Innovative Solutions for the RCM Lifecycle in Sleep Medicine

Industry-wide, sleep medicine groups are impacted by an onslaught of changing governmental regulations, insurance payer modifications, and transitioning patient expectations. Our evaluation of the revenue cycle management (RCM) process points to four areas where significant revenue is at risk when not performed efficiently: 1) medical coding, 2) medical billing, 3) A/R management, and 4) bad debt/collections. While a sleep practice may opt to handle these functions in-house with a mix of automated and manual systems, we outline a far superior structure that brings artificial intelligence (AI) and machine learning capabilities using secure technology, supported by certified billing specialists, as a comprehensive solution to today’s RCM process.

While it’s the generating of healthcare revenue that gets a lot of the spotlight, billing and collecting that revenue can be the real workhorse. Sleep medicine groups are facing real challenges as we enter the 2020s, and headwinds are everywhere. Changing and expanding requirements from government entities and insurance companies, new payment models, and RCM issues like rising bad debt and uncollectible amounts leave groups searching for a way forward.

From the macro perspective, maximizing revenue in the healthcare industry’s complex third-party payer system continues to be a challenge. With increasingly elaborate payment models and fee schedules, and escalating payer scrutiny for advanced genetic and molecular testing, it’s never been more critical to stay current with new technological advances in the revenue payment lifecycle.

To add to the complexities, according to the Healthcare Financial Management Association, the CDC has released findings showing that 47% of commercially-insured consumers were choosing a High Deductible Health Plan (HDHP) hoping to reduce household health insurance costs by lowering premiums. This shift in financial responsibility from government and private insurance payers to consumers has made it imperative for practices to embrace new technology or risk precious margin on the accelerated growth of bad debt.

For these purposes, let’s define the lifecycle as twofold: 1) patient access, including prior authorization and insurance verification, coupled with 2) RCM—coding, billing, A/R management, and insurance discovery. By examining RCM’s key functions, we can determine where maximum efficiency could be gained, and previously lost or delayed revenue could be captured.

Let’s look at four RCM components and their inherent issues:

Once a patient is seen, the first step is to determine the accurate codes and service levels. However, whether it’s due to lack of time or chart completion expectations, procedures and testing are often under coded, miscoded, documentation is missing, or service level is misclassified. This causes severe problems that impact revenue, including denials, underpayment, and abandonment of claims.

Staffing issues from unexpected changes in employment status, family leave requests, and hiring mis-queues can create bottlenecks that slow down claims processing and impact bottom line revenue. This creates further problems with ancillary responsibilities, such as resolving credit balances and managing contract performance to aid future negotiations.

In healthcare’s complicated and administratively burdensome accounts receivable process, managing the unpaid or rejected claims can be incredibly frustrating for billing staff. Not only does it create large blocks of unproductive time while staff members sit on hold or wait in insurance payer queues, but there are often limits on the number of claims that can be submitted or questioned during each call.

This manual process is estimated to cost $10.13 and take 12-20 minutes per claim, according to the 2020 CAQH Index. It takes valuable staff time away from higher-value billing functions or improving the overall patient experience.  By automating the claims management process, a solution can ensure an average savings of $7.72 per claim while saving valuable staff time and lost opportunity to be redeployed to more meaningful functions.

RCM inefficiencies can pose one of the most perplexing problems in practices today! Complex approval processes and requirements that differ by health insurance plan make claims management a challenge for even the most well informed billing administrator. It’s not enough to rely on the status quo; groups today must be proactive in developing plans to improve operations and patient access procedures, as well as their RCM to meet the test.

With today’s focus shifting toward maximizing the patient experience and alleviating any roadblocks to collecting timely reimbursement where due, harnessing AI-driven, cloud-based technology to improve administrative workflow makes the most significant in-roads for the expense outlay. Coupled with certified billing specialists, improved automation generates more revenue for less administrative expenses in a reduced time frame.

Medical Coding

Today, it’s imperative to accentuate the process of coding to achieve accuracy and maximize the shrinking healthcare dollar. Suppose the ongoing education burdens a sleep medicine practice- and personnel-related issues that arise with developing and maintaining a strong coding department. In that case, they may want to consider enlisting a third-party partner. A trusted partner can absorb the workflow efficiently and code thoroughly to maximize reimbursement.

