It is well understood at this point that data plays a huge role in every single decision we make in healthcare. What is still in talks, though, is the best way to utilize and leverage that data. We reached out to our beautiful Healthcare IT Today Community to ask — how are organizations leveraging data analytics to improve charge capture accuracy and financial performance? Below is what they had to share.
Sunil Konda, Chief Product Officer at SYNERGEN Health
For some, charge capture is a top-five challenge and one of the highest-leakage areas in RCM. With analytics, we’re beginning to see a way to close those gaps. The combination of CDI analytics with retrospective charge auditing creates a feedback loop that can capture and flag missed charges before they result in lost revenue. That data is then used to prevent the same error from occurring further upstream in the workflow. This switches RCM from an offensive, reactive charge recovery to a defensive, proactive charge integrity, driving a direct, quantifiable impact on net revenue.
Yousuf J. Ahmad, President and CEO at AssureCare
Organizations are increasingly using analytics not just for retrospective reporting but as a driver of real-time performance improvement across the revenue cycle.
With detailed visibility into denial codes, reasons, and payer-specific trends, teams can identify systemic issues and take corrective action upstream. This might include improving clinical documentation, refining coding practices, or adjusting workflows to prevent recurring issues before they impact claims.
Analytics also enhances collections performance by enabling more targeted prioritization of follow-up and recovery efforts. The result is stronger charge capture accuracy, reduced revenue leakage, and more consistent financial outcomes. Instead of reacting to missed revenue after the fact, organizations can proactively tighten processes and improve overall performance.
Monte Sandler, Chief Operating Officer at WebPT
Data tells you where the system is breaking. When denials, rejections, and unpaid accounts receivable are analyzed, patterns become visible. From there, the root cause can be addressed and resolved in bulk instead of working on claims reactively one at a time. That is how accuracy improves, and the cost to collect is reduced. The organizations that win are the ones that listen to the data and act on it.
Elevsis Delgadillo, SVP, Customer Success at KeenStack
Organizations are using analytics to improve financial performance by tracking core revenue cycle metrics like collections and denials and identifying where breakdowns occur. By tying analytics to specific workflows, they can measure what changes after new processes or technologies are introduced and better understand what is driving performance.
Firoze Lafeer, SVP of Data Engineering at Revecore
Data analytics is helping organizations move from periodic audits to continuous monitoring by identifying where charges are systematically missed at the service line, provider, or facility level before they become lost revenue. By analyzing patterns across large claim populations, revenue cycle teams can pinpoint the root causes of charge capture failures and fix them upstream rather than chasing discrepancies after the fact. For example, if analytics consistently surfaces a payer systematically underpaying a specific DRG or denial pattern tied to incomplete documentation, that intelligence can drive upstream coding and charge entry corrections before the issue scales across thousands of claims.
Deb Jones, Senior Director, Insights Strategy at Tendo
Organizations are increasingly moving beyond retrospective reporting toward real-time and predictive analytics. On the charge capture side, this means identifying gaps as care is being delivered—not weeks later. By connecting clinical documentation, orders, and billing data, analytics can highlight missing or inconsistent charges, ensure alignment with coding requirements, and surface opportunities for more complete capture.
More broadly, advanced analytics are helping organizations understand the root causes of revenue leakage—whether it’s documentation gaps, workflow breakdowns, or payer-specific trends. Instead of looking at isolated metrics, leading organizations are analyzing performance across the entire revenue lifecycle. The shift is from “What happened?” to “What’s about to happen—and how do we intervene?” That’s where analytics starts to drive meaningful financial improvement.
Jake McCarley, CEO at Alluvium Health
Charge capture begins the moment a patient tries to access care — if referrals leak out of network or scheduling breaks down, there’s no charge to capture.
Advanced analytics platforms consolidate fragmented access data across EMRs, call centers, and digital channels to reveal exactly where revenue is being lost: which referral sources consistently leak, which specialties have untapped capacity, and where in the patient journey demographic collection, insurance verification, and prior authorization break down. Surfacing these gaps earlier — and optimizing how they’re resolved — directly improves charge capture accuracy.
