Quick Summary
A multispecialty hospital’s claims process was manual, and that manual workflow was producing processing delays and errors. Bacancy Technology implemented medical claims automation combining UiPath for claims processing with HL7 for healthcare data exchange, and data validation and submission were streamlined through automation without human intervention. Claims processing time fell by 65% and claim denials fell by 40%. This Insight covers what was manual, what we implemented, which technology contributed what, and why those two numbers moved.
Introduction
The hospital’s claims process ran on manual effort, and it was producing two things nobody wants in a revenue cycle: delays and errors.
Those two problems behave differently. A delay costs time in a predictable way. An error costs time unpredictably, because it doesn’t surface at submission. It surfaces later, as a denial, and by then the claim has to be reopened, corrected, and resubmitted. The work gets done twice and the reimbursement arrives late.
For a multispecialty hospital, both effects scale with claim volume across departments. Adding staff to a manual claims process doesn’t remove either one. It just distributes the same repetitive work across more people while the cause stays where it is.
That’s what we were brought in to change. We implemented medical claims automation, combining UiPath for the claims processing work with HL7 for healthcare data exchange, and data validation and submission were streamlined through automation.
This Insight covers what we implemented, which technology contributed what, and what measurably shifted afterward.
What Was Slowing Down the Medical Claims Process?
Two problems sat inside the same manual workflow, and they had different causes. One was a timing problem: claims moved at the speed of the people handling them, so reimbursement waited on staff availability rather than on the work each claim required. The other was a quality problem, with manual handling introducing errors that reached payers. A single fix rarely addresses both, because speed comes from removing waiting and quality comes from removing variance.
Manual Claims Processing Was Creating Delays
The claims workflow was manual, and processing delays followed from that. Manual handling puts a person between every claim and the next step it needs, so the time claims spend waiting is time the hospital isn’t being reimbursed for work it has already done. In a multispecialty setting the effect is amplified, because claim volume arrives from many departments at once while processing capacity stays fixed at however many people are available that day.
The size of that cost is easy to underestimate, because two very different numbers sit inside it. The effort of preparing a single claim is measured in minutes. The elapsed time from claim ready to claim submitted is measured in days. Everything between those two figures is queueing, with the claim sitting in someone’s list waiting to be reached, then sitting again before the next step. Most of what the hospital was losing wasn’t work. It was waiting.
Claims Errors Were Contributing to Denials
Manual validation is inconsistent by nature: a person checking their fortieth claim of the day isn’t checking it the way they checked their fourth. The rules don’t change, but attention does.
That inconsistency decides where errors got caught, and where an error is caught determines what it costs. A claim rejected inside the hospital costs minutes. A claim denied by a payer costs weeks of delayed revenue plus the labor of reworking it, and the feedback arrives far too late to correct whatever produced it. In practice the payer had become the hospital’s validation layer, because the payer’s checks never have a bad afternoon.
How We Implemented Medical Claims Automation
Three components went in, and Bacancy Technology sequenced them deliberately rather than in parallel . The data exchange had to be working before we automated any checks, because a validation rule can only act on a field it can locate. The execution had to be running reliably before we took the supervising person out of it. Only then did we let validation and submission run without human intervention. Had we gone the other way and automated submission first, the hospital would have been sending flawed claims faster than before. Getting that order right is most of what made the rest work.
| Component | What it handled | Why it mattered |
|---|
| UiPath | Claims processing | Executes defined sequences through existing systems, without a person per claim |
| HL7 | Healthcare data exchange | Delivers claim data in a structured form that automated checks can act on |
| Automated validation and submission | Both steps, without human intervention | Removes the manual dependency, which is what both results trace back to |
We Used UiPath to Automate Claims Processing
The first decision was whether to automate the existing claims workflow or rebuild it in new software. We chose RPA, and the reason was what the hospital could absorb rather than what was technically preferable. Claims ran through systems that couldn’t be replaced without pausing the revenue cycle, and most offered no clean integration path. UiPath works through the same interfaces staff already use, so we could change how claims moved without changing what they moved through.
That choice set a boundary we then had to hold:
- What we scoped in. The claims-processing steps where the same inputs always produce the same correct action: retrieving information, moving it into place, applying a format, advancing the claim.
- What stayed with people. Anything needing interpretation, including unusual claims, ambiguous documentation, and payer responses that require a judgment call.
Pushing past that line is how automation starts making decisions it can’t make well, and those failures are silent, because the software has no way of knowing it got one wrong.
