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Don’t bring a knife to a gun fight, and – if you’re in healthcare revenue cycle management – don’t bring a spreadsheet to a bot battle.
One of the biggest payer/provider battles today is over denials. With denial rates rising to 15%, healthcare providers must bring their best weapons to claims processing. Achieving clean claims in medical billing today takes cutting-edge technology and processes, as well as a keen awareness of exactly what payers use to deny claims.
Today, payers approach the claims arena armed with:
- Machine Learning to detect upcoding and unbundling, and adapt quickly to “unusual” or new billing patterns.
- Robotic Process Automation (RPA) to automate repetitive claims processing tasks and work around the clock while reducing human error.
- Automated Claim Auditing systems to perform real-time claim analysis and automate denial based on complex rule sets.
- Natural Language Processing (NLP) to analyze clinical notes to verify medical necessity and identify discrepancies between diagnoses and treatments.
- Blockchain to prevent duplicate payments.
- Big data to benchmark providers against peers for outlier detection.
- Extensive legal departments or retained law firms specializing in healthcare law.
- Advanced contract management systems
Providers need squeaky clean claims to stand up to this onslaught.
Luckily, technology doesn’t pick sides, and you can use these same tools to achieve clean claims in medical billing. Unleash these tactics and technologies to improve your claims accuracy and boost revenue.
What are clean claims in medical billing?
Clean claims are medical claims that are submitted without errors and can be processed without the need for additional information or manual intervention. These claims are accurate, complete, and compliant with payer requirements, allowing for faster reimbursement and reduced administrative burden.
Why clean claims in medical billing are important
Clean claims in medical billing are critical to revenue cycle management for several interconnected reasons.
First, clean claims are typically processed and paid much faster by payers, significantly improving cash flow for healthcare providers. According to MGMA, clean claims are often paid 2-3 weeks sooner than those with errors. This fast turnaround not only enhances financial stability but also reduces the administrative burden associated with claims management.
Moreover, clean claims boast a higher first-pass resolution rate, a move that streamlines the revenue cycle. Top-performing organizations achieve first-pass resolution rates of 95%. When claims are resolved on the first submission, organizations avoid time-consuming and costly rework, appeals, and follow-ups.
Beyond financial implications, clean claims play a crucial role in ensuring compliance with payer and regulatory requirements. This compliance is particularly important given the regular audits conducted by entities like the Office of Inspector General (OIG). By focusing on clean claims, healthcare providers not only optimize their revenue cycle but also fortify their position against potential audits and penalties.
6 recent innovations in clean claims in medical billing for providers
1. Artificial intelligence and machine learning
Our article on AI in Revenue Cycle Management explains that generative AI, which has recently garnered heated attention, is actually a subset of traditional AI. Traditional AI encompasses a broader range of technologies, including:
- robotic process automation
- optical character recognition
- natural language processing
- natural language models
Among these forms of traditional AI, robotic process automation is now the most widely used.
According to the American Hospital Association, 46% of hospitals and health systems are currently utilizing traditional AI in their RCM operations. It's highly likely that your organization is already leveraging it for one or more of these RCM tasks:
- automating claims processing
- streamlining billing procedures
- optimizing scheduling
- verifying patient eligibility
- conducting RCM analysis
This widespread adoption highlights the growing importance of AI technologies in improving efficiency and accuracy in healthcare revenue cycle management.
Today, there is no evidence that generative AI is helping healthcare organizations achieve clean claims in medical billing.
Traditional AI- and machine learning-driven platforms impact claims with these features:
- Predictive Analytics: AI algorithms predict potential claim denials before submission. Staff can then be directed to take particular care in preparing claims with a high probability of denial.
- Automated coding: Coding complexities, particularly for specialties like orthopedics and cardiology, make accurate coding a challenge. Machine learning models assist in accurate code selection based on clinical documentation. With coding errors one of the top reasons for claims denials, staff welcomes this assistance.
Clean claims with AI and ML: Real world examples
It’s easy to find healthcare organizations using AI and ML to improve claims accuracy in the real world.
One large radiology practice abandoned faulty manual processes and turned to an AI-driven platform to limit frequent errors in prior authorization generation and benefits verification. These errors led to a high denial rate. Once they automated 85% of their benefits verifications, their denials rate dropped by 50%.
A Fresno, California community health care network has implemented a traditional AI-powered tool to enhance their claims submission process. The predictive analytics feature of this tool analyzes claims before they are submitted. It uses historical payment data and payer adjudication rules to identify claims likely to be denied. This organization specifically targets two common reasons for denial: lack of prior authorization and services not covered by the payer.
Implementation of this AI-driven tool has yielded:
- 22% reduction in denials due to lack of prior authorization from commercial payers
- 18% decrease in denials for services not covered
- time savings of 30-35 hours per week by reducing the need for back-end appeals
Notably, these improvements were achieved without the need to hire additional revenue cycle management (RCM) staff, demonstrating the efficiency gains provided by the AI tool.
