Audit
Medical coding audit processes face a plethora of issues that need to be addressed for future opportunities. From inaccuracies within current audits to workforce challenges to high denial rates, current processes negatively impact hospital and RCM/HIM efficiency by creating more challenges than they solve. Learn how to identify these problems in your audit processes and how AI and pre-bill auditing can act as a viable solution.
Medical coding audits are a key component of health information management (HIM) and revenue cycle management (RCM) activities, which is why it is important to maintain the quality of workflow throughout the auditing process. Presently, American hospitals possess several issues, including: (1) a lack of audit planning with limited focus on high-value opportunities; (2) missing expertise to identify which cases are most likely to have opportunities; and (3) limited resources with high volume of claims, allowing audits of less than 10% of cases to be audited.
These issues need to be identified and addressed to close gaps in the current audit process and improve future preparedness toward efficient medical auditing. AI and pre-bill auditing can act as the necessary solution with their ability to uncover past and new reimbursement opportunities, thus saving hospitals time and money.
The root of most auditing challenges is the decline of in-house HIM workforces, which worsened in 2020. Because of cuts made before and during the pandemic, limited revenue cycle staff are struggling to maintain a stable working pace as American hospitals return to their normal, pre-pandemic operations and elective surgery volumes. Furthermore, they now have to take into account new challenges: “they face stiffer competition, nationally, with increasing demand for revenue cycle staff, including billing and coding professionals, many of whom are now part of a growing remote workforce.”
If the issue is worker shortage, why not rehire the HIM professionals that had to be let go during the pandemic? One reason is that there were simply not many auditors and coders in the first place with the needed skill set and expertise with the ICD-10 medical codeset. Even now, mid-sized hospitals (100-250 beds) only have roughly two auditors who must carry out all of the hospital’s auditing processes. Secondly, healthcare facilities are unable to resolve these capacity issues with rehiring because several organizations simply cannot afford it. Many hospitals experienced steep financial losses during the pandemic, and have not yet recovered enough to hire staff back as revenue cycle work volumes rise again.
So with these restricted staff numbers, most hospitals do not have the bandwidth to audit 100% of their accounts. In fact, they are only able to accomplish auditing between 10-20% on their own with manual processes. There is not enough time before discharge, and not enough people overall to boost efficiency without any form of assistance.
To get the proportion of claims audited closer to 100%, hospitals have the option to outsource to a service vendor, but it is very expensive. Depending on the type of audits hospitals are looking for, the amount of data needed to be audited, and for how long, they can be looking at multi-million dollar contracts with these external auditing companies. At those prices, it is not a sustainable option that can be used long term, especially without it having the convenience of in-house processes.
One large factor contributing to auditing errors is the auditing method itself. The majority of American hospitals currently deploy manual techniques to spot-check their coded and claims data after it has already been sent off for billing. Once claims are coded, auditors re-review what has been coded to ensure that the charts have been coded correctly. The manual reviews and worker shortage results in the lengthy auditing process, that of which only achieves 10-20% of the documentation being audited. In addition to it being time-consuming, it leaves a lot of room for inaccuracies. Not only does this open the door for plenty of error-filled possibilities because of its manual nature, but it also means that even if errors are found, they are found late. This results in missed and delayed reimbursements.
Common accuracy errors in medical auditing range from denials, incorrect diagnosis-related groups (DRGs), and non-optimized DRGs, which are what the reimbursement is made on. Inaccuracies can result in various consequences for several parties, such as long term reputational harm should a hospital get audited due to repetitively sending inaccurate claims. It is because of these consequences that the American Hospital Association (AHA) has made it clear to the Centers for Medicare & Medicaid Services (CMS) that it is necessary for them to evaluate and change their methods for conducting hospital compliance reviews. As the U.S. federal agency that assists in providing health insurance, it is the responsibility of the CMS to address the constant flaws and inaccuracies in the audits that have led to "vastly overstated repayment demands, unwarranted reputational harm, and diversion of hospital and physician leaders' time" from providing their much-needed services to patients, which is their highest priority.
Coupled with inefficient methods riddled with inaccuracies, current auditing processes are not void of quality challenges. Low data quality results in limited improvement to patient care, lower facility administration efficiencies, and lower reimbursements. It can also negatively impact visibility into health system performance and inhibit potential efficiencies in care delivery, operational processes, and financial performance. Quality-based concerns with significant impacts include second level reviews, hospital-acquired conditions (HACs), and present on admission (POA) status. Second level reviews, which are conducted manually, are meant to prevent claims denials so that the final coding and DRG assignment is accurate prior to billing. They also ensure that the documentation correctly records information such as the severity of illness and risk of mortality. These reviews are a “concurrent process between the CDI and coding staff to verify appropriate coding before billing and a great opportunity to identify areas of improvement and education” for RCM and HIM professionals as well as physicians.
These current audit processes have a tendency to fail in their purpose of providing ways in which hospital workflows can be streamlined and improved for efficiency, accuracy, and quality reasons. This is largely due to how auditors often focus on reviewing obsolete standards while also including several incorrect claim denials.