We Used HL7 to Support Healthcare Data Exchange
Claim data didn’t originate in the claims process. It came from the systems holding registration, encounter, and service information, which meant the automation was only ever going to be as reliable as what arrived from them. We used HL7 rather than building direct connections into each source system, for two reasons:
- The checks depended on it. Automated validation can check a field it can locate with certainty, and can’t check information it has to interpret out of free text. Structured data arriving in the first place was the precondition for everything we planned to validate.
- Direct connections don’t survive change. An integration built around one system’s particular behaviour breaks when that system is upgraded or replaced, which in a hospital happens on someone else’s schedule, usually while claims are still going out.
So we treated the exchange layer as part of the automation scope rather than as groundwork before it. How much of the workflow we could safely automate was set by what the data made checkable, and that ceiling gets decided in the healthcare interoperability services layer, before any automation is scoped.
We Extended Medical Claims Automation to Validation and Submission
Validation and submission are two steps inside the longer claim lifecycle that claims management software covers, running from claim creation through scrubbing, submission, adjudication, and payment posting. We automated both, and two decisions shaped how:
- Together, not separately. Automating submission alone would have accelerated the existing error rate. Automating validation alone would have produced cleaner claims still waiting behind a person. Either one on its own moves a single number and leaves the other where it was.
- Fully, not partially. A step where software prepares the work and a person still starts it, reviews it, and releases it is faster than manual, but it keeps the queue and it keeps the variance, because both are attached to the person rather than to the task.
Running validation and submission without human intervention is what removed them.
Claims Moving at the Speed of Whoever Is Handling Them?
Bacancy Technology’s healthcare automation team builds the RPA, data exchange, and validation layers that let claims move without a person on every step.
What Changed After We Implemented Automated Claims Processing?
Two numbers came out of this, and they aren’t two versions of the same improvement. One changed when the hospital got paid. The other changed how often it did the same work twice.
Claims Processing Time Fell by 65%
Faster claims processing means the reimbursement clock starts sooner, and because the improvement applies across the hospital’s whole claim volume rather than to individual claims, it shifts cash flow timing without any change in payer behavior or contract terms.
That last part is worth sitting with. Most levers a hospital can pull on reimbursement timing require negotiating with someone: contract terms, payer relationships, clearinghouse arrangements. This one didn’t. It improved the timing of money the hospital was always going to receive, using only changes made inside its own four walls.
Claim Denials Fell by 40%
Denials cost more than they appear to. A denied claim is delayed revenue plus duplicated labor, since it has to be investigated, corrected, and resubmitted. Some are never successfully reworked, and that revenue is written off entirely, which is why denial rates are one of the numbers revenue cycle management software is judged on.
A 40% reduction means a substantially smaller share of claims re-entering the process after submission. That compounds in a useful direction: less rework means more billing team capacity for the complex claims that genuinely need experienced attention, rather than for claims that failed on something a check could have caught.
It’s also the number worth watching after go-live. Processing time improves immediately and holds as long as the automation runs. Denial rates track claim quality, which drifts as payer requirement
Conclusion
The hospital’s claims process was manual, and that manual handling was producing delays and errors. What changed was the dependency: UiPath took over the claims processing work, HL7 gave the workflow structured healthcare data to operate on, and data validation and submission were streamlined through automation without human intervention. The outcome was a 65% reduction in claims processing time and a 40% reduction in claim denials. What’s worth carrying forward from this medical claims automation project is where those results came from. Not from making manual claims work faster, but from removing the manual dependency from steps that didn’t need it.
Frequently Asked Questions (FAQs)
We compare two numbers before we scope anything. How long it takes someone to prepare one claim, and how long that claim takes to go out once it’s ready. When we see minutes against days, we know the hospital is paying for queueing, and that’s what we remove. When those two numbers sit close together, we say so, because the bottleneck is somewhere automation won’t reach.
Not properly, so we fix your data exchange first and automate second. Automated checks can validate a field they can locate, not information buried in free text. Run automation on messy data and the system works confidently while getting things wrong, which costs you the denial reduction you were paying for.
No. Bacancy Technology didn’t replace anything we rarely need to. RPA works through the same screens your staff already use, so everything underneath stays as it is at a fraction of what a replacement costs. The catch is upkeep, because when a system gets upgraded the automation can break. If you’re already planning to replace a core system, tell us early. Automating around it first means building it twice.
It depends on how many claim steps are in scope and how much of your data already moves on a standard. The part clients tend to under-scope isn’t the build. It’s the stretch where we run automated checks alongside your manual ones until we can prove they agree. We won’t cut that short, because going live with rules nobody verified is how these projects fail.
