How Machine First Approach Can Unlock Revenue Cycle Management Transformation

With analytics, AI and automation emerge to be more mainstream, and like every industry, even the lifecycle industry is to gain an advantage.

Fremont, CA: Industries are using the advancements in these technologies to optimize the business process in order to address the current challenges and gain a competitive advantage. The healthcare industry, which presents numerous opportunities to drive transformation, is not an exception. 

Revenue Cycle Management (RCM) is a complex and critical process for healthcare providers. It works with the payment lifecycle for treatments that include patient registration, claim generation, submission of the claim to the payer, and revenue collection for offered healthcare services to the patient. 

RCM has always been very slow and includes tedious manual interventions. Here are a few significant challenges that are faced in RCM.

• Claim denial: Claim denials lead to delayed or reduced payments for the provider. This is pointed out by a higher DSO that impacts the cash flow of organizations.

• Wrong code: To generate and process each claim, the RCM team has to generate lots of codes and data. The payer will deny claims with the wrong codes.

• Timely response: A timely response from payer organization while insurance eligibility verification, pre-authorization, and claim processing is vital for providers to get their payment on time. Most of the provider organization teams do an intensive manual follow-up with payers to track the claims.

• Resource requirement: RCM is a tiring process that includes heavy data processing and sophisticated calculations. 

Organizations traditionally deploy a significant percentage of the workforce to perform these operations.

Technology can unleash the opportunity in the challenge

The advancements in AI and analytics can provide organizations with the best tools and processes to deal with the challenges in RCM. There are three broad goals, such as operational excellence, reduced cost, and improved revenue that the healthcare organizations must strive while optimizing RCM.

Here are some points on how automation, analytics, machine learning, and AI can be leveraged to meet the challenges.

• Insurance eligibility verification: The process involves several laborious and time-consuming tasks that are not done in real-time. E-verification of insurance eligibility enabled by the payer APIs is a solution.

• Patient registration: The front-desk staff is responsible for entering a substantial volume of data, a process in which inaccuracies are bound to creep in. RPA-based data entry and ICR-based solutions for document scanning will improve data accuracy considerably.

• Claim management: Medical billing errors are frequent in this complex process. Integration with EMR, automation in claim generation, and machine-learning-powered denial management systems can drive optimization here.

• Payment posting: Manual verification of EOB increases the cost and time required. AI-based ICR solutions for payment posting, automated data reading from EOB, and ERA driven by ML can improve accuracy and speed.

See also: Top Revenue Cycle Management Companies