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Clean Claims: A New Year's Resolution You Can Keep

Date posted: Thursday, February 16, 2023

Category: Coding

Over half of hospital claim denials are due to coding errors¹-making medical coding quality and accuracy a must-have for health systems and practices looking to improve their revenue cycle management (RCM) operations and finances in 2023.

Considering that coding errors can cost upwards of $20 billion per year in either delayed or permanently lost reimbursement,² clean claims are essential to the health of any medical organization's bottom line.

While new year's resolutions are notoriously hard to keep, if your organization's resolutions include cleaning up your claims to avoid denials, high-quality and accurate medical coding can help you achieve and sustain your goals.

Why do claims get denied?

Most often, claims are denied due to incorrect, missing, or skewed information about patient encounters and any services rendered.

Incorrect Coding, Missing Information

Patient details essential for claim processing include data such as their name, date of birth, sex, home address, social security number, and insurance policy number. If a payer receives a claim with any of these data fields missing or wrong, it is an automatic denial.

Patient encounter details are a bit more complex and can include a broad swatch of errors. Professionals may report the wrong ICD, CPT, or HPCS codes. Or append modifiers on HCPCS or CPT codes that conflict with the original code or documentation and create confusion.

Whatever the source, incorrect coding is not something to take lightly. One study found that inaccurate ICD-10 coding can result in a potential loss of $1.149 million across 612 inpatient cases or an average of $1,877 per inpatient case.³

Duplicate billing, when an office submits a second claim for an encounter without knowing it has already been paid for or reported, is another source of claim denials. These errors often boil down to poor communication between billing and coding staff.

Other more technical, sometimes underhanded, coding practices lead to denied claims, such as overcoding and undercoding.

Overcoding

Overcoding occurs when a medical organization, whether intentionally or unintentionally, assigns incorrect codes that will result in higher than justified reimbursements.

The two most common overcoding culprits are upcoding and unbundling.

Upcoding happens when a claim includes codes for a diagnosis, procedure, or service that is worth more in reimbursements than is warranted based on what actually happened. For example, a healthcare provider may use a code that indicates a complex procedure that may have lasted an hour, when in reality, the procedure was more routine and likely only lasted 15 minutes.

Unbundling occurs when a claim includes codes for individual procedures that should have been billed together with one CPT code.

Repeated instances of overcoding are especially concerning because they are not only fuel for denied claims but also can lead to suspicion of fraudulent activity and audits.

Undercoding

As the name suggests, undercoding is the opposite of overcoding. Undercoding happens when claims are submitted with codes that report services that amount to less in reimbursements than a medical organization should receive. Undercoding also includes instances when performed services are not reported at all.

With the increasing fear of payer audits looming in the minds of coding professionals, some may even intentionally undercode to ensure they aren't responsible for any overpayments. However, organizations should strive to avoid undercoding at all costs. While undercoding may feel like a defensive mechanism, it is still incorrect coding and another catalyst for denied claims and lost revenue.

How Denied Claims Negatively Affect the Revenue Cycle

Denied claims can add days, and sometimes even weeks, to the collection period. When left unchecked, high volumes of denied claims can create lasting financial consequences.

Delayed or lost reimbursements decrease payment velocity and cash flow and negatively affect KPIs, such as net collection rate (NCR), first pass resolution rate (FPRR), total charge lag, and days in A/R. They also affect an organization's ability to properly forecast their profits. Without accurate data on costs, write-offs, and reimbursements, revenue cycle management (RCM) professionals cannot anticipate their financial future.

Denied claims can also lead to coding backlogs. When staff members are constantly appealing, managing, and resolving denials, it is difficult for them to find time to get to other portions of their job. As a result, there often is not enough time or resources to devote to coding quality and accuracy.

This, in turn, creates a negative feedback loop of burned-out staff members, low-quality coding, and denied claims. Without any intervention, this vicious cycle is difficult to stop.

Benefits of Clean Claims

Clean claims have the power to touch virtually every facet of RCM operations, helping medical organizations boost their efficiency and revenue.

Eliminating the lengthy and often labor-intensive process of receiving a claim denial, determining what went wrong, and then resolving it with the payor increases payment velocity and cash flow.

Fewer denied claims result in a lighter workload for medical coding personnel, slowing the vicious cycle already mentioned. When team members have time to focus on complex claims at greater risk of denial due and ensure accuracy at the get-go, they can dramatically reduce their denial rate.

A decreased workload also allows coders to thoroughly evaluate claims to find long-tail ICD codes and previously missed procedure codes and modifiers to prevent future mistakes.

Five Resolutions RCM Professionals Can Make to Ensure Clean Claims

Sticking to these five resolutions will help you make 2023 the year of clean claims within your organization.

1. Prioritize staff education.

When day-to-day operations get busy, education and training are often among the first areas that fall by the wayside. Coding employees and other RCM personnel are so focused on finishing their tasks that they don't have the time, energy, or opportunities to learn about new industry updates.

