Diving Deeper Into Denials
By Selena Chavis
For The Record
Vol. 32 No. 2 P. 14
Health care professionals draw on the power of analytics to improve revenue cycle processes.
Claim denials are a sore spot for many health care providers as they strive to improve revenue cycle to support leaner operations and better profit margins. Notably, a 2017 Change Healthcare study found that a whopping $262 billion out of $3 trillion in submitted medical claims by US hospitals were denied.
To improve the outlook, health care organizations are increasingly looking to the promise of analytics, according to John Raley, president of revenue cycle management at iMedX, who cites the technology’s ability to provide a more comprehensive and systematic approach to identifying root causes and trends from both provider and health plan perspectives.
“Such analytics allows the entity to more readily address and rectify these denials and underlying causes and also allows the focus to be placed upon the most impactful areas with the greatest financial benefit, further allowing prioritization or denials resolution,” Raley says. “For years, health care providers have tried to resolve denials on a case-by-case basis or even by individual payers through various means such as bulk appeals and individual claim rebills and appeals. But there has been a lack of dedicated focus on the reasons for the denials and how to approach these opportunities for a meaningful and positive impact to the organization.”
Manual processes that have defined revenue cycle management processes over the past decade have limited the ability of health care organizations to get a bird’s eye view of claim denial trends, suggests Angela Billet, senior vice president of client services at AGS Health.
“With the emergence of analytics and underlying technology, everything has changed. It’s now easier than ever to handle large data sets,” Billet notes, adding that the right combination of data analysts and revenue cycle experts can enable providers to significantly reduce denial rates. “Advanced data tools have made denial prediction possible, giving health care providers the ability to review and fix outliers early in the process.”
Marianne Loeffler, COC, CPC, CPC-I, CRCR, a charge audit validator with Medical Record Associates, points out that health care organizations can now take the data elements from the explanation of benefits (EOB) to identify trends in specific departments. “For instance, an organization can run reports that identify different types of denials—[such as] medical necessity, authorization, coordination of benefits—and use that information to pinpoint the root cause, then set up a workgroup that includes key stakeholders from each department to work through ways to prevent future denials,” she says.
Beyond process flow opportunities, denial analytics can further pinpoint nonfinancially viable service lines, clinical treatment plan/pathways enhancement needs, clinical criteria, and payer guidelines standardization, Raley says, pointing out that the insights that come from denials impact every single touchpoint of both the patient care and revenue cycle continuum.
“Analytics are ensuring a more holistic approach to the denials and allowing for specific key performance indicators and metrics to address the denial trends by priority and categorization in a more real-time approach vs being handled retrospectively in the claims billing and claims resolution aspect,” he says.
The Impact of CARCs and RARCs
Billet notes that claim adjustment reason codes (CARCs) describe the primary reason for denials while remittance advice remark codes (RARCs) help clarify the denial. Consequently, both codes are critical to properly analyzing a denial.
CARCs are used on an EOB to communicate the reason for a payment adjustment that describes why a claim or a line item on a claim was paid differently than it was billed or denied altogether. It’s important to understand that each carrier uses these codes in different ways, Loeffler notes.
Specifically, CARCs use a two-letter prefix in addition to a number to communicate. Used to assign responsibility for the adjustment amounts, the two-letter “group codes” feature the following:
• CO (contractual obligation);
• CR (corrections and reversal note—this value is not to be used with 005010 and higher);
• OA (other adjustment);
• PI (payer-initiated reductions); and
• PR (patient responsibility).
For example, CO45 indicates that this is a contractual obligation in which the charge billed exceeds the fee schedule/maximum allowable or contracted/legislated fee. This means there is a set or “allowed” price that the carrier will pay for the service being billed. The difference between the amount billed and the allowed amount is an amount that is not paid and must be adjusted.
In contrast, RARCs are used to provide additional explanation for an adjustment already described by a CARC or to convey information about remittance processing. For example, M20 refers to missing/incomplete/invalid HCPCS codes.
CARCs and RARCs provide a gateway to identifying and understanding trends, patterns, volume, and the dollar value of denials, Raley says. “These codes will allow a provider or health care entity to categorize the reasons the payers have either not adjudicated a claim at all or incorrectly adjudicated a claim,” he explains. “Conversely, such denials may be accurate and correct from the payer, thus identifying key process failures, noncompliance with payer guidelines, gaps in clinical care guidelines, and medical necessity criteria that support the level of care being rendered.”
Proper and detailed reviews of CARCs and RARCs provide a window into the areas where providers have an opportunity to enhance processes and minimize future denials, Raley adds. This model provides a different approach than traditional methods of seeking resolution at the individual claim level, which may result in proper payment of the claim in question but do not result in an overall reduction in denials at the payer level or, more importantly, at the institutional level.
