How AI Can Increase the Success of Clinical Denials Appeals

Steve Albert, EVP and Chief Product Officer, R1

It’s a perfect storm of financial pressures facing healthcare provider organizations – from rising costs to labor shortages to constrained capacity – that stymies revenue growth. Growing challenges with payer payments only exacerbate these issues. According to a Kaufman Hall report, 73% of leaders surveyed said claims denials, which was the top revenue cycle issue in 2022, had increased in 2023.

The cost of denials is staggering. A recent data analysis revealed that providers spent nearly $20 billion in 2022 on efforts to resolve delays and denials across payers. More than half of the total – about $10.6 billion – came from denied claims that were appealed and ultimately paid. 

The traditional approach for providers has been to rely on medical professionals, including physicians and nurses, to help capture revenue at risk in denied claims by writing appeals. The need to hire clinical expertise to pursue such claims further adds to already substantial costs. The average cost to challenge a $43.84 denied claim will increase by $13.23 for a general inpatient stay and $51.20 for inpatient surgery. With an average of three rounds of appeals, providers are often waiting up to six months after care is delivered to receive payment, which can impact the ability for providers to maintain operational balance sheets. 

Moreover, there is a cascading effect on patients, increasing stress and detracting from the patient experience. For example, if a patient undergoes an outpatient procedure, such as a knee replacement, but then experiences complications that result in an overnight stay, the patient may not even realize their stay could be at issue. Even though the overnight stay was necessary to address complications and prevent deterioration, if the claim is denied, balances may become the patient’s responsibility, and the steps in the process are often confusing for patients. 

Prevent denials upfront

Providers now have the option to apply modern technology – including analytics, automation, and AI – to help improve their claims management processes to not only prevent denials upfront but also identify improvements to continuously hone processes, and efficiently resolve denials that can be overturned. Throughout the process, AI augments human expertise to reduce denials, reduce AR days and improve financial performance.  One method in which AI technologies can help organizations is with coding accuracy and compliance, so claims can be submitted along with appropriate documentation, resulting in fewer denials. 

The typical process for appealing a clinical denial is time-intensive, often requiring multiple rounds, resulting in long payment delays. Using AI technology to ingest, parse, and summarize text portions of the patient record can speed up and improve the entire process. AI-enabled analytics can pinpoint likely denials as well as identify trends by payer, clinical indication, etc. Providers can then focus more attention on those denied claims that are most likely to be successfully overturned. 

AI technologies also equip organizations with valuable data. Leveraging advanced analytics tools, organizations can identify areas for improvement to continuously optimize claims processes from start to finish. With a focus on preventing delays and denials, insights gained from both successful and unsuccessful appeals can be applied to better substantiate each claim upfront in the ever-changing payer landscape. 

Resolve denials accurately and efficiently

For each appeal, a clinician must formulate a strategy by reviewing a patient’s chart – potentially hundreds of pages of history, notes, and summaries – to assess the patient’s situation, treatment, existing conditions, and comorbidities. In minutes, AI can review the patient record and summarize all pertinent information for the type of appeal required, including the key identifiers, an accurate clinical summary, and the clinical argument to substantiate the claim. 

In addition, with manual chart reviews, people may miss key details or overlook important trends. Today’s AI technologies can efficiently and accurately go through the entire patient record in minutes to identify the points critical to depicting the complexity of the patient case. AI doesn’t get tired or experience stress – it can consistently and reliably pull together the data points needed for an effective appeal. 

Using AI in this way makes clinicians appeal editors rather than appealing authors. Instead of reading hundreds of pages and writing from scratch, clinicians review and fine-tune the appeal drafted by AI to ensure it presents a compelling, accurate case to the payer. By integrating people services and technology capabilities, the time to resubmit claims can be reduced from hours to minutes – upwards of 75% in time savings. Such time savings on administrative and medical staff offer the added and critical benefit of enabling clinicians to focus on applying their expertise at the top of their license, which reduces their burden, relieves burnout, and improves job satisfaction and staff retention. 

Overcome AI adoption challenges 

Provider organizations evaluating AI solutions for the revenue cycle need to consider governance, change management, as well as policies and procedures to overcome common adoption challenges, including:

Concern that AI will replace jobs: The best approach – proven by AI’s successful use by leading health systems today – is for AI to support, not replace, human decision-making. Providers should involve end users and other stakeholders right from the start to understand the most pertinent issues, build a solution that truly benefits end users, and gain buy-in along the journey. Compliance and patient privacy: Publicly available solutions, such as ChatGPT, can put organizations at risk. However, adopting a robust framework and a closed environment enables providers to build upon their policies and procedures to productively manage patient privacy and compliance. 

Transform processes for sustainable growth

Healthcare organizations that adopt AI for high-value, high-cost administrative processes, such as clinical claims denials, will be better equipped to navigate today’s healthcare challenges. AI-enabled technologies can help organizations improve efficiency and clinician job satisfaction, more successfully resolve denied claims and apply their success to prevent future claim denials. In addition, AI can make the process more seamless for payers by making appeals more consistent and accurate, requiring fewer iterations. Most importantly, healthcare providers spend more time treating patients, and those patients receive better experiences – from care to cost. All told the resulting increases in revenue and cash flow can put healthcare organizations on a stronger footing for sustainable growth, supporting better outcomes for all. 

About Steve Albert

Steve Albert is Executive Vice President and Chief Product Officer for R1. He joined R1 following the acquisition of Cloudmed where he also served as Chief Product Officer. Steve has over two decades of leadership experience in new market development and product innovation for enterprise-scale data management and analytics organizations. He leads R1’s product vision and roadmap, drives product innovation, and helps grow the company through expansion into new markets. Prior to joining Cloudmed, Steve held product and market development leadership roles at 1010data, Mastercard, Equifax, and GeoPhy. He has extensive experience leading and scaling go-to-market, product, and data science teams that delivered product-led revenue growth.