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.
Exceeding Industry-Accepted Norms for RCM
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.
4 Areas Where Strategic Improvements Will Enhance Sleep Medicine Revenue
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:
1. The Medical Coding Process
Since its release in October 2015 and the myriad subsequent changes and updates, the ICD-10 coding system presents a multifaceted and intricate set of issues, especially for a specialty that utilizes different care venues (practices, hospitals, outpatient centers) and complex diagnoses and procedure options.
Primary care providers, seeking to improve patient care and outcomes, are finding more opportunities to use sleep medicine early to benefit the patient. This, in turn, creates growing problems for insurance payers who are trying to contain costs and ensure medically sound and appropriate patient care is being delivered.
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.
2. The Medical Billing Process
One of the most significant pain points for sleep medicine groups is projecting the billing process workflow and the inability to scale for the fluctuating workload. Everything can be either overloaded or slowed to a crawl based on staffing within the billing department, from charge entry to payment posting.
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.
3. A/R Management
At a recent healthcare business management symposium(1), a study accepted industrywide by the Medical Group Management Association (MGMA), estimates the cost to rework a claim that has been denied by insurance is $25.00 for each occurrence. And even more impactful is the fact that between 50% and 65% of denied claims go unchallenged due to lack of time and/or understanding on how to proceed with the revenue thereby lost completely.
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 2019 CAQH Index. It takes valuable staff time away from highervalue 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.
4. Bad Debt and Collections
The most frustrating component of RCM is bad debt, and those accounts that must be reluctantly turned over to a collection agency for potential follow-up. These claims are often overdue because uninsured patients or unpaid self-pay accounts are being written off completely or categorized as charitable care. Bad debt can total as much as 15% of total receipts in today’s practices, and that number is sure to grow as patients assume more and more of the financial obligations for care unless something is done to curb this trend.
Best Automated and AI-Driven RCM Processes
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.
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.
To fully capturing revenue due, whether in-house or with a third-party partner, consider:
1. A paramount challenge in healthcare, and specifically, sleep medicine, is staying up to date on coding with ICD-10CM and PCS, CPT, and HCPCS codes through education and knowledge building.
2. Minimize human error and careless keying mistakes that can drastically affect reimbursement by implementing an accountability structure that rewards coding accuracy.
3. Ensure medical documentation is not only present but accurate and complete with every patient encounter. Payers request this information and will deny claims based on inaccurate, untimely receipt of documentation so be sure to include the following:
- Procedures and testing performed elaborating on medical necessity and level of care requirements,
- Physician or APP’s involvement in patient care and level of service performed,
- Tests ordered and corresponding results with treatment prescribed,
- Billable supplies and equipment used throughout patient treatment,
- Referrals, both incoming and outgoing, and
- Prior authorizations for all procedures and testing as required by the patient’s insurance provider.
4. Capture commonly missed tests that are billable or support treatment/diagnosis decisions.
5. Code to the highest degree of specificity and code to the diagnosis and not necessarily the symptom.
6. Design and implement a review and audit of the documentation and coding process. This suggests areas for improvement that directly affect the bottom line and ensure compliance with government regulations and contractual insurance payer obligations.
7. Make sharing knowledge and training as it becomes available as a core foundation of organizational operations. Everyone impacted should participate in comprehensive opportunities to improve the coding and documentation program’s quality and feel a personal investment.
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:
- Charge Entry
- Payment Posting
- Credit Balance Resolution
- Contract Management
- Reporting and Analytics (designed for financial management)
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).
Insurance Discovery to Improve Bad Debt and Collections
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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- Marrow, KW. But Will it Get Paid? Coding Strategies. RBMA 2019 Paradigm Symposium, April 14-17, 2019, Colorado Springs, Colorado. Accessed February 14, 2020.
- A Leading Outpatient Imaging Provider Improved Collections from 90+ Aged Denials Using Infinx’s Technology-Driven Approach. Accessed February 20, 2020.
Infinx provides innovative and scalable payment lifecycle solutions for healthcare practices. Combining an intelligent, cloud-based platform driven by artificial intelligence and automation, with our trained and certified prior authorization, coding and billing specialists, we help clients realize revenue, enabling them to shift focus from administrative details to billable patient care.