The most effective way to address the scalability issue that often stresses the billing function is to engage a thirdparty partner that can assume responsibility for executing all aspects of the billing process. Utilizing a scalable, costeffective automated solution that manages billing complexities while meeting payer criteria ensures accurate claims are submitted, paid quickly, and denials minimized.

By engaging an off-site team to provide billing support, these functions would seamlessly resolve behind the scenes:

Once a patient has been seen and a claim has been coded and billed, there are inevitably denials and rejections that prolong payment if not outright stop revenue capture. These rejected claims make up the A/R and must be worked individually to ascertain the problem and then collect the necessary information before resubmission.

Denials management is often cited as an ongoing problem in many practices. To optimize your A/R, state-of-theart technology with machine learning capabilities is available to utilize proprietary recovery prediction algorithms to focus efforts on which denials are collectible so that energy is spent on the recovery of revenue and increase of early cash flow.

By reducing write-offs and identifying the next best activity through automated AI-driven algorithms based on payer guidelines and procedures, a group can be assured the maximum revenue is collected. When evaluating claims management solutions, consider these AI-driven software functions critical to achieving the long-term goal of permanently reducing revenue loss from denied claims:

• • The ability to predict recovery, including forecasting the dollars potentially available and the timeline to achieve final collections. With machine learning algorithms, unpaid claims can be evaluated on several available parameters, such as aging, payer, and modality
  • Access to predictive and deterministic criteria prioritize follow-up strategy activities to maximize and focus human intelligence efforts where they can be most effective.
  • Automated claim status checks matched with the most-likely cause, i.e., integrated insurance verification and eligibility data, CPT mismatch technology, and DOS and benefits check capabilities. Once the cause is identified, appropriate changes are made, and the claim is resubmitted.
  • Auto-creation of required appeal letters, if necessary.
  • Automated eFax capabilities, when required.
  • The ability to perform a root cause analysis through operational analytics to find where mistakes originate upstream, including insurance verification, prior authorizations, or coding problems, so that processes can be reviewed and upgraded where necessary.
  • Adaptability so that if additional areas are identified as automation candidates, integration is possible with ease.

A third-party billing system’s complexity requires diligent review and follow up when revenue is held up, and the bottom line is affected. With the technology available today that harnesses AI-driven automation, machine learning, and predictive analysis, each patient encounter can be verified, submitted, and followed up on in real-time. As reported in a recent Infinx Case Study, it’s conceivable to recognize a +15% improvement in 90+ days collections from A/R Optimization alone⁽²⁾.

One thing we know for sure—throughout the healthcare spectrum, patients frequently present for care without understanding their insurance coverage or benefits. Phrases like “annual maximums,” “remaining deductible,” and “explanation of benefits” may be overwhelming to patients that are unfamiliar with insurance terms and how they’re treated within the industry. The fact that your organization has ended up with an outstanding amount is often not from misrepresentation but simply misunderstanding.

Coupled with the growth of patient consumerism and HDHP in recent years, we see a cascading problem that can only worsen with time. The key may be early intervention with a cloud-based, AI-driven Insurance Discovery Solution.

With an AI-driven Insurance Discovery solution, these accounts are processed through an automated coverage identifier package where patient demographics, insurance profiles, and benefits are verified, and undisclosed coverage is identified. These uncompensated accounts can then be submitted to the appropriate insurance and revenue retrieved.

One such solution may be initializing an insurance discovery process that uses AI-driven automation to scour clearinghouses and insurance databases to identify undisclosed coverage. Especially useful for Medicaid and commercial insurances, an Insurance Discovery software uses probabilistic analytics and machine learning capabilities to identify missing or incorrect information so that patients’ coverage can be properly billed and collected.

Anytime bottom-line results are being used as a barometer of group efficiency, performance and getting there are critical to the evaluation. By looking at RCM practices honestly and seeking clarity on improvements that can make long-term sustainable gains, reducing the administrative burdens associated with reimbursement in a third-party payer system leads to greater recognized revenue and better patient experiences overall.

Instead of accepting ongoing operational shortfalls, each of these solutions brings increased administrative efficiencies that allow for a more smoothly run operation. Overall, this provides higher cash flows, faster recovery times, reduced aging, and improved stakeholders’ margins.

To learn more about opportunities to improve your sleep medicine groups RCM efficiencies using advanced automation and AI-driven software, supported by experienced billing specialists, visit www.infinx.com.

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