How to Balance AI and Human Expertise in the Middle Revenue Cycle

While artificial intelligence (AI) can automate many functions within the middle revenue cycle, there remains a need for human expertise to manage complex, nuanced decisions to ensure high accuracy and proper context. Ultimately, AI should complement – not replace – humans to drive greater efficiency without compromising quality. 

In revenue cycle management (RCM), the middle cycle refers to the phase between the front end, which is patient access and care delivery, and the back end, which encompasses billing and reimbursement. During the middle revenue cycle, the focus is generally on capturing patient data, documenting clinical procedures and treatments, and ensuring compliance with regulatory standards. 

The greatest challenge associated with middle RCM is often translating the language of a clinical encounter into the vocabulary of the revenue cycle. When it goes wrong, the result may be reimbursement delays, claim denials, and an unsatisfactory patient experience. However, by combining what humans and AI do best, providers can optimize management of the middle revenue cycle. 

Humans and AI: Combining strengths
In the medical field, humans excel at solving problems that require nuanced, complex judgment and interpretation, often informed by prior experiences. In contrast, AI performs well in executing consistent, repeatable, routine tasks that often involve combing through massive amounts of data to identify outliers. AI brings the ability to reduce human error, boost operational efficiency, and reduce costs. 

Given the complexity of the RCM environment, it is necessary to keep humans in the loop. With their inherent knowledge, experience, and critical thinking, humans are better-suited than AI to handle complex cases that may involve intricate steps. 

For example, human empathy plays an essential role in patient communication, such as explaining to patients why they owe what they owe, addressing concerns about affordability, and answering questions. Without a human touch in these instances, providers will have a difficult time delivering positive patient experiences. 

Additionally, human expertise is important when managing responses to new regulations and payer policies, helping providers adapt to changing circumstances by establishing new guidelines and procedures when appropriate. By ensuring strong compliance, providers can reduce the chances of claim denials and accelerate collections. 

3 ways AI can help in the middle cycle
As healthcare providers continue to face pressure to improve the efficiency and accuracy of their RCM, AI offers powerful solutions to optimize the middle phase of the process. By leveraging AI-driven technologies, providers can automate routine tasks, enhance coding accuracy, and predict denials before they happen. These capabilities not only streamline operations but also reduce errors, improve reimbursement rates, and accelerate cash flow. Here are three ways providers can use AI to improve the efficiency and accuracy of the middle revenue cycle:

1. Automation: Repetitive and routine tasks such as claims processing, payment posting, and eligibility verification can be streamlined via AI-driven automation. This helps providers reduce manual errors and frees RCM employees to spend time on more patient-centric activities. 

2. Coding assistance: AI-assisted coding tools use technologies like natural language processing to improve the accuracy of coding by making suggestions and correcting errors of human coders. Human-in-the-loop assistance can help to provide assurance that AI doesn’t over-code or under-code.  

3. Denials prediction: Providers can use AI to review historical claims to predict current claims that are likely to be denied, as well as offer suggestions about how to increase the likelihood of clearance. By identifying potential issues before claims are filed, providers can accelerate the revenue cycle and reduce leakage. 

While AI offers the potential to significantly improve the middle revenue cycle by automating routine tasks, enhancing coding accuracy, and predicting denials, it is important to recognize that certain aspects still require human oversight. The most effective use of AI in healthcare occurs when it works alongside human expertise, combining the speed and efficiency of AI with the critical judgment and experience of healthcare professionals. By leveraging both, providers can achieve a more streamlined, accurate, and efficient revenue cycle, ultimately leading to improved financial outcomes and better care for patients.

By embracing AI’s capabilities and ensuring that the human element remains an integral part of the process, healthcare providers can maximize operational efficiency without compromising on quality or patient engagement. This balanced approach will not only improve financial performance but also enhance the overall healthcare experience, aligning operational goals with the broader mission of delivering high-quality, patient-centered care.

About Dr. Jennifer Weinberg 

Dr. Jennifer Weinberg serves as the Vice President of Operations for R1 Physician Advisory Solutions (PAS). She has been a Physician Advisor with R1 PAS since February 2011 and is renowned for her numerous presentations on admission status compliance, regulation updates, and physician documentation. Dr. Weinberg earned her medical degree from Loyola Stritch School of Medicine in Maywood, IL, completed her Pediatric Internship and Residency at Children’s Hospital of Wisconsin, and holds board certification in Pediatrics. Dr. Weinberg currently resides in Chicago, IL.