in-house team is a fixed supply: two FTEs is 80 hours a week, and that is what you have. An outsourced company charging a fixed percentage of revenue is also a fixed supply, because they convert that fee into a labor budget and a set number of people who will not spend more time than the budget allows. The third model, variable time supply, is rare and is the one we run. Even a variable model has limits, because at extreme complexity the cost of the time required stops being sustainable, which is exactly why reducing the complexity is the only durable answer.
Structure makes it worse in a specific way. A small operation runs linear-process billing: one biller does claim submission first, then payment posting, then patient balances and statements, then the month-end close, and only then, with whatever time is left, works the AR. AR sits at the end of the line. When complexity inflates every earlier step, there is little or no time left for AR, so the biller cherry-picks the easy claims and lets the complex, time-consuming ones age. That is precisely how AR inflates. A larger operation can move to parallel process, splitting the work by function so the AR team works AR regardless of where posting stands. The signal that supply and demand are out of balance is your ADO Score: your actual days in AR against the Target ADO your payer mix should produce. We want it between 95% and 105%. Once it climbs past about 115%, the extra time required just to bring it back down starts to grow exponentially.
The fix is a two-pronged attack
There is a quote I like from Alan Perlis: fools ignore complexity, pragmatists suffer through it, some can avoid it, but geniuses remove it. I am not trying to call myself a genius, but this is what the work has to be: removing complexity.
So the attack is two-pronged. Reduce and prevent the complexity at its source, which means aligning the whole team around reducing it and using data to break the silos, and at the same time match time supply as closely as you can to the demand that remains. Reducing complexity does more than save FTEs and cost. It lets you define and standardize workflows, which is also what makes them ready for automation. Automation is coming to these functions, and within a few years most of this administrative work will run through it. But automation only works if it is built on a complete understanding of where the problems actually are; the ones that ignore the human workflow fail. That is why the order matters: understand the problem, reduce the complexity, then automate what remains. Better data leads to better process, better process leads to better outcomes, and better outcomes lead to a better business.
Find out where your revenue cycle is failing
You cannot fix what you cannot see, and most owners have never had their revenue cycle complexity quantified or their silos mapped. Every RCS relationship begins with a revenue cycle assessment that shows where your complexity is generated, what it is costing you in time, and where the opportunities to fix it are, shared with you at no cost and no risk.
Request Your Free Revenue Cycle Assessment
Listen to the full episode, "Why Revenue Cycles Fail," on the Rehabbing Your Revenue Cycle podcast: https://rehabbingyourrevenuecycle.com/episodes/how-internal-complexity-breaks-revenue-for-healthcare-leaders/.
Frequently asked questions
Why do revenue cycles fail?
Usually for three connected reasons: the team runs as functional silos instead of a unified team aligned around revenue cycle objectives; the culture blames the biller or the payer instead of solving problems; and the systems and controls are not in place to reveal where the problem comes from, which is revenue cycle complexity. Underneath all three, complexity inflates billing time demand beyond the available time supply.
Is my AR problem a billing problem?
Almost never. When AR is backlogged, the two most common targets are the biller and the payer, and both are wrong 94% of the time. Billers account for about 6% of complexity; 71% is generated inside the practice but external to billing, 10% comes from payers, and 13% from systems.
What are functional silos in a revenue cycle?
Functional silos are what you get when credentialing, verification of benefits, authorization, registration, documentation, and billing each operate as islands, with no shared data showing how one function's work affects the others or the common objectives. Silos are the norm, and they are the primary reason revenue cycles fail to optimize.
Why does fixed time supply cause AR to back up?
Because billing time demand is variable and fixed time supply is not. In a small linear-process billing operation, AR sits at the end of the workflow, after claim submission, posting, patient balances, and the month-end close. When complexity inflates the earlier steps, little time is left for AR, so complex claims age and AR inflates.
How do you fix a failing revenue cycle?
With a two-pronged approach: reduce and prevent complexity at its source by aligning the team and using data to break silos, and match time supply to the demand that remains. Reducing complexity lowers billing time demand, improves cash flow and cost, and standardizes workflows, which also makes them ready for automation.