Advanced Revenue Cycle Management Solutions in Radiology
Industry-wide, radiology 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) coding, 2) billing, 3) accounts receivable (AR) management, and 4) bad debt/collections. While a 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 through an integrated and secure HL7 or API technology, supported by highly-trained specialists, as a comprehensive solution to today’s RCM workflow.
Exceeding Industry-Accepted Norms for RCM
While it’s the generation of healthcare revenue that gets a lot of the spotlight, billing and collecting that revenue can be the real workhorse. Radiology groups and imaging centers 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 radiology 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 radiology procedures and 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 (HFMA), 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.1 This shift in financial responsibility from government and private insurance payers to consumers has made it imperative for radiology practices and imaging centers to embrace new technology or risk precious margin on the accelerated growth of bad debt.
Four Areas Where Strategic Improvements Will Enhance Practice Revenue
For these purposes, let’s define the lifecycle as two-fold: 1) Patient Access, including prior authorization and insurance verification, coupled with 2) RCM—coding, billing, AR 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 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, imaging centers) and complex diagnoses and procedure options. The balancing act of capturing charges and documentation between locations during a typical day requires juggling talents for providers and staff alike.
Once a patient is seen, the first step is to determine the accurate codes and levels of service. However, whether it’s due to lack of time or chart completion expectations, procedures are often under coded, miscoded, documentation is missing, or level of service is misclassified. This causes severe problems that impact revenue, including denials, underpayment, and abandonment of claims.
2. The Billing Process
One of the most significant pain points for radiology groups can be projecting the billing process workflow and the inability to scale for the fluctuating workload quickly. From charge entry to payment posting, everything can be either overloaded or slowed to a crawl based on staffing within the billing department.
Staffing issues from unexpected changes in employment status, and family leave requests, can all create bottlenecks that slow down claims processing and can impact bottom line revenue. This creates further problems with ancillary responsibilities, such as credit balances and managing contract performance, to aid in future negotiations.
3. AR Management
At a recent Radiology Business Management Association (RBMA) symposium,2 a study (accepted industry-wide) 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 AR process, managing the unpaid or rejected claims can be especially 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 and takes valuable staff time away from higher-value billing functions.3 By automating the claims management process, a solution can ensure an average savings of $7.72 per claim while also saving valuable staff time and lost opportunity that can be redeployed to more essential functions, including improving the overall patient experience.
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 of uninsured patients or unpaid selfpay accounts and end up being written off entirely 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 Processes for RCM
RCM inefficiencies can pose one of the most perplexing problems in radiology practices and imaging centers 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 workflow 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 automation and AI-driven, cloud-based technology to improve administrative workflow makes the most significant advancements. Coupled with highly trained 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. If ongoing education burdens a radiology practice, and personnel-related issues that arise with developing and maintaining a strong coding department, they may want to consider enlisting a third-party partner. A trusted outside team 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:
- A paramount challenge in healthcare, and specifically, radiology, is staying up to date on coding with ICD-10-CM and PCS,
CPT, and HCPCS codes through education and knowledge building.
- Minimize human error and careless keying mistakes that can drastically affect reimbursement by implementing an accountability structure that rewards coding accuracy.
- 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 performed elaborating on medical necessity and level of care requirements,
- Physician or Advanced Practice Provider’s involvement in patient care and the 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 treatment as required by the patient’s insurance provider.
- Capture commonly missed tests that are billable or support treatment/diagnosis decisions.
- Code to the highest degree of specificity and code to the diagnosis, not necessarily the symptom.
- Design and implement a review and audit of the documentation and coding process. Not only does this suggest areas for improvement which directly affect the bottom line, but it also ensures compliance with government regulations and contractual payer obligations.
- 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 quality of the coding and documentation program and feel a personal investment.
The most effective way to address the scalability issue that often stresses the medical billing function is to engage a third-party partner that can assume responsibility for executing all aspects of the billing process. Utilizing a scalable, cost-effective 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
- 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 AR and must be worked individually to ascertain the problem and then collect the necessary information before resubmission.
Denials management is an ongoing problem in many radiology practices. Using an AR Optimization Solution, an AI-driven software with machine learning capabilities, utilizes proprietary recovery prediction algorithms to focus efforts on which denials should be collected first, so that energy is spent on the recovery, increasing early cash flow.
By reducing write-offs and identifying the next best action through automated algorithms based on payer guidelines and procedures, a radiology 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 complete final
collections. With machine learning algorithms, unpaid claims can be evaluated on a number of available parameters, such as aging, payer, and modality.
- Access to predictive and deterministic criteria that 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, date of service, 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.
The complexity of a third-party billing system 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 using the AR Optimization Solution alone.4
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.
Couple that with the growth of patient consumerism and HDHP in recent years, and we see a cascading problem that can only worsen with time. The key may be an early intervention with an AI-driven Insurance Discovery Solution.
When using an Insurance Discovery Solution, 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.
An Insurance Discovery Solution uses AI and machine learning capabilities to scour clearinghouses and insurance databases to identify undisclosed coverage. This is especially useful for Medicaid and commercial insurances, as the solution uses probabilistic analytics and machine learning to identify missing or incorrect information so that patients’ coverage can be properly billed and collected.
Anytime bottom-line results are used as a barometer of radiology group efficiency, performance and the means of 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 more 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 margins.
To learn more about opportunities to improve RCM efficiencies through automation and AI-driven solutions, supported by highly training specialists, visit www.infinx.com.
Let us Optimize Your RCM Workflow
Schedule a call with our team to learn how we can capture you more revenue using technology leveraged by experienced billing and coding specialists.
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.