AI-powered insights allow operators to identify these issues quickly and take targeted action, transforming access from reactive troubleshooting into strategic, data-driven performance management. And because the underlying model is purpose-built for this use case, the system compounds in value over time — getting smarter with every interaction.
Kevin Coloton, CEO at HURC
What looks like a payer–provider imbalance is increasingly a breakdown in revenue cycle communication. Hospitals can excel at registration and collections, but when the middle cycle fails, the full value is not achieved. This operational layer not only balances utilization review and denials management but also clinical documentation improvement (CDI) and medical coding.
If there are any gaps in documentation, they lead to denials, extended length-of-stay, and unclear payer communication, which create delays, write-offs, and appeals that never should have occurred. Hospitals are spending nearly $18 billion annually reworking claims and a staggering $43 billion trying to collect payments insurers owe for care already delivered, according to the AHA’s 2026 Cost of Care Report. That is why the middle revenue cycle is where CFOs feel pain so acutely.
Partial fixes such as adding more software, hiring more staff, or outsourcing have not solved the problem. What’s emerging instead is a more effective model: integrated, tech-enabled services that combine technology to handle all the operational components of the middle revenue cycle with experienced operators and embed directly into hospital workflows. This approach enables real-time alignment between clinical intent and payer requirements, driving fewer denials, shorter stays, faster post-acute transitions, and meaningful net revenue improvement, without reducing staff.
Ryan Hungate, DDS, MS, Chief Clinical & Strategy Officer at Henry Schein One
Health IT and AI are finally connecting what has historically been a fragmented process, from patient intake all the way through reimbursement. The biggest impact isn’t just faster claims processing; it’s eliminating the manual handoffs and rework that slow everything down.
When you automate eligibility checks, documentation, coding support, and claim submission in a coordinated way, you reduce errors upstream and improve financial performance downstream. But just as important, it allows staff to step away from the screen and focus on the patient.
The organizations seeing the most success aren’t just adding AI; they’re redesigning workflows so that technology handles the administrative burden and people can focus on care, communication, and decision-making where it matters most.
Thomas Shea, Chief Revenue Officer, AI and Patient Affordability Solutions at Doceree
Making sure every service gets billed correctly sounds straightforward, but a surprising amount slips through — a procedure performed but never coded, a visit billed at a lower level than the work supported, a supply used but not logged. Teams would catch these gaps weeks later in an audit, if at all. That’s changing.
The best organizations now run analytics inside the clinical workflow itself, flagging those gaps in real time — while the provider is still in the note, not after the claim has gone out. In my work embedding analytics into HCP workflows, I’ve seen denial rates drop and clean-claim rates climb noticeably when these checks run at the point of documentation, not in a back-office queue. The biggest wins come from the quiet checks running inside the EHR before the note is even signed.
Scott Schrader, President, Provider Healthcare Solutions at Firstsource
Hospitals lose an estimated 1–3% of net revenue to charge capture problems alone, amounting to millions of dollars annually in legitimately earned revenue that was never billed. Leading organizations are closing this gap by deploying NLP-driven mining of unstructured clinical notes to surface missed billable concepts, computer-assisted coding that can reach 90-95%+ accuracy, and concurrent CDI programs that query physicians on documentation gaps before discharge — identifying significant DRG corrections that compound in impact across a facility each month.
The most sophisticated are building unified analytics across the full revenue cycle that trace a denial back to its origin in registration or documentation rather than treating it as an isolated back-end event, because catching a charge capture problem upstream is exponentially cheaper than appealing a denial downstream.
So many great points to consider here! Huge thank you to everyone who took the time out of their day to submit a quote to us! And thank you to all of you for taking the time out of your day to read this article! We could not do this without all of your support.
How do you think organizations are leveraging data analytics to improve charge capture accuracy and financial performance? Let us know over on social media, we’d love to hear from all of you!
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About Dr. Kaushal Kulkarni