Article author and evidence
Dr. Kainat Amjad, MBBS
Dr. Kainat Amjad is a medical doctor and practice growth strategist who writes about healthcare operations, revenue cycle workflows, administrative burden, and practical technology adoption. Her work focuses on helping providers improve the business side of care without weakening accountability, accuracy, or reimbursement.
What Should Practice Leaders Know Before Buying More AI Tools?
Disconnected AI creates data silos
When eligibility, coding, and billing tools cannot share data automatically, staff manually re-enter the same information, and every manual entry point is a place where errors and denials start.
Denial rates are climbing with AI adoption
Industry data shows denial rates rising as AI tools have proliferated without the integration behind them, and up to 65% of denied claims never get reworked at all.
Fragmentation is a compliance exposure too
Every manual handoff between disconnected AI tools is an extra, often undocumented place where protected health information moves, multiplying BAA and data-flow obligations.
Fix the workflow, then connect the tools
Map the claim lifecycle, confirm real API integration before signing any contract, and assign one owner accountable for the full chain from intake to payment.
What’s Actually Happening Inside These Practices?
Practices are spending real money on AI right now. Coding assistants, claims submission tools, denial management software, eligibility checkers. Most of it gets bought with good intentions and a clear promise: fewer denials, faster payments, less staff burnout.
Then three months later, the same denials come back. The same claims bounce for the same reasons. The only thing that changed is the invoice from the software vendor.
This isn’t a failure of AI. It’s a failure of connection. When AI tools operate as isolated point solutions instead of a connected system, they don’t fix the revenue cycle. They just move the same broken process through more expensive software, faster.
Saiqa Abbas, an RCM optimization specialist, put it directly in a recent post that captured what a lot of billing teams are quietly experiencing: throwing AI at revenue cycle management does not fix revenue on its own. Practices are buying AI for coding, AI for claims submission, AI for denial management, and AI for eligibility, and the tools look impressive individually. But an AI tool that submits claims faster doesn’t help if the documentation, coding accuracy, or eligibility verification behind that claim was already wrong. It just means bad claims move through the system quicker than before.
Revenue cycle management is a chain. Front desk intake feeds eligibility. Eligibility feeds coding. Coding feeds claims submission. Claims submission feeds accounts receivable and denial management. When one link in that chain breaks and nobody notices because the AI tool sitting at that step has no idea what happened one step earlier, the entire cycle leaks money.
This lines up with what IT leaders inside larger health systems are seeing on the ground. One healthcare IT director, discussing AI integration challenges on a healthcare IT forum, said the biggest surprise wasn’t the AI itself. It was discovering how fragmented the existing infrastructure already was, with so many disparate third-party systems and no real single source of truth for data. Layer AI on top of that kind of environment and you don’t get intelligence. You get faster fragmentation.
Why Do Disconnected AI Tools Create Data Silos?
A data silo happens when two systems that should share information cannot talk to each other automatically. Your practice management system holds the appointment. Your eligibility tool holds the coverage check. Your coding software holds the CPT and ICD-10 assignments. Your billing platform holds the claim. If none of these share data in real time, staff end up manually keying the same patient information into multiple places, and each manual entry point is a place where something can go wrong.
Google’s own AI Overview on this exact topic describes the mechanism plainly: isolated AI tools create double work because staff have to enter the same data into two or more programs, more errors because moving data by hand leads to typos and wrong billing codes, faster claim denials because insurers reject bills the moment information doesn’t match across systems, and slower cash flow because fixing those mismatches after the fact can take weeks or months.
None of this is theoretical. The industry data backs it up with real numbers.
What Is the Real Cost of Disconnection, In Numbers?
Denial rates have been climbing steadily, and the timing lines up almost exactly with the surge in AI tool adoption across healthcare.
Experian Health’s 2025 State of Claims report found that 41% of providers now report more than one in ten of their claims gets denied, up from 30% just three years earlier.
Source: Experian Health, 2025 State of Claims Report, cited in Medical Economics, 2026.
HFMA’s analysis of Kodiak Solutions data shows the initial denial rate climbed to nearly 12% in 2024, and continued upward from there.
Source: HFMA, analysis of Kodiak Solutions data, cited in Medical Economics, 2026.
