The biggest revenue leaks often occur between technologies, workflows, and teams, where no single tool owns the outcome
Health systems have spent the past decade investing in revenue cycle technology. Coding engines, denial predictors, eligibility tools, and workflow automation. Technology is deeper and more capable than it has ever been. Yet revenue still leaks through the seams, and for finance leaders watching margin absorb the difference, that leakage is not a rounding error. It is capital that should already be on the balance sheet.
A 2025 Black Book Research study found more than 79 percent of participating organizations actively modernizing their revenue cycle infrastructure to improve data flow. The same research found the problem persisting anyway and located it in the seams: the handoffs between EHRs, patient accounting, patient access, and collections systems. Leaders traced the failures to disconnected infrastructure and departments working in isolation, not to any single tool falling short.
The problem is not a lack of technology. It is what happens at the handoffs between the parts of the revenue cycle.
The leak is not where you think
When revenue slips, the instinct is often to blame the tool. The coding engine missed the code. The denial model let one through. So, the organization buys a better engine or a more capable model, and the underlying problem persists.
Often, the bigger leak is not inside the tool. It sits between tools, workflows, and teams.
A coding engine does its job and hands off to a billing system built on different assumptions. An eligibility check clears, but its output never reaches the team working the denial it could have prevented. Each tool performs its task. The revenue disappears in the handoff none of them own.
This is why revenue integrity cannot be solved in one function at a time. What happens upstream shapes what happens downstream, and a failure that surfaces in one part of the revenue cycle often began somewhere else.
AI accelerates whatever you point it at
The current wave of AI makes this more important, not less.
Automation is an accelerant. Point it at a disciplined process and it can compound the gains. Point it at a fragmented one and it amplifies the fragmentation.
The Peterson Health Technology Institute made the point plainly in an April 2026 analysis of administrative AI. PHTI concluded that when AI is deployed on top of flawed administrative workflows, data complexity, and incentive structures, it can exacerbate the underlying problems. In prior authorization, for example, AI can reduce costs for individual organizations while failing to reduce costs across the healthcare system.
The lesson is not that AI or automation does not work. It is that technology cannot compensate indefinitely for the insufficient processes underneath it.
Denials expose the system
Denials make the problem visible because their root causes rarely stay neatly contained within denial management.
The American Hospital Association estimates hospitals spent nearly $18 billion in 2025 overturning claims denials. More telling, 70% of denied claims were eventually paid, but only after multiple costly reviews.
In financial terms, that is process friction. Revenue that ultimately should be collected gets caught in documentation requests, authorization requirements, payer rules, appeals, and other handoffs before reaching its destination.
A better denial tool can help identify and manage the results. Preventing avoidable denials requires understanding and connecting the processes that produce them.
The tool was never the strategy
Abandoning automation and running the revenue cycle by hand is not a serious proposition at the volume modern health systems operate at. The argument is narrower and more useful: technology performs best within the operating model that encompasses it.
That means process discipline must precede scale, even when process redesign and technology deployment happen at the same time. Organizations do not need to perfect every workflow before they automate it. But they do need to understand the workflow, define ownership, establish reliable handoffs, and know where judgment belongs before asking technology to execute it thousands of times.
Under pressure to demonstrate a quick return on technology investments, organizations can move to scale before determining whether the underlying process is stable enough to support it.
Otherwise, automation can scale the same inconsistency the organization was trying to eliminate.
Revenue integrity is an operating model, not a product
Revenue integrity is not a product you install. It isn't a technology you can buy either. It is an operating model you run: connected workflows, disciplined processes, reliable data, clear accountability, and expert judgment placed where the handoffs require it.
Technology carries the volume. The operating model determines whether that volume becomes collected revenue or faster leakage.
The organizations positioned to get more from their technology treat individual tools as components of a larger system rather than solutions in a box. The question for healthcare finance leaders is not simply which automation tool to buy next. It is whether the operating model around those tools can translate their capabilities into financial performance.
Finding the answer starts with understanding the processes already in place, not prescribing to another technology. Access Healthcare starts with the operating model: how work moves, where handoffs break, who owns each step, and where technology or human expertise can improve performance. That process-first approach helps healthcare organizations strengthen what needs to work before applying automation at scale.
And that leaves the question every CEO and CFO weighing their next technology investment should be asking: if the operating model needs human judgment to connect technology, process, and context, where exactly does that judgment belong, and where does human involvement create friction instead?
We'll explore that question in next week's blog: where human-in-the-loop improves performance, and where it gets in the way.
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