RCM and its importance within the healthcare system

Revenue cycle management (RCM) is the process used by healthcare systems to track revenue from patients. It is a cycle that describes revenue and payments for a healthcare provider across the life cycle of a patient from admission (registration) to the final payment disbursement from the payer or insurer.

A McKinsey 2021 Analysis of Health Services and Technology (HST) (source: https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/how-healthcare-services-and-technology-companies-can-boost-productivity-with-data-and-analytics) predicts that RCM would have the highest growth rates relative to all other functions within payers, providers, and life sciences.

Given its importance, it brings forth three key focus areas for providers

1. Improving the patient’s financial experience with pricing transparency

While this may be table stakes in many industries, it is quite unusual in the healthcare sector to accurately estimate treatment costs in advance.  And hence, there has been an advent in automated, machine learning-based price estimation tools that estimate patients’ out-of-pocket costs before they receive care. The system automatically retrieves real-time eligibility and benefit data from the patient’s insurer and combines this with charges and contracted rates to create an estimate of out-of-pocket costs unique to a specific patient. The technology gathers and learns from insurance claims to improve the accuracy of estimates over time. Needless to mention, that in the absence of AI enabled automation, creating the estimates would be an extremely manual process of marrying and reconciling disparate information from numerous systems. It is of no surprise, that successful implementations have resulted in almost 70% of the estimates are calculated without any human touch.

2. Optimizing net revenue

Having the ability to discuss payment options in advance of care, leads to 60-100% improvements in point-of-service collections across various clinics and hospital departments. Physicians are also happy with estimates being provided in advance, because it leads to fewer cancellations of procedures on the day of service. With the U.S. government mandating advance estimates for care, it isn’t a distant reality when estimate systems will be made available in an online self-service format so price shoppers could obtain their own estimates when evaluating where to receive care.

3. Reducing cost to collect

Most medical coders can now take advantage of AI-enhanced computer-assisted coding systems such as TP Optify to identify and validate the correct codes. Grappling with the thousands of medical codes in the International Classification of Diseases or ICD-10 can be a heavy cognitive load as well as error prone for the RCM workforce. If a claim doesn’t include the right codes, it could get held up, rejected, and then sent back to the provider for correction. Every time this occurs, precious staff time and resources is wasted on rework. Adding artificial intelligence can definitely speed up work while reducing mistakes. Using the right codes, the first time, every time, is the key to getting reimbursed in a timely fashion from payers for services rendered by providers. 

AI opportunities galore across the RCM value chain, to influence these focus areas

1. Patient Pre-Authorization

Converting partially electronic prior authorizations to fully electronic transactions presents the largest per-transaction savings opportunity. Manual prior authorizations costed $10.26 per transaction in 2019, according to the 2020 CAQH Index Report, compared with $3.64 per electronic transaction and $7.07 per partially electronic transaction. For health insurers, the cost equates to $3.14 per manual transaction, compared with just 12 cents per electronic or partially electronic transaction (those taking place through a portal).

2. Eligibility & Benefits Verification

Insurance eligibility verifications and benefit checks are one of the first steps in the healthcare revenue cycle, and unfortunately, are also one of the first places that inefficiencies occur, causing downstream revenue cycle problems in the form of claim denials. At an average, 24% of all claims denied are simply because of eligibility and registration issues. When you hand off insurance eligibility verifications to intelligent automation, benefit checks can be done more frequently and more accurately than humanly possible.

3. Claims Submission

Using artificial intelligence to automate administrative claims processing is really just the tip of the iceberg. In fact, many high-volume and low-cost claims with legal and technical checks (which must be made before a claim can be accepted) have been mostly automated for decades. But what AI allows insurers to do, is expand their capability for automation to even more complicated claims, where decision-making is key. Machine learning allows the AI software to study behavioral analytics and customer data to make more accurate decisions on whether a claim is genuine, and this can be applied to ever more nuanced claim types. The biggest hurdle for claims managers at the moment will likely be in ensuring that AI technologies and practices can be smoothly integrated into current ways of working.

4. Payment Posting

Preparing a payment file, submitting payment batches for approval, viewing remittances, researching payment history, etc. can all be automated. A fully electronic payment process in accounts payable means every step of paying hospital suppliers and insurers can be done safely and remotely. It additionally simplifies reporting too, benefiting everyone from Accounts Payable managers to controllers. Another equally important reason to upgrade legacy payment systems is to mitigate fraudulent activity.

5. Denial Management

While an estimated 67 percent of denied claims are appealable by the providers, as many as 65 percent are not reworked because the time and expense of reworking the denials manually would be more than the revenue they could generate. The best solutions for denial management in medical billing are those which catch a mistake before it occurs. Applications of automation could range from correcting errors from duplicate billing, incorrect CPT modifiers and inaccurate patient demographic information.

6. Accounts Receivable Follow-Up

From our experience, process reengineering led automation can influence the entire AR workflow starting with sales and delivery (communication with the customer, suppliers and plans), invoices (sending invoices), payment collection, and reconciliation. Mapping it out first, helps to decide what to streamline and automate, and what unnecessary steps can be eliminated reducing payment friction and simplifying billing interaction. These have an immediate and direct impact on key KPIs, such as days sales outstanding (DSO) and collection effectiveness index (CEI), as automation drives the process of which customer to charge, how to charge, when to charge, and what to charge for.

7. Reporting

Health systems must first centralize the collection, cleansing and storage of different sources of data. Tying individual charges to outcome data is essential to improving quality and optimizing workflow. TP’s analytical offerings can accelerate the revenue cycle by tracking patient revenue from the initial appointment to the final payment. By doing so, critical RCM success factors such as charge lag, bill lag, average time in AR and denial rate can be audited, improved and sustained.

Summarizing, it is clear that Healthcare RCM can be transformed through AI

RCM is a clear candidate for intelligent automation through AI, since it consists of repetitive and inefficient processes that add to the administrative waste burdening of the healthcare system. The use of AI allows providers the ability to improve and therefore speed up the billing process, and hence usage of digitization for targeted interventions in the healthcare industry will only increase over the coming years. TP’s RCM value added services can help in eliminating the time-consuming and inefficient back-and-forth process of treatment authorizations and appeals in favor of something more automated and efficient.