10 Ways AI/ML Can Help Improve RCM Workflow

10 Ways AI/ML Can Help Improve RCM Workflow

By Thomas Donhauser, Felix Chief Revenue Officer

        In the modern world, there are many ways that artificial intelligence and machine learning can improve your business. The medical field is one area where AI/ML has made significant progress in recent years. We have seen how it can be used to help diagnose diseases or predict health outcomes for patients.

In the financial sector, AI/ML has been applied to detect fraud and identify emerging risks more quickly than ever before. But did you know that AI/ML also has a role to play in Revenue Cycle Management (RCM)?

  1. Predictive Analytics

One major use of AI/ML in RCM is for predictive analytics, which can help healthcare organizations identify where they can improve their coding for better reimbursement. Using machine learning and natural language processing (NLP) to analyze claims data can offer a more accurate idea of the risk profile for each patient.

For example, suppose you have a patient who is on Medicare and is admitted to your hospital. You can use AI/ML algorithms to search for such patients’ claims information (such as the number of medical conditions) and cross-reference it with other sources like lab results, doctors’ notes, and medication history. This helps identify the risk level of the patient, which in turn enables more accurate pricing and better revenue.

  • Improve Claims Adjudication Accuracy

RCM professionals can use AI/ML to add a layer of intelligence and speed up claim adjudication. This helps improve the accuracy of claim submissions, enhance reimbursement rates and minimize denied claims, all at the same time.

Also, more accurate claim adjudication can be carried out using AI/ML even in the absence of a medical professional. This comes as a big benefit to RCM executives since it allows them to streamline their processes and save costs at the same time.

  • Improve Decision Making for Claims Submission 

AI/ML can also be used to improve decision-making when it comes to claims submission. By studying a patient’s medical history, lab results, and other relevant data, an AI program can identify those claims that are most likely to be reimbursed with the highest priority.

  • Reduce Claims Abandonment Rates

As Revenue Cycle professionals know, the submission of a claim can become abandoned for a number of different reasons. This is mostly due to the patient’s failure to provide the required information or their inability to pay for services rendered. If RCM teams can use AI/ML to track claims at every stage of the revenue cycle, they can reduce the number of abandoned claims.

  • Improve Medical Billing Accuracy

AI/ML can also play an important role in improving billing accuracy by correcting common transcription mistakes identified during data entry and validation checks. This ensures that there are no delays in claim submission or denied claims due to invalid information.

In addition, AI/ML can be used to optimize the cost-efficiency of medical billing services. RCM professionals are able to use AI/ML to improve medical billing operations by streamlining workflows, reducing human error, and improving their revenue cycle management.

  • Analytics-Driven Workflows

AI/ML can be used to automate data collection and delivery by looking at patterns in data and identifying them as standard operating procedures. This helps to automate workflow activities, reducing the time wasted on administrative tasks.

By identifying workflows that are high-volume and repetitive in nature, AI/ML can dynamically update these activities without any human intervention. It also updates documentation to reflect recent changes in regulatory guidelines, laws, or policies.

  • Enhance Patient Data Quality 

When you opt for AI/ML to carry out data entry, validation checks, and other such tasks, you not only speed up the revenue cycle process but also improve the quality of patient data. This is because it can reduce human error and increase accuracy rates by as much as 80%.

However, it’s important to remember that AI/ML cannot completely replace the role of a medical professional. It is designed to complement human intelligence and not replace it.

  • Improve Patient Outcomes with Predictive Analytics

Going beyond the RCM portion of the healthcare business, AI/ML can be used to predict health outcomes for patients. By analyzing data collected from multiple sources, an AI program can identify at-risk individuals and recommend specific actions that will improve their prognosis.

Furthermore, the use of AI/ML in the revenue cycle can help hospitals with their risk-based contracts. These are contracts that require an agreement between the hospital and a payer for a fixed fee. The insurance company agrees to pay a specific amount to the hospital for a set period based on whether or not they deliver the defined quality of care.

Hospitals can use predictive analytics to help them make better-informed decisions and reduce financial losses related to risk-based contracts.

  • Streamlined Verification of Benefits (VOB)

AI/ML can be used to automatically verify benefits before claims are submitted, based on the eligibility information provided by the patient. This eliminates any chances of manual error that could result in delayed processing or reimbursement.

An eligibility verification solution uses AI/ML to categorize each patient’s benefits in the same way human experts do. It can also take into account special cases like newborns, elderly patients, and others with complex coverage plans. Since this system learns from historic data, its accuracy improves with time.

  1. Optimize Workflows for Better Results

In today’s digital age, the healthcare sector is witnessing a huge boom in electronic health records (EHR) and other such information systems. This has led to an increase in the need for efficient workflow management that helps RCM teams optimize their time and boost revenue cycle performance. Here, AI/ML can play a vital role in streamlining collaborative efforts throughout the revenue cycle process without requiring significant investment.


Artificial Intelligence and Machine Learning are going to continue to play a huge role in reshaping the healthcare industry. Felix Healthcare’s work with providers and other RCM vendors has helped us prove out how we can help organizations save on their labor costs, improve their profitability, and streamline operations. 

The article cited research from Deloitte which predicts that AI will have an overall positive effect on healthcare industry financials by increasing productivity and new service offerings, while also reducing costs.