However, a large workload doesn't stop ICD and CPT coding guidelines from undergoing constant evolutions. Employees uninformed about coding updates may inadvertently make repeated mistakes, resulting in denied claims.

To add fuel to the fire, every commercial payor regularly updates their fee schedules and contracts, and employees must be aware of these changes or risk denial. As you may know, it's not uncommon for a health system to have 40+ contracts with payors-creating many opportunities for error without the proper education.

RCM departments that prioritize regular education and training undoubtedly will boost their coding quality and accuracy. While some departments may find it challenging to carve out the time to do so, the time they will spend researching, defending, and fixing denied claims caused by a lack of employee education is greater.

2. Don't miss claims deadlines.

Every payor has its own timeline for filing claims. Some are as short as 30 days after an encounter, and others can be as long as a year. Submitting a claim after this predetermined window of time will result in a denial.

Filing within the payor timeline is called timely filing-a practice that every organization looking to reduce their number of denied claims should strive to achieve. That said, consistent timely filing is sometimes easier said than done.

One contributing factor to the difficulty is that there is no standard timeline, making it tough for staff to keep track of the various deadlines, especially if an organization works with multiple payors. Pairing that with charting and coding backlogs creates a recipe for missing deadlines and lost revenue due to claim denials.

Similar to the previous resolution, RCM departments that make time to educate their team members on their different payors' claim timelines will improve their operational efficiency and avoid missing deadlines. Finding and implementing solutions to help eliminate charting and coding backlogs also will help support this resolution.

3. Reduce medical coding and billing staff burnout.

Even the best coding and billing staff will make costly errors when they feel burned out and unsupported.

A major contributor to burnout is staffing issues-a current phenomenon felt by virtually every department within the healthcare industry. In fact, MGMA found that 73% of medical group practices reported staffing as the leading challenge in 2022.4

Naturally, when there is too much work to go around and not enough employees, workload, burnout levels, and mistakes made by staff all increase. To avoid these negative consequences, make supporting existing coding and billing personnel a top priority in 2023.

4. Audit your medical coding.

Regular internal audits are an excellent tool to clean up your claims because they provide in-depth insight into both good and bad day-to-day coding operations. However, auditing is another function that often falls by the wayside due to a lack of time and resources.

Because many organizations do not conduct regular audits, they are entirely unaware of their current accuracy levels. A lack of visibility into such an essential aspect of RCM operations creates the perfect breeding ground for inaccuracies that build upon each other and wreak havoc on an organization's denial rates.

For example, a coding team may continually use the wrong code because they are unaware of a payor guideline update. Without identifying these mistakes via an audit, this error could fly under the radar for an extended period of time.

Choosing not to access and leverage this information is detrimental to any efforts to clean up your claims. How can you create a benchmark of success if you don't know where you currently stand? It's nearly impossible.

If your organization has an existing baseline measurement for medical coding success, executing regular internal audits will provide a clearer picture of your progress toward that goal.

5. Utilize autonomous medical coding technology.

Artificial intelligence (AI) powered autonomous medical coding technology that leverages deep learning can help health systems and medical practices achieve and sustain the aforementioned resolutions.

To make 2023 the year of clean claims, use AI medical coding technology to automate portions of work that commonly cause denied claims. Deploying these solutions to complete tasks such as insurance eligibility verification or fully coding routine encounters will help coding and billing teams avoid incorrect and missing patient and service information, as well as incorrect coding practices such as over and undercoding.

RCM professionals should view autonomous medical coding technology as an extension of their existing team. This technology is constantly up to date on industry changes and always guarantees high accuracy across all major coding elements-evaluation and management (E/M) levels, procedure codes, modifiers, and diagnosis codes.

Because the latest generation deep learning AI solutions ensure a high level of accuracy, coding and billing teams can avoid a large amount of denied claims from ever occurring, leading to less denial management rework for existing team members.

With more free time, billing and coding staff can attend regular education and training sessions to review the latest industry updates and ensure they are not missing any payor deadlines.

Support burned out employees by taking repetitive, sometimes tedious tasks off their plates and put more time back into their day. Then, they can use their knowledge and expertise to tackle more complex issues, as well as manage and resolve previously denied claims.

AI-powered technology also makes internal auditing an easy and informative process that reveals further opportunities for efficiency and accuracy across all RCM operations.

From the beginning, an autonomous solution can analyze thousands of examples of an organization's current E/M levels, procedure codes, modifiers, and diagnosis codes, and compare it to its own coding through a comprehensive proof of concept. The technology will reveal patterns of errors and inaccuracies that your organization can use to reduce denied claims and identify potential missed revenue.

For example, the AI-powered auditor can identify trends that affect reimbursements, such as missed quaternary diagnosis codes or instances of coding staff reporting lower E/M level codes than the documentation supports.

Your organization can use this information not only to increase clean claims and boost RCM operational efficiency but also to find hidden pockets of revenue and improve your bottom line.

