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Improve Profitability and Cash Flow with AI For Revenue Cycle Management

By Steve Cavolick

AI is used widely within the healthcare industry on the clinical side.  AI applications deliver medical imaging insights, automated diagnoses, and personalized medicine based on disease and genetics.  AI can also be used to drive improvement in the back office operations of healthcare providers, and one area of focus for AI should be in Revenue Cycle Management (RCM). 

Predicting actual versus expected payments is a cornerstone of a sound financial strategy for healthcare providers, as this impacts revenues and cash flow.  However, insurance companies are now initially rejecting 9% of all claims.  These denials total $262 billion per year, or roughly $5 million per hospital per year.  By resubmitting claims, hospitals and providers are able to recover 63% of the denied claims, but at an average cost of about $118 per resubmission.  So even with the resubmissions that are recovered, less than 100% of all claims are reimbursed, and the administrative costs of the resubmitting claims erode the value of those payments.

Claims that don’t get reimbursed are either written off by the provider or passed back to the patient.  The industry knows that people are already paying more out-of-pocket costs than ever before, so providing a positive clinical and financial experience is key to maintaining patient loyalty and protecting your brand from negative online reviews.

One can argue that the easiest way to avoid resubmitting a claim is to avoid the initial rejection.  Coding problems continue to be a headache for providers, as there are over 70,000 billable codes that can be added to forms.  AI can help select the correct codes and then review applications for errors before they are submitted.  Other ways AI can help manage the revenue cycle include:

  • Predict and Reduce Denials: AI models can determine the factors that led to payer denials in the past. This allows providers to predict denials and fix errors on a form before the submission occurs, resulting in higher revenues through a lower denial percentage.
  • Find Weak Links in the Revenue Cycle Process: Patient eligibility should be recorded twice- once at the time of check-in and reviewed again when the claim is submitted. The second check is often ignored.  If a patient changes jobs and insurance companies between the time medical services are provided and the claim is submitted, it will be rejected.  Let AI automate the process of the second check to increase the percent of clean claims submitted. 
  • Predict When Payment Is Remitted: AI can predict how long it will take to process a claim and predict to the day when insurance companies are likely to pay individual claims based on billing code. This helps the overall financial health and cash flow of a provider.

Hospitals and healthcare providers face a multitude of challenges in RCM, but AI can be a solution to hurdles such as improper coding, eligibility verification, and predicting the timing of revenue remittances. 

If you are ready to build AI applications to drive greater revenues and reduce back office errors, our data science team can help. If you’re not quite there yet, the LRS Big Data and Analytics group has over 20 years of experience implementing applications in advanced analytics, information management, and data warehousing.  Not sure how to get started?  Our strategic offerings can help you align business and technology teams, discover the right use case, and determine an ROI.  If you are interested in understanding how we can help you find value in your data, please fill out the form below to request a meeting.

About the author

Steve Cavolick is a Senior Solution Architect with LRS IT Solutions. With over 20 years of experience in enterprise business analytics and information management, Steve is 100% focused on helping customers find value in their data to drive better business outcomes. Using technologies from best-of-breed vendors, he has created solutions for the retail, telco, manufacturing, distribution, financial services, gaming, and insurance industries.