AI in Revenue Cycle Management: Streamlining Billing to Cash

Did you know hospitals make an annual loss of $5 million due to unresolved claims denials?

The revenue cycle of most hospitals is nothing short of a mess. From reimbursement time of 45-60 days, the money is often late or simply accounts for loss if not addressed. However, claim denials are not the only reason for the hospital’s revenue cycle mess; in fact, some studies suggest that around $250+ billion is lost due to administrative complexities or inefficiencies.

While many healthcare practices are adopting Revenue Cycle Management (RCM) software solutions to streamline their finances, they simply fall short due to many complexities. And those who have tried in-house solutions have finally admitted to the complexities that go around in claims submission and inefficiencies, etc. 

However, some of the practices have taken a leap of faith and adopted an AI RCM automation solution, and surprisingly, have seen a steady increase in clean claim rates by a staggering 95%.

Sounds surprising to you, too?

Well, it is indeed true that billing automation AI solutions can literally simplify billing processes for you, and with a claims submission agent in place, it can even do what you want, just like you would do it.

Now, you have all the reasons to believe that this is all a little too far-featured, and it is now up to me to convince you of AI RCM automation solutions and, streamline your revenue cycle and improve the financial health of your practice.

On that note, let’s see how!

AI-Ready RCM Stages: Identifying Automation Opportunities

Every healthcare practice is different, and their requirements for claims submission and reimbursement requirements also vary from that of a generic healthcare practice. Now, AI is a wide concept, and for automation, you need to identify the opportunities that might be explored with AI RCM automation solutions. 

1. Patient Registration & Eligibility Verification: Manually verifying a patient’s insurance and eligibility can be a time-consuming process, and the chances of error are also high. Here, billing automation AI can automate the insurance verification process, collect the necessary demographic data, and also set the system for the prior authorization process as well.

2. Charge Capture & Coding: Coding and charge entry automation can improve coding accuracy and ensure that the documentation required for coding and charges is complete. This automation can be used for revenue leak prevention with AI.

3. Claims Management & Submission: Managing claims and their submission is a process that has a lot of processes in it, like claims scrubbing, error detection, and optimal submission timing, depending on the requirement. This presents you with a perfect opportunity to deploy a claims submission agent that can fast-track claims management and submission with accuracy.

4. Denial Management & Appeals: If stats are to be believed, the chances of a healthcare claim being rejected is somewhere between 5% and 10%. This is where RCM denial tracking powered by AI can come into the picture. With an AI-powered denial pattern analysis, these claims can be easily managed, and revenue leak prevention AI can automate the appeal generation by eliminating the root cause of denial.

Comprehensive AI Strategy: Pre-Claim, Claim & Post-Claim Automation

Claim submission is quite a lengthy process. At first, there is the pre-claim process that you need to streamline, then actually file the claim, and once they are filed, post-claim processes kick in, such as follow-ups, analysis, etc.

Now, here, a customized AI RCM automation solution gives you enough opportunities to instill itself in the claims submission process. Let’s see how you can leverage the extensive capabilities of a claims submission agent:

1. Pre-Claim Intelligence: The processes that come with pre-claim submission are patient eligibility verification, validating patient’s insurance coverage, and ensuring that they have prior authorization in place before the service is actually delivered. Here, a claims submission agent can easily verify, validate, and ensure everything before claims submission.

2. Claims Optimization: One of the aspects that has a huge margin of human error is claim filing. Just like in the prior process, an AI-powered claims submission agent can automate claim creation, validate and automate the coding of CPT and ICD based on the documents, and submit the claim in time to avoid any denials.

3. Post-Claim Management: Once the claims are submitted, the post-claim management process involves tracking and analyzing the claim in case of denial and automating follow-up for outstanding claims. Seems like a tiring process, right? Well, it is, and that is why RCM denial tracking with claims submission agents can assist you in each and every process.

Speed & Accuracy Revolution: Faster Reimbursement Through AI

If you have been following this article, then you must know that billing for healthcare in this age is quite a lengthy and time-consuming process. Moreover, you are somewhere at war with time, so ensuring accuracy and timing is essential. And speed and accuracy being some of the core competencies of an AI RCM automation solution, here are some ways in which it can bring speed and accuracy to your billing process:

1. Clean Claim Rate Optimization: While scratching the surface of the internet about AI systems submitting claims, some case studies suggested that 95% acceptance can be achieved. Though it seems a little far-featured, it can be done through intelligent error detection and correction.

2. Accelerated Payment Processing: For accelerating the payment process, automated payment posting is not enough; you also need to reconcile EOB (explanation of benefits). For this, along with a claims submission agent, you would need an EOB GenAI parser so that payment processing can be accelerated.

3. Predictive Analytics for Cash Flow: Analysis is another core competency of AI models. Bringing this into your AI RCM automation solutions, you can easily forecast payment timelines, identify payment collection risks beforehand, and optimize finances accordingly.

4. Payer-Specific Intelligence: As an individual payer, there are certain payer requirements and processing patterns that you are used to. Now, by deploying a machine learning system, payer-specific needs can also be met, further streamlining your billing processes.

Seamless Integration: Connecting AI RCM with EHR & Billing Systems

Data is the foundation of your billing practice. It can literally streamline your practice’s billing process and even set in motion the ideal workflow that suits your practice the best. That is why seamless integration of your EHR system, billing system, and AI RCM automation solutions is necessary.

Let’s see why it is necessary and why you should consider this:

1. EHR Integration Excellence: EHR is the epicenter of data for any healthcare practice, and it might be for your practice as well. Integrating your billing automation AI with an EHR system would enable bidirectional data flow. This is crucial to maintain seamless workflow continuity and bring everyone on the same page.

2. Practice Management System Connectivity: Another reason why you should integrate your AI RCM automation solution with a practice management system is because it enables real-time synchronization. Let me explain, you see, from scheduling to registration and care planning, everything is stored in your system. Now, integration with this system can enable real-time synchronization, further fast-tracking the billing process while ensuring accuracy.

3. Financial System Integration: With a 40 to 60-day period of reimbursement, getting an idea about your practice’s financial performance can be a little overwhelming. This is why connecting your AI RCM automation solution with accounting, banking, and financial reporting systems can provide you with comprehensive revenue visibility to plan things better.

4. Third-party Payer Integration: Integration with major insurance payers and clearing houses can be of extreme use as it enables real-time eligibility verification and gives you real-time updates on claim status.

Cost Recovery & Revenue Optimization: AI-Powered Financial Intelligence

Claim denials often lead to losses, which can negatively impact your practice’s financial performance. However, other than automation and speeding up your healthcare billing process, AI-powered financial intelligence can be utilized for cost recovery and optimizing your revenue as well. Let’s see how:

1. Revenue Leak Detection: When you have an AI instilled in your system, then use it effectively. For instance, with the revenue leak prevention AI module, the system can easily identify underpayments and missed charges and make contractual adjustments after reviewing.

2. Denial Pattern Analysis: Claim denials always have patterns, and it is usually seen in patients whose claims are often denied. In such cases, you can deploy machine learning algorithms to identify the root cause of denials and recommend preventive measures.

3. Contract Optimization: While you’re at it, use AI-powered analysis to analyze payer contracts so that you are reimbursed accurately.

4. Bad Debt Prevention: Bad debt is another indicator that can count your losses. Here, instilling a predictive model to identify collection risks can be of extreme use. Other than that, taking suggestions from it can also help you in implementing proactive intervention strategies.

Frequently Asked Questions