By Roni Berlin, BSHCM, CPC, CPB, associate vice president at ExdionRCM
You entered the healthcare field to make people better, but there’s a grim chance a cancer deep within your revenue cycle management system is the cause of lost revenue, longer work hours, and other back-office stress issues you never envisioned for your career. The tumor that’s bleeding your practice is the dreaded coding error, and the cure to your coding problems is much easier and more affordable than you think.
Next generation revenue integrity software solutions, infused with artificial intelligence (AI) and machine learning (ML) systems, are designed to automatically recover lost revenue due to coding errors. Properly integrating with revenue cycle management platforms, they can greatly impact an organization’s bottom line – but selecting the right one can be time-consuming and confusing. It’s important to understand these systems and select the right solution for your practice. Asking the right questions and understanding the most important features a solution offers are key when meeting with a vendor for a demonstration.
This should not require a large expense: Select a solution based on the operating expense (OPEX) model, which calls for an easy, affordable monthly spend. Gone are the days when technology was traditionally a capital expenditure (CAPEX) that involved high business risk and needed multiple approvals. With technology maturity and the democratization of hardware and infrastructure, complex AI and ML systems, which were once only affordable for a handful of behemoth healthcare organizations, are now available and affordable for the independent physician.
Return on investment should align with your goals. ROI should be clearly reported by considering Medicare fee schedule, as well as the client’s own charges allowed and payments to quantify top line improvement. This assessment should not be left for you to do. It should be crystal clear from day one.
It should easily pay for itself: The next level of technology maturity is outcome-based, where you only pay when you win. Technologies that offer outcome-based models are significantly invested in the practice’s success. The solution should easily pay for itself at a fraction of what you recover.
You want intuitive, proactive coding customized for your organization: This is the magic of AI, driven by ML and rule-based systems, which can be “truly” customized for your organization. An automated, robust system proactively provides codes, including missed billable codes, and will not make you guess which codes are correct. It also should be intuitive, providing codes based on the diagnosis and standard of care. You do not want primary rule-based systems with little to no data learning processes nor do you want a generic solution with limiting customization capabilities. You also should avoid solutions that only scrub after coding. The customization is not difficult, and you don’t have to do it yourself.
Integration with other platforms should be easy: The solution you select should easily integrate with your other platforms. If it takes heavy lifting by you or the vendor to plug into them, it’s not next generation software. Of course, the vendor should do the integration.
Onboarding should be easy also: No one likes to learn new software, but new AI/ML are honestly very easy to use. AI/ML coding solutions are not designed to replace people; they are designed to make coding much easier and more efficient much like how newer word processing solutions help you write better content with proper grammar and spelling. There is nothing to fear.
Automated encounter reports – no manual pulling: You want a solution that can pull encounter reports from any system – it should NOT require them to be manually pulled, uploaded, and submitted. You also want to be sure your solution is highly scalable to pull as many encounters as needed. Remember, true AI/ML solutions automate everything.
Clinical Documentation Integrity (CDI) is integral: If the solution doesn’t include CDI, it simply isn’t next generation and is leaving you short. A robust CDI should help dramatically slash claim lag. Look for an integrated automated provider querying workflow that also learns and identifies patterns, assists in recounting procedures rendered but not documented, and assists with correct documentation to maximize compliance and reimbursement.
Reporting is robust, clear and easy: How deep and easy is the reporting offered? You want a clear view of measures, including financial impacts as well as accountability. If you’ve got four offices, you should be able to see each one separately as well as the overall organization view.
Standards and expertise matter: Look for extensive AI algorithms backed by a CPC-certified coding team trained as per U.S. standards. Ask the vendor about their specific coding expertise to ensure they understand your type of practice.
The patient experience is better: Billing problems are not only a financial issue for practices, but also for patients. A good solution gets it right the first time, and claims are processed and closed more quickly. For patients, seamless and quick processing of claims helps eliminate confusion and delays that make it more difficult to manage their own budgets.
As a reminder, these are the most important features to discuss and evaluate for next generation AI/ML revenue integrity software. Do not be afraid. The sooner you evolve your coding platform, the sooner you can begin recovering lost revenue and finally eliminate coding stress.
Roni Berlin, BSHCM, CPC, CPB, