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April 3, 2020

As analytics, AI and automation become more mainstream, every industry is utilizing advancements in these technologies to optimize business processes to address current challenges and gain competitive advantage. Healthcare, which presents multiple opportunities to drive transformation, is no exception.

A case in point is Revenue Cycle Management (RCM), a complex and critical process for healthcare providers. It deals with the payment lifecycle for treatments, which includes patient registration, claim generation, submission of claim to payer and collection of revenue for offered healthcare services to the patient. 

Painfully slow and tedious manual interventions have traditionally been the bane of RCM. Based on my experience with various healthcare providers, I have listed below the major challenges faced in RCM.

Claim denial: Claim denials result in delayed or reduced payments for the provider. This is indicated by a higher DSO that impacts the cash flow of organizations.

Wrong code: For generating and processing each claim, the RCM team needs to generate lots of codes and data. Claims with wrong codes are more likely to be denied by the payer.

Timely response: Timely response from payer organization during insurance eligibility verification, pre-authorization and claim processing is crucial for providers to get their payment on time. Most of the provider organization teams do intensive manual follow-ups with payers to track claims.

Resource requirement: RCM is a tedious process that involves heavy data processing and complex calculations. Organizations typically deploy a significant percentage of the workforce to perform these operations. 

How Technology Can Unlock the Opportunity in the Challenge
Advancements in AI and analytics now offer organizations the best tools and processes to meet the challenges in RCM. Operational excellence, reduced cost and improved revenue — for me, these are the three broad goals that healthcare organizations should strive for while optimizing RCM.

The common thread in the challenges is the volume of manual work that is needed now. Operational overheads, high cost and decreased revenue are the results of the quantum of manual work. Let us consider how automation, analytics, machine learning and AI can be used to meet the challenges and capitalize on the opportunities in each stage of the RCM journey.

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

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

Pre-authorization: The process, which has a significant impact on denials, is documentation-intensive with a long paper trail. This can be made more efficient through process re-imagination and electronic pre-authorization solutions like web crawling to a payer’s website using provider credentials or payer’s APIs.

Charge entry and medical coding: The error-prone and expensive process can be optimized through rules and analytics-powered medical coding solutions.

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 increase 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.

Collections and follow-ups: The volume of transactions and need for manual verification of follow-ups add complexity to the process. RPA-based solutions for manual tasks and analytics-driven process optimization will drive transformation in this stage.

Underpinning all these opportunities is the ‘Machine FirstTM’ approach that gives technology the first right of refusal to sense, understand, decide and act on the challenges in the RCM domain.

The A Team
Automation, AI and analytics make up the A team that can take provider organizations all the way in the RCM transformation journey. Machine First is the guiding philosophy that will help healthcare providers choose the technological solutions that will deliver the best results. 

Provider organizations also need to

  • identify the hotspots of inefficiency
  • lay out a roadmap for building AI and analytical capabilities
  • foster a culture of continuous monitoring and improvement.

The prize: sustained RCM transformation. 

Get in touch with us at (  to discuss ideas on RCM transformation.

Ajay Krishnaswamy leads the Healthcare Provider segment within TCS and has spent the past 10 years partnering with large Healthcare Providers in their transformation journey. Prior to the current role, he has engaged in a variety of projects ranging technical, consulting, and sales over the past 22 years in TCS. The development and implementation of a custom RCM platform for a leading Specialty Provider - that is handling close to USD 10 billion worth of claims every year, and has been designed as a touchless system – is one of the key successful programs in Ajay’s portfolio


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