“Performing a ‘deep dive’ analytics review of these codes will result in an understanding of payer expectations for various claim and service types—why the net revenue and cash flow for a specific payer is not at the expected level—and identify the departments and processes that are resulting in routine denials,” Raley says.
Understanding the Differences Between Reason Code, Issue, and Root Cause
In summary, Billet differentiates the reason code, issue, and root cause this way: “CARC or the reason code is the information provided by the payer for the denial. The issue refers to the prima facie grounds for the denial. The issue in most cases is on the provider side but could occur on the payer side. Finally, the root cause refers to the buried issue or the process where the cause for the denial originated.”
With these three elements in play, Loeffler believes that health care organizations will have the necessary data at their disposal to create a corrective action plan and provide feedback to executive management and key stakeholders in operations. “The analysis will also uncover processes and procedures that may need to be created or revised in order to ensure denials are prevented moving forward,” she adds.
Raley says capturing these items within an analytics initiative allows for the hierarchy and drill-down necessary to resolve the accounts in question; identify multiple trends such as by physician, department, procedure or service line, end user, and payer and charge codes; and identify and address the processes and procedures that could be leading to such trends.
Offering a simple example, Raley points to a denial for “no authorization,” where a reason code of 39 states: “Services denied at the time authorization/precertification was requested.” The issue is that proper authorization was not obtained on the case. The root cause may be that the front-end processes in place for obtaining authorization do not have a mechanism to reschedule or cancel services until an authorization is confirmed.
“Having all three elements in a comprehensive analysis is crucial to being a change agent for long-term positive effects and not only the reduction of denial but also the elimination of denials,” Raley notes. “Without these three areas, the results will continue to reflect repetitive denials and individual claims resolution.”
In another example, Billet points to a procedure that is denied for being deemed not medically necessary. The initial issue could be that the coder missed recording a diagnosis code or that the underlying diagnosis data in the medical record were unavailable. In either case, the root cause signals a need for improved education and documentation.
Data Elements Needed for Relevant Analysis
The amount and type of data available will dictate the level of analysis that can be performed, Raley says. “With that being said, the data elements presented in an 837 and 835 file set are the most relevant in an initial denial analysis. If 837 and 835 files are not available, all the data elements reflected on the claim forms along with the detail of the remittance advices and EOB are necessary to begin such analysis,” he says. “Initial findings and trends may necessitate the need for further information derived from the clinical documentation housed in the EMR.
Ideally, the demographic, medical, insurance, and financial data for all claims, both denied and paid, are captured to perform the best possible analysis. “Some of the rare denial scenarios could be based on place of service, the patient’s gender, the patient’s relationship with the subscriber, the frequency of a service within a time period, or provider acceptance to assignment of benefit,” Billet says. “If all these fields are available in the database, the analysis would yield the best results.”
Loeffler says the most necessary data elements are the following:
• CPT codes;
• revenue codes;
• bill type;
• expected reimbursement;
• quantity billed;
• payer; and
• account numbers.
Creating an Action Plan for Process Improvement
According to Billet, creating a workable action plan requires engaging the support of the data analysis team and revenue cycle stakeholders to identify what changes are needed. Improvements may include simple process measures, additional training, system upgrades, and reprogramming.
“The key thing to remember is that a comprehensive denial management plan is not a ‘one and done.’ Ongoing analysis is needed,” Billet says, emphasizing that as much as automation can help, people are at the heart of this process. “All of the automation in the world won’t fix a poorly trained employee.”
As a first step, Raley suggests organizations summarize denials in a manner that clearly identifies where there is the most opportunity and financial ramifications. “This will reflect which denial reason code or category needs prioritization in the development of an action plan. Likely some denial categories may be as simple as an overview with the staff or a system tweak,” he says, pointing out that the process of tying together reason code, issue, and root cause will most often produce the insights needed for an action plan.
“These three items need to be clearly defined and determined for the denial category requiring an action plan,” he explains. “This will require some type of categorization of the denials and association of those to the key areas of responsibility or root cause.”
The next step entails securing the current policies, procedures, and protocols associated with the root cause areas to identify process gaps. “The root cause will also need to identify end users, clinical team members, physicians, and anyone else associated with the denial to further determine trending by individual to address training needs if the protocols and procedures are appropriately aligned to prevent such denials when followed correctly,” Raley says.
According to Raley, an action plan must outline how the existing denied cases will be handled and by whom, how their successful resolution will be measured and tracked, how unresolvable denials will be handled and accounted for, and perhaps most importantly, what actions will be taken to prevent future denials in this category.
“The development and implementation of specific key performance indicators to monitor this denial category will be crucial,” he notes. “Once this category sees acceptable improvement, the action plans continue through each of the prioritized denial categories.”
— Selena Chavis is a Florida-based freelance journalist whose writing appears regularly in various trade and consumer publications, covering everything from corporate and managerial topics to health care and travel.