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Why Healthcare Workflow Automation Is Necessary in 2026
Discover why healthcare workflow automation is vital in 2026 to enhance efficiency, reduce errors, and improve patient outcomes.
Is your practice protected from underpayments? It should be. In 2023 alone, hospitals absorbed roughly $130 billion in underpayments from Medicare and Medicaid alone. This has come to be known as a “silent” drain on revenue because, as common as they are, underpayments are often overlooked. The problem isn’t new but it’s quickly getting worse. Studies show that underpayment shortfalls grew by an average of 14% annually between 2019 and 2023.
Underpayments are situations where a provider is reimbursed for less than the expected or contracted amount for services by an insurance company or other payer. These are not denials. The practice receives a partial payment. That makes it more difficult to detect the error within a routine medical billing workflow. Another reason underpayments are often overlooked is that they are commonly small payment variance discrepancies. These discrepancies, however, can add up over time to become substantial losses.
Margins are tightening in the healthcare industry, and every dollar must be protected. Underpayment recovery has become an essential function of effective revenue cycle management. Because of this, automated revenue cycle tools are vital to prevent revenue leakage and maintain financial stability.
Underpaid claims are very different from clinical denials. When a claim is denied, the provider is notified and given reasons for the denial. The provider can then make corrections and re-submit. This is costly and time-consuming, but often results in full payment. Underpayments go through as normal payments and are often overlooked.
Payer reimbursement methods, fee schedules, bundled payments, and contract-specific rules complicate payment calculations, further concealing inadequate payments. It isn’t necessarily as sinister as it may sound at first. Many underpayments are mistakes resulting from missed contract updates and outdated payer contracts. Whatever the reason, they can be costly and difficult to catch.
Without a strong, automated detection system, recurring underpayment issues can quietly erode cash flow. Small percentage losses can, over time, become very expensive. The frustrating thing is that these discrepancies are hiding in plain sight and creating significant revenue leakage.
What causes these payment discrepancies? Let’s take a look at these errors on both the payment and billing sides of the transaction.
Insurance companies sometimes pay the wrong amount because their systems apply outdated fee schedule updates or incorrect contract rates. Even when a claim is processed normally, the reimbursement may not match what the provider contract requires.
Claims may be processed with services bundled incorrectly, codes reduced to lower-paying versions, or modifiers misunderstood. These processing mistakes can lower the payment amount even when the original claim was coded correctly.
A payer may approve a prior authorization or agree to certain payment terms in payer contracts but then fail to apply those terms when the claim is processed, resulting in a lower reimbursement than the provider was promised.
Insurance plans are becoming more complex, and payers frequently update their rules and reimbursement policies. These constant changes increase the risk of payer issues and make it harder for providers to track correct payment amounts.
Many revenue cycle teams are short-staffed while handling large volumes of claims. With so much work to process, staff often do not have enough time to manually review payments and catch underpaid claims.
Some organizations still rely on basic billing systems that cannot automatically flag payment differences. Without automation or machine learning, it becomes difficult to identify discrepancies across thousands of claims.
When services are not recorded completely or charges are entered late, the claim may not include everything that was done for the patient. This can lower the amount the provider is allowed to bill and reduce the final reimbursement.
If codes are entered incorrectly, or modifiers are left off, the payer may calculate the payment using the wrong information. Even small coding errors can lead to lower reimbursement than the provider should receive.
If an organization does not actively track contract management responsibilities or compare expected reimbursement to what was actually received, underpaid claims can easily go unnoticed and reduce net revenue annually.
So, what impact do underpayments actually have on your bottom line?
Studies estimate that providers lose one to three percent of revenue annually, while some studies have shown losses of up to 11%. In 2025, 41% of providers reported denial rates of 10% or higher, which illustrates the increasing reimbursement pressure in the industry.
Historically, Medicare has lagged behind the cost of care, and this trend continues with the government agency covering about 83 cents per hospital dollar spent in 2023.
What does this mean in practical terms?
A 20-provider group that generated $13.3 million per year in net revenue annually stands to lose between $133,000 and $399,000. That lost revenue represents capital that could have been used for staffing, technology investments, or clinical equipment. These underpayment issues also affect valuation and can become a serious concern during mergers, acquisitions, or private equity review.
Underpayments tend to get lost in the shuffle and can be difficult to identify manually. An automated healthcare revenue cycle system makes spotting underpayments much easier.
These platforms perform automated payment variance analysis and contract modeling to quickly identify discrepancies between expected and actual reimbursement. AI-powered tools use machine learning to analyze claims data and detect patterns in underpaid claims across payers or service lines.
These systems generate detailed variance reports that help organizations analyze root cause issues and monitor trends throughout the entire revenue cycle.
When performing audits, it makes sense to look at your high-volume or high-dollar claims first to maximize your potential revenue recovery and recover lost revenue.
Organize underpaid claims by payer, dollar value, and service line. Prioritize accounts that are approaching appeal or filing deadlines.
Payer follow-up and escalation should be handled by experienced revenue cycle staff who know how to escalate payer issues effectively.
Automated platforms help identify discrepancies, generate appeal documentation, and track payer responses. These tools integrate with contract management systems to calculate expected reimbursement automatically.
Once recurring underpayment issues are identified, teams can analyze root cause problems such as payer adjudication errors or coding errors. Organizations can then provide direct feedback to payers and internal teams to prevent underpayments and reduce future underpayments.
End-to-end RCM services like Claimocity can instantly analyze contracts and payments to detect payment variance and accelerate underpayment recovery.
AI-driven tools continuously monitor claims data and flag underpaid claims. These solutions integrate with existing medical billing workflows to improve revenue cycle performance and strengthen cash flow without increasing administrative burden.
More than just a generic RCM platform, Claimocity’s AI-powered software and revenue cycle management services were designed specifically for medical practice workflows.
The team at Claimocity lives and breathes healthcare RCM. We rely on more than 20 years of experience dealing with hospital medicine billing complexities to leverage seasoned experts, support compliance with payer contracts, and help providers maximize revenue recovery.
Underpayment can represent a significant amount of lost revenue across the entire revenue cycle. With the right tools and processes, however, organizations can recover lost revenue and reduce revenue leakage without adding to an already heavy administrative burden.
You’ve already earned the revenue. Protect every dollar with proactive detection, automation, and a structured recovery process.
Even if you believe reimbursement is accurate, you won’t know if revenue leakage exists until you examine your current healthcare revenue cycle processes for inefficiencies.
Underpayments occur when approved claims are paid below the contracted reimbursement rate.
Common causes include payer processing errors, missed contract updates, coding errors, and complex payer reimbursement methods.
Modern systems detect underpayment issues using payment variance analysis, automated revenue cycle platforms, and machine learning.
The most effective strategy combines automated detection, prioritized follow-up, structured underpayment recovery services, and ongoing root cause analysis.
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