By Sarah Mendiola, Vice President of Denials, R1
LinkedIn: Sarah Mendiola, Esq., LPN, CPC, CPCO
LinkedIn: R1 RCM
Claims denials are an issue that do not merely take a financial toll on healthcare providers; they create a ripple effect that delays payment, eats up staff time and resources, and worsens the patient experience.
Of course, the financial expense cannot be ignored. Hospitals and health systems spend an estimated $19.7 billion per year to fight denied claims, according to a recent report from Premier. The report revealed that 15% of all claims submitted to private payers for reimbursement are initially denied.
Among claims that were initially denied, 54% were ultimately overturned and paid, but only after multiple, costly rounds of provider appeals. The average cost of fighting a claim stood at nearly $44, not including the costs associated with added clinical labor, which is estimated to add about $13 to the adjudication cost per claim for a general inpatient stay and $51 to the cost of inpatient surgery.
Often, payers use artificial intelligence (AI) to help make future health predictions, coverage determinations, and denials based on criteria like the accuracy of a physician’s judgement or changing rules. Now, providers can leverage AI to improve their own processes around denials.
Health systems, hospitals, and other providers can today use AI to improve operations and analyze denials patterns to help prevent them. Improved operations mean more accurate billing for patients, leading to higher satisfaction rates and more trust.
Best practices for AI and claims denials management
Properly using AI for claims denials management begins with a shift in mindset. Hospital leaders must get away from the mentality that the revenue cycle is task-oriented, and that team members simply set up tasks and walk away. With AI, revenue cycle management is a continuous improvement process, in which users help the system to learn and become more agile.
Similarly, when it comes to utilizing AI or any other new technology, leaders must identify areas for cross-organizational collaboration and optimization, in addition to taking a holistic view of the revenue cycle to achieve success.
For example, consider using AI for denials both predictively and prescriptively. Predictively, providers can use AI to forecast what might happen to identify trends and issues. Prescriptively, they may utilize AI to make recommended actions.
For AI to provide successful recommendations, forecasts, and trends, it is essential that the system begins with quality data. The more and the better information available to analyze, the more accurately and quickly these advanced systems can respond.
Finally, identifying the right metrics and data to focus on is vital to driving change with AI and to reinforce success. These metrics and measurable data help project leaders showcase the positive changes that AI and automation have delivered to the organization, and provide team members with a sense of accomplishment.
4 ways AI and automation improves denial management
AI and automation can play a pivotal role in denial prevention, management, and recovery by streamlining processes, reducing errors and enhancing efficiency. Following are four ways in which AI automation can be used by providers.
- Preventing denials: AI can help prevent denials by identifying potential issues before claims are submitted. For instance, AI can analyze historical data to predict which claims are likely to be denied based on factors such as diagnosis, treatment and insurance plan. This allows providers to address potential issues proactively, reducing the likelihood of denials.
- Automating routine tasks: Automation can handle repetitive tasks such as insurance verification and pre-authorization, reducing the risk of human error. This not only improves accuracy but also frees up staff to focus on more complex tasks.
- Analyzing denial patterns: AI can analyze patterns in denials, identifying common reasons for rejections. This information can be used to improve processes and training, reducing the rate of denials over time.
- Streamlining appeals: When denials do occur, AI can help streamline the appeals process. AI can automatically generate appeal letters based on the specific reasons for a denial, saving time and improving the chances of successful appeals.
As with any new technology, implementing AI into denials management comes with a learning curve and trial and error. Ultimately, using it effectively revolves around finding the best use cases and applications for an organization. For many healthcare organizations, that begins with using AI for denials management to automate routine tasks, analyze denial patterns, and streamline appeals.