2. Automation for clean claims in medical billing
Where AI and ML leverage algorithms to get provider work done, automation depends on “bots” – short software programs that perform repetitive, rule-based tasks without the need for complex decision-making or learning from data. Unlike AI and ML, which can adapt and improve over time based on new data, robotic processing automation (RPA) bots execute predefined workflows to streamline operational processes efficiently.
Automated claims processing systems digitize and automate claims submission. Most healthcare organizations have already learned that manual submission involves a lot of paperwork, data entry, and coordination, which can lead to delays and errors. As such, 98% have automated their claims processing, according to the CAQH Index.
Electronic claims processing systems extract relevant information from electronic health records (EHRs) and populate claim forms automatically. This reduces the need for manual data entry, ensuring that claims are submitted promptly and accurately. Automation also allows for the bulk submission of claims, significantly speeding up the process and improving cash flow.
3. Real-time claim adjudication for claims accuracy
Real-time payer adjudication refers to the process where insurance claims are submitted, processed, and adjudicated (approved or denied) by the payer in real-time or near real-time, typically while the patient is still present at the point of care.
With manual claims processing, staff submit claims to be adjudicated in batches over a period of time, often after the patient has left the care setting. Manual handling increases the likelihood of errors, leading to denials and rework. Further, patients may remain uncertain about their financial obligations until claims are resolved, which can lead to billing confusion and dissatisfaction. The slow and opaque manual process also makes it difficult for patients and providers to track claim status in real-time.
Immediate claims feedback, on the other hand, alerts staff to potential issues for fast rectification. For instance, with government regulations and payer restrictions changing throughout the year, real-time claims adjudication helps ensure compliance with these moving targets. By adhering to the most recent payer and federal changes, you minimize the risk of claims errors, and therefore denials. Real-time claims adjudication enables organizations to identify and address potential issues before they escalate.
Real-time claims adjudication requires sophisticated technology systems that can process claims instantly, apply complex rules, and communicate between providers and payers seamlessly. It takes software to complete these critical real-time claims adjudication tasks. Without advanced software systems, the speed and accuracy required for real-time claims adjudication is not possible.
While claims submission software gets integrated with Electronic Health Records (EHR) systems or Practice Management Systems (PMS), it is not inherently part of either. Hundreds of healthcare claims software solutions exist. Explore them on G2, the software review site.
4. NLP for improving clean claims in medical billing
Natural Language Processing (NLP), a subset of artificial intelligence, extracts relevant information from unstructured clinical notes for billing. By analyzing unstructured data, such as physician notes, lab reports, and discharge summaries, it extracts relevant billing information and converts it into structured formats.
NLP contributes to cleaner claims because it:
Extracts accurate billing information from clinical notes
In this step, NLP ensures that the correct codes and documentation are included in claims submissions, reducing coding errors and omissions—common reasons for claim denials. For example, NLP tools can identify specific diagnoses or procedures mentioned in clinical notes and ensure they align with the appropriate billing codes.
Ensures proper documentation for medical necessity
One of the leading causes of claim denials is insufficient documentation to support medical necessity. NLP algorithms can analyze clinical records to verify that the services provided meet payer requirements for medical necessity, minimizing denials due to inadequate justification for treatments or procedures. For instance, NLP can flag missing details in documentation that are required by payers to approve specific claims.
Automates code assignments
NLP can assist in automating the assignment of ICD-10, CPT, or HCPCS codes by analyzing clinical documentation and matching it with the appropriate billing codes. This step reduces reliance on manual coding, which is prone to human error, and improves coding accuracy while reducing delays caused by manual reviews. For example, NLP tools can identify key terms in physician notes, such as "type 2 diabetes" or "knee replacement," and recommend the correct billing codes.
Identifies discrepancies between documentation and claims
NLP can cross-check claims data against clinical documentation to identify discrepancies between diagnoses, procedures, and treatments. This ensures that claims accurately reflect the services provided, reducing rejections caused by mismatched or incomplete information. For example, if a claim includes a procedure code that isn't supported by the clinical notes, NLP can flag it before submission.
Improving claims scrubbing
NLP enhances claims scrubbing processes by identifying errors or inconsistencies before submission. This includes checking for missing patient information, incorrect codes, or incomplete documentation. By increasing the likelihood of first-pass resolution and reducing rework, NLP ensures cleaner claims submissions. For example, NLP-powered claims scrubbing tools can flag incomplete fields or mismatched codes in real time.
Enhancing compliance with payer rules
Payers often have complex and varying rules for claims submission. NLP systems can analyze payer-specific guidelines and ensure that submitted claims comply with these requirements. By reducing denials caused by non-compliance with payer policies, NLP helps streamline the revenue cycle process. For example, NLP tools can check whether a claim meets specific payer requirements for bundled services or modifiers.
NLP significantly enhances clean claims when it:
- processes unstructured data
- automates coding tasks
- ensures compliance with payer rules
- identifies discrepancies in documentation
- streamlines prior authorization processes
These accomplishments not only reduce administrative burdens but also accelerates reimbursements and improves overall revenue cycle efficiency for healthcare organizations.