The part that should worry every practice owner more than the denial itself is what happens after. HFMA reports that up to 65% of denied claims never get reworked at all. That revenue isn’t delayed. It’s gone, written off entirely because staff don’t have the time or the systems to chase it down.
Source: HFMA, cited in Medical Economics, 2026.
A meaningful share of these denials trace back to the exact intake and eligibility gaps that isolated AI tools were supposed to catch. Experian Health’s 2025 data shows that 26% of respondents attribute at least one in ten denials to intake errors such as wrong policy numbers, outdated insurance cards, and missed eligibility rechecks.
Source: Experian Health, 2025, cited in Medical Economics, 2026.
Coding-related denials have followed the same trend. MDaudit data cited by HFMA shows coding-related denials increased 126% over a recent three-year period, a jump that coincides directly with the rise in standalone AI coding tools operating without a connection back to documentation or eligibility data.
Source: MDaudit data, cited by HFMA, in Medical Economics, 2026.
Here’s the part that should reframe how practices think about buying AI. When eligibility verification actually connects to the front end of the workflow in real time, instead of running as an isolated check, Experian Health case data shows practices have cut denial rates by as much as 42%.
Source: Experian Health case data, cited in Medical Economics, 2026.
The gap between adoption and impact is wide right now. A 2025 Experian Health survey found that 67% of healthcare organizations believe AI can improve the claims process, yet only 14% have actually implemented AI tools.
Source: Experian Health, 2025, cited in Medical Economics, 2026.
That 53-point gap is not a technology problem. It’s an integration problem. Practices know AI can help. What’s stalling most of them is that nobody has connected the tools they already have to the workflow those tools are supposed to support.
McKinsey’s research on agentic AI in revenue cycle describes the current state of most health systems bluntly: decades and billions of dollars invested in automation, and most systems still run through a patchwork maze of loosely connected point solutions and IT systems.
Source: McKinsey, “Agentic AI and the Race to a Touchless Revenue Cycle,” 2026.
That patchwork maze is exactly what’s driving the denial numbers above. It’s also exactly what Saiqa Abbas and the healthcare IT director both described from two completely different vantage points, one in RCM strategy, one in IT infrastructure. When two people with different jobs looking at different parts of the same problem land on the same description, it’s worth taking seriously.
What Common Mistakes Do Practices Make When Buying AI?
Buying a tool for every symptom instead of fixing the chain. A denial management AI tool treats the symptom. If the eligibility check upstream was never connected to it, the tool will keep flagging the same category of denial every month with no way to prevent it at the source.
Assuming the vendor demo reflects your actual data environment. Vendor demos run on clean, structured sample data. Your EHR, your clearinghouse, and your practice management system were likely never designed to talk to each other, and a slick demo will not reveal that gap until the tool is live and producing mismatched claims.
Skipping the API question before signing the contract. Practices frequently commit to a tool based on features and price without confirming whether it actually integrates with the EHR and billing platform already in place. Without a documented API connection, integration often means an employee copying data by hand between two screens, which defeats the entire purpose of buying automation.
Treating AI adoption as an IT purchase instead of a workflow redesign. The practices seeing real improvement, according to Abbas, are not just buying tools. They’re fixing the workflow behind the technology first, then layering AI on top of a process that already makes sense.
No ownership of the front desk to AR handoff. When eligibility, coding, and billing report to different managers with no shared accountability for the full claim lifecycle, AI tools inherit that same lack of ownership. Nobody is responsible for noticing when the chain breaks between departments.
What Actually Fixes This?
Map the full chain before buying anything. Walk the actual path a claim takes today: front desk intake, eligibility verification, coding, claims submission, accounts receivable, denial management. Identify where data currently gets re-entered by hand. Those handoff points are where an AI tool will either help enormously or cause new problems, depending on whether it connects to what comes before and after it.
Choose platforms built to share data, not just process it. A unified system where coding, billing, and denial management pull from the same patient and claim record eliminates the manual re-entry that creates errors in the first place. This doesn’t necessarily mean one giant all-in-one platform. It means every tool in the stack needs a real, tested connection to the others.
Confirm the API before the contract, not after. Ask any AI vendor directly how their tool shares data with your EHR and practice management system, and ask to see the integration working with your actual system, not a generic demo environment. If the answer is vague, that’s the answer.