The high rates of claim denials remain as another core issue indicating a need for change in American auditing. The increasing workloads for the declining numbers of workers mean that hospitals do not have the needed resources or capacity. When “medical billing and coding are handled by non-experts, billing complex clinical cases meets with denials.” Staff attrition and training challenges contribute to this denials factor as well. Other reasons behind high denial rates include new documentation and growing denials and procedures backlogs.
Denial rates have been climbing as of late, with Harmony having observed a 20%-plus increase over the past five years. In 2017, the average rate of claims denial in large American hospitals (defined by those that contain 200 to 400 beds) was over 10%, which is higher than the average claim denial rate of 6-13%. The rise of COVID-19 meant an increase in denial rates, as seen in Harmony Healthcare’s results from surveying hospital reimbursement executives. One-third of the poll’s respondents said their hospital had an average denial rate exceeding the “denials danger zone” of 10%. Change Healthcare found a claims denial rate of 11.1% through the third quarter of 2020. Denial rates as high as this, which is a sentiment also extended to small and medium hospitals, reflect an opportunity for hospitals to reduce their risk of denials by accurately capturing cost and payments in the first round of claims. In doing so, this improves the accuracy and integrity of claims data while also addressing compliance issues during Medicare audits by reducing overpayments.
Other concerns include compliance issues, denials, having to work denials on the backend, looking to be audited by Medicare, and having to return money. Also worth noting is that claim denials have financial repercussions due to how costly they are, causing revenue leakage. In fact, Change Healthcare found that each denied claim cost about $118, contributing to about $8.6 billion in appeals-related administrative costs.
As now established, there are several examples of how current auditing processes in the U.S. are inefficient, causing gaps that allow missed and incomplete claims to slip through, resulting in consequences such as high claims denial rates. Because of the significance of medical auditing in RCM, the issues mentioned above highlight the need for change and improvements in the current auditing process.
One solution takes the form of a more widespread use of pre-bill auditing, which is the process of proactively mitigating risks by implementing an auditing process earlier than in many current auditing cycles. Pre-bill auditing proactively addresses the potential for costly take-backs and denial risks by improving initial claim submissions. This makes it more likely for hospital claims to be accurate, paid, and accepted, and it also saves healthcare facilities both time and money.
The effectiveness of pre-bill auditing is seen in a large academic medical center located in northeast America, which reviews cases as part of a two-part denial prevention strategy. They use pre-bill reviews for high-dollar inpatient cases, and conduct post-bill reviews for low-dollar ancillary claims, which add up to large amounts. “In the single fiscal year since the medical center began performing the pre-bill reviews, it has been able to identify and recoup $11 million in revenue that would have been unclaimed and written off had the reviews not been performed.”
A key factor behind the small number of hospitals implementing pre-bill auditing is the low hospital clean bill rates. These reflect the percentage of error-free claim submissions that are a result of factors such as high claim denials, low revenue integrity, and patient payment errors. This can be combated by another technological upgrade: in this case, software with healthcare RCM process transformation capabilities. With this, they can positively impact hospitals and RCM teams by providing solutions for many of the current claims issues that result in missed opportunities.
Pre-bill auditing is the most efficient method to ensure more accurate, higher quality, and timely audits. The current workforce shortages are amplifying the impacts towards the current manual and inefficient audit process inhibiting claims review before a patient is discharged from the hospital. To utilize pre-bill auditing at a consistent rate, sophisticated technology, specifically, AI-powered auditing software, is a necessary asset in order to maximize audit quality.
AI and Machine Learning (ML) technology give medical auditors the ability to evaluate the longitudinal patient record trends from the last 12-24 months. Additionally, this technology aids in the early detection of data quality opportunities to enable revenue cycle teams to take a proactive measure to efficiently improve the quality of claims to reduce the current high claim denial rate. Moreover, pre-bill auditing enables an in house audit tool to save money for hospitals by streamlining the current audit process to reduce the need for third party vendors, and address pressing RCM and HIM workforce challenges. As a result, AI and ML technology within the current audit process can ensure claim integrity and compliance within pre-bill audits to empower RMC and HIM staff to efficiently source data quality opportunities to address high denials, incorrect diagnosis-related groups (DRGs), and non-optimized DRGs. In doing so, hospitals can receive accurate reimbursements and reduce inaccuracies in the current audit process.
The Semantic Health Information Platform, specifically, the Semantic Auditor, is worth considering as a worthwhile solution to the auditing issues discussed above. AI is able to scan 100% of clinical data, allowing for much higher auditing rates than current, manual processes capable of only 10-20%. By sourcing data quality opportunities and assessing the overall relative weight value, auditors are able to scout out data quality opportunities to ensure accurate reimbursements and thoroughly access CC/MCC cases, SOI levels, and validation of APR and MS-DRG values. With AI-powered auditing technology, RCM and HIM teams are able to boost efficiency while hospitals can accurately and effectively ensure more accurate reimbursements and relative weight values.
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Semantic Health helps hospitals and health systems unlock the true value of their unstructured clinical data. Our intelligent medical coding and auditing platform uses artificial intelligence and deep learning to streamline medical coding & auditing concurrent with patient admission, improve documentation quality, optimize reimbursements, and enable real-time access to coded data for secondary analysis.