Don't Let The Implementation Process Deter You

A major concern that comes with any new technology, especially within the healthcare industry, is the implementation process. If the implementation requires a heavy lift from multiple teams across a practice or health system or takes years to come to fruition, the likelihood of adoption drops significantly.

Historically, technology implementation processes ask a lot from information technology (IT) teams, who are usually already bogged down with other work. When talking specifically about implementing an autonomous coding solution, overwhelmed IT and coding teams could see this as yet another burdensome task adding more time to their existing workload.

However, this isn't always the case. Today's AI-driven technology can be implemented fully in as little as three weeks. While coding and IT teams may need to provide some information, the implementation process does not rest squarely on their shoulders. Let's look at a standard implementation process for an AI-powered coding solution.

The first pieces of information needed are a medical organization's day-to-day rules and workload from a coder's perspective. The same way a newly hired coder would require an information sheet to know which non-CMS custom guidelines are specific to a location, payer, provider, or organization as a whole to apply, an AI solution needs the same rule book basics before building its coding model. Then, the solution needs some sample data provided in any format from the IT team. The AI-powered autonomous medical coding technology then develops its coding model seamlessly.

The more data provided, the more informed and accurate the coding model will be. While that may seem like a lot of work, this data dump requires minimal effort from both teams.

Once the solution creates a customized coding model based on the data from the IT team and guidelines from the coding teams, organizations can use it quickly and easily to accomplish daily coding activities. After implementation, IT teams can leverage any standard EHR coding API, HL7, or SFTP file transfer to ensure that the results flow into their system seamlessly. These file integrations are familiar with all EHR and billing system format types.

This entire process takes only three weeks to three months, depending on the complexity of an organization's workflows and coding guidelines.

One item to note is that some vendors use a rule-based machine learning AI engine to build their coding models. So, while their GPU or processor creates the automation, analysts must still manually build rules into the system to account for any variances in data format and coding guidelines.

The issue with these engines is that they break very easily, cannot automate as much volume, and take significantly longer to implement—making it essential to find a vendor that uses a deep-learning AI model. Deep learning AI can interpret unstructured data with many variances, as any human would, making it more robust and scalable. Leverage a deep learning AI coding solution if you want to actually see the impact in 2023.

Make 2023 The Year of Clean Claims

The new year brings new opportunities to improve medical coding accuracy and, thus, reduce denied claims, save large portions of delayed or lost revenue, and boost overall RCM efficiency.

Health systems and medical practices would be remiss not to consider autonomous medical coding technology. Billing and coding staff can harness these autonomous solutions to work at the top of their game and not worry about simple, routine tasks clogging up their day-to-day workload.

Make 2023 the year that your organization keeps its resolutions and ushers in a new era of clean claims by fully embracing AI technology.

Taylor Ross, CPC, CCA, is the Strategy and Operations Lead at Fathom, a Tarsadia and Founders Fund backed company that uses deep learning to automate medical coding. At Fathom, Taylor is involved in strategic analysis and client analytics and reporting. As a certified coder, she also interfaces with engineering and product development on coding quality and verification. Visit http://www.fathomhealth.com

Prior to joining Fathom, Taylor was a Consultant with Berkeley Research Group, specializing in coding and compliance consulting for various healthcare organizations and physician practices. Additionally, Taylor graduated from the University of Pennsylvania with a bachelor's degree in economics and minors in mathematics, healthcare services management, and biological basis of behavior.

Fathom is the nation's leading medical coding automation platform, an AI solution that fuses the best of deep learning and Natural Language Processing (NLP) to automate medical coding with unprecedented accuracy and efficiency. Fathom provides the highest automation rates and the broadest specialty coverage to help clients increase speed and accuracy while reducing costs.

Fathom is backed by world-class investors, including 8VC, Alkeon Capital, ApolloMD, Cedars-Sinai, Founders Fund, GV, Lightspeed Venture Partners, Tarsadia, and Vituity’s Inflect Health. For more information, visit fathomhealth.com, or follow the company on LinkedIn.

References

1. Holloway SC, Peterson M, MacDonald A, Pollak BS. From Revenue Cycle Management to revenue excellence. McKinsey & Company. https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/from-revenue-cycle-management-to-revenue-excellence. Published June 10, 2021. Accessed October 10, 2022.

2. Clean claim, write-off metrics key to diagnostic provider success. RevCycleIntelligence. https://revcycleintelligence.com/news/clean-claim-write-off-metrics-key-to-diagnostic-provider-success. Published August 6, 2020. Accessed October 10, 2022.

3. Dano Tkacik E, Charland RHIT, CCS K. The financial truth about ICD-10 coding accuracy: Two drgs to watch. hfma. https://www.hfma.org/topics/article/53771.html. Published April 30, 2017. Accessed October 10, 2022.

4. MGMA Staff. Outsourcing, automation may provide help to short-staffed practices. https://www.mgma.com/data/data-stories/outsourcing,-automation-may-provide-help-to-short. Published March 23, 2022. Accessed October 10, 2022.

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