Audit vendors on data flow, not just feature lists. Before signing with any AI tool, get specifics on what happens to data the moment it enters that tool. Where does it go next? Does it update the patient record automatically, or does it sit in the tool waiting for someone to transfer it manually?
Assign one owner for the full claim lifecycle. Someone on the team, whether that’s a billing manager or an outsourced RCM partner, needs visibility into the entire chain from intake through final payment. Fragmented ownership produces fragmented systems, no matter how good the individual tools are.
Fix the workflow before layering on the technology. If documentation, coding accuracy, or eligibility checks are unreliable today, adding AI on top will not fix that. It will automate the mistake and deliver it to the payer faster.
“A patchwork maze of loosely connected point solutions”
McKinsey describes most health systems’ current revenue cycle technology stack this way, after decades and billions of dollars invested in automation. It is the same fragmentation Saiqa Abbas and healthcare IT leaders describe from opposite ends of the organization: RCM strategy on one side, IT infrastructure on the other.
What Compliance Risk Comes From Disconnected AI Tools?
Disconnected AI tools don’t just cost money through denials. They create a compliance exposure that rarely gets discussed until an audit forces the issue.
Every time patient data moves manually between systems because two AI tools don’t share a connection, that data passes through an extra, often undocumented step. A staff member copying eligibility results from one screen into a coding tool, or pasting claim details from a denial management dashboard into a billing platform, is creating a data trail that most practices have never mapped for HIPAA purposes.
This matters more as AI vendors multiply inside a single practice. Each new tool is a new place where protected health information gets stored, processed, or transmitted, and each one needs its own signed business associate agreement and its own documented data flow. A practice running five disconnected AI tools has five separate points where a breach, a misconfigured integration, or an over-permissioned account can expose patient data. A connected system with fewer, properly integrated touchpoints is not just more efficient. It’s also easier to actually secure and audit.
Before adding another AI tool to the stack, it’s worth asking who has access to the data once it enters that system, how long it’s retained, and whether the vendor’s BAA actually covers the way your practice plans to use the tool. These are questions that get skipped when a purchase decision is driven entirely by the promise of faster claims.
How Do You Measure Whether Integration Is Actually Working?
Buying a connected system or fixing a broken handoff doesn’t mean the fix is working. Practices need a few concrete numbers to track before and after, otherwise “it feels better” is the only signal available, and that’s not enough to justify the cost of a new platform or a workflow change to leadership.
Track denial rate by category, not just overall. A falling overall denial rate can hide a coding-denial category that’s still climbing. Break denials into eligibility, coding, authorization, and documentation buckets. If a fix targeted eligibility integration specifically, that’s the bucket that should move first.
Track first-pass clean claim rate. This is the percentage of claims that get paid without any rework, resubmission, or appeal. It’s a more honest measure of whether the front end is actually connected to the back end than the denial rate alone, because it captures friction that never becomes a formal denial but still slows payment.
Track manual touches per claim. Pick ten claims at random each month and count how many times a human had to re-enter, correct, or manually transfer information for each one. This number should trend toward zero as integration improves. If it’s flat six months after buying a new AI tool, the tool didn’t fix the actual problem.
Track time from denial to resolution. HFMA’s finding that up to 65% of denied claims never get reworked usually traces back to this number being too high. If a denial takes weeks to even get assigned to someone, connected systems that flag the denial immediately and route it to the right person can cut that time significantly, and that’s a number leadership actually cares about since it maps directly to cash flow.
None of these require expensive new reporting infrastructure. Most practice management and billing systems already capture this data, it just needs to be pulled into one place and reviewed monthly instead of left buried in individual system dashboards that nobody cross-references.
What Are Practices Asking About Disconnected AI Tools and Denials?
Why do denials keep happening even after we invested in AI tools?
Most AI RCM tools solve one narrow task well, like flagging a missing modifier or predicting a likely denial. But if that tool has no connection to the eligibility check or documentation that came before it, it’s reacting to a problem it can’t actually prevent. The denial pattern repeats because the root cause, disconnected data, was never addressed.
Is the answer to buy one giant, all-in-one AI platform instead?
Not necessarily. A single connected system can work, but so can several specialized tools as long as they genuinely integrate through documented APIs and share the same underlying patient and claim data. The goal is connection, not consolidation for its own sake.