The above AHA resource highlights a healthcare organization increasing its claims accuracy rate with NOP. Auburn Community Hospital, a 99-bed independent rural access facility in New York, began integrating NLP into its RPA and machine learning nearly ten years ago.
It credits NLP with these improvements in its RCM performance:
- 50% decrease in discharged-not-final-billed cases
- 40% increase in coder productivity
- 4.6% rise in case mix index
This example demonstrates how NLP and other AI technologies can substantially enhance efficiency and accuracy in healthcare financial operations, even for smaller, rural hospitals.
5. Advanced contract management systems
Another technology update improves your clean claims rates: contract management systems.
Contract management systems are specialized software solutions designed to manage and optimize healthcare contracts between providers and payers. These systems centralize contract information, monitor payer payments, automate compliance monitoring, and streamline workflows to improve provider efficiency and revenue. By ensuring that claims are submitted in accordance with contractual terms, contract management systems help healthcare organizations avoid denials and underpayments, ultimately enhancing their revenue cycle performance.
Contract management systems improve clean claims rates because they:
- automatically apply contract terms and rules to claims, reducing errors from manual interpretation.
- maintain current fee schedules, ensuring claims are submitted with accurate pricing.
- integrate real-time eligibility checks, reducing denials due to coverage issues.
- flag non-compliant codes based on contract terms, preventing denials.
- track and enforce prior authorization requirements specified in contracts.
- effect proper bundling of services and application of modifiers as per contract terms.
- enforce payer-specific documentation or submission requirements.
- track and alert on approaching timely filing deadlines specified in contracts.
- analyze denials in the context of contract terms, helping identify systemic issues.
By addressing these areas, contract management systems significantly contribute to higher clean claims rates and improved revenue cycle performance.
Payers like Blue Cross / Blue Shield, Humana, Optum and more digitize and analyze contracts via contract management platforms. Providers need to do the same.
Take a quick, self-guided tour through a powerful contract performance optimization and payer underpayments identification tool:
6. Blockchain: coming soon to improve clean claims in medical billing
Just what you need: one more form of technology to wrap your head around.
We promise that once you see the security and interoperability improvements blockchain delivers, you’ll want to know all about it. And you have time!
Blockchain hasn’t quite reached the healthcare space yet, but it’s coming. Let’s take a step back to determine where it will fit in the provider technology ecosystem.
The introduction of Electronic Health Records (EHRs) has transformed how patient data is stored and accessed, but the current EHR landscape remains fragmented, with data often confined within individual healthcare organizations. Today’s patients see many providers, often across different healthcare systems. They get blood drawn at one lab and joint X-rays performed at another. All that data has to go to the patient’s primary care doctor and any specialists involved in their care.
This fragmentation hinders seamless data sharing and interoperability. Blockchain technology offers a promising solution by enabling secure, decentralized data exchange and storage. It allows medical providers to easily connect EHRs to clearinghouses, leveraging blockchain's distributed architecture to securely share patient data across multiple providers. Blockchain technology eliminates the need for centralized repositories that are vulnerable to breaches and failures. Patient records are stored as unique, immutable blocks on the blockchain, accessible only to authorized parties. Blockchain's cryptographic capabilities ensure that patient data remains encrypted and secure during transmission, safeguarding sensitive information and fostering trust in the healthcare ecosystem.
Given the $2.4 billion Change Healthcare ransomware débâcle, patient security is paramount now. You’ll be hearing more about blockchain in years to come. Embrace it.
Blockchain will be impacting claims accuracy, mostly via their use of “smart contracts,” a new term most often associated with blockchain. Smart contracts are special because they can be programmed to automate key processes, such as verifying eligibility, authorizing payments based on predefined criteria, and triggering automatic reimbursements. By doing so, they significantly reduce the need for manual intervention and lower the risk of human error in claims, thereby enhancing efficiency and accuracy in financial transactions.
Improve clean claims in medical billing when you leverage contract management technology
From artificial intelligence and machine learning to robotic process automation and advanced contract management systems, healthcare providers now have a powerful arsenal of tools at their disposal to improve claims accuracy.
As payers continue to adopt increasingly sophisticated systems for claims adjudication and denial management, it's crucial for providers to keep pace. By investing in and leveraging these cutting-edge technologies, healthcare organizations can ensure they're not just meeting the bare minimum for claim submission, but are actively optimizing their processes to secure the reimbursements they've rightfully earned. In this technological arms race, staying ahead isn't just about efficiency—it's about financial survival and the ability to continue providing quality care to patients.
To improve clean claims rates through effective contract management, healthcare organizations can leverage specialized platforms like MD Clarity's RevFind. This type of solution centralizes contract information, automates compliance monitoring, and streamlines workflows for enhanced efficiency.
RevFind supports your staff in ensuring:
- claims details are compliant with payer requirements
- claims are compliant with governmental regulations
- renewal deadline adherence
- decisions are backed up by advanced analytics capabilities
Get a demo to see how RevFind can limit your claims reprocessing, raise your net revenue, and put you in control of your contracts.
Get paid in full by bringing clarity to your revenue cycle
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