How do we know if our AI tools are actually integrated or just running in parallel?
Trace one claim from intake to payment and count how many times someone manually re-enters the same information into a different screen. If that number is higher than zero, the tools are running in parallel, not as a connected system.
What is a realistic first step for a practice that already has three or four disconnected AI tools in place?
Start by mapping the current claim lifecycle end to end and identifying exactly where handoffs break down. Fixing the two or three worst handoff points usually delivers more denial reduction than buying another point solution.
Does connecting eligibility verification to the front end actually reduce denials?
Experian Health case data shows practices that connected real-time eligibility verification into the front-end workflow, instead of running it as an isolated check, cut denial rates by as much as 42%. The reduction comes from catching coverage problems before the claim is created, not from the eligibility tool itself.
What is the difference between an AI tool that prevents denials and one that just processes them faster?
A prevention tool is connected upstream to eligibility and documentation data, so it can flag a problem before the claim is submitted. A processing tool works after the fact, sorting or prioritizing denials that already happened. Both can be useful, but only the connected version reduces how often denials occur in the first place.
What Are Practice Leaders Asking About Disconnected AI Tools and Denials?
Practice leaders want fewer denials without buying yet another disconnected tool. These answers focus on data silos, integration, ownership, and compliance exposure.
What is a data silo in revenue cycle management?
A data silo happens when two systems that should share information, such as eligibility, coding, and billing platforms, cannot talk to each other automatically. Staff end up manually keying the same patient information into multiple places, and each manual entry point is a place where something can go wrong.
Why is revenue cycle management described as a chain?
Front desk intake feeds eligibility. Eligibility feeds coding. Coding feeds claims submission. Claims submission feeds accounts receivable and denial management. When one link breaks and the AI tool sitting at that step has no visibility into what happened one step earlier, the entire cycle leaks money.
What should a practice check before buying an AI revenue cycle tool?
Confirm how the tool shares data with the EHR and practice management system already in place, and ask to see that integration working with real data rather than a generic demo. Without a documented API connection, integration often just means an employee copying data by hand between two screens.
Why do so many denied claims never get reworked?
HFMA reports that up to 65% of denied claims never get reworked at all. Staff do not have the time or the connected systems to trace what went wrong, so the revenue is written off entirely instead of being recovered.
What compliance risk comes from disconnected AI tools?
Every manual data transfer between two AI tools creates an extra, often undocumented step where protected health information moves. Each additional AI vendor is a new place where data is stored or processed, and each one needs its own signed business associate agreement and documented data flow.
Should a practice fix its workflow or its technology first?
Workflow first. If documentation, coding accuracy, or eligibility checks are unreliable today, adding AI on top will not fix that. It will automate the mistake and deliver it to the payer faster.
What Are Healthcare Operations Professionals Saying About Disconnected AI Tools?
Practice leaders are not only asking which AI tool to buy. People actually running RCM strategy and IT infrastructure are independently describing the same fragmentation problem from opposite ends of the organization. The answers below connect those concerns to what actually reduces denials.
Does buying more AI tools for revenue cycle management actually reduce denials?
Not on its own, according to RCM optimization specialist Saiqa Abbas. An AI tool can submit claims faster, but if the documentation, coding accuracy, or eligibility verification behind that claim was already wrong, the tool just submits bad claims faster. The practices seeing real improvement are fixing the workflow behind the technology, not just adding more of it.
Source: Saiqa S. Abbas, LinkedInWhat do IT leaders inside larger health systems say is the biggest surprise with AI integration?
One healthcare IT director, discussing AI integration on a healthcare IT community forum, said the biggest surprise was not the AI itself but discovering how fragmented the existing infrastructure already was, with many disparate third-party systems and no single source of truth for data. Layering AI on top of that kind of environment creates faster fragmentation rather than intelligence.
Source: r/healthIT discussionWhich Sources Support This Revenue Cycle Integration Guide?
Ready to Stop Paying for AI Tools That Don’t Talk to Each Other?
Map the claim lifecycle, the systems it touches, the handoff points where data breaks down, and who owns the chain end to end before adding another point solution. Practices working with Staffingly can try the full connected workflow through a Two-Week Risk-Free Pilot, with no setup fees and no security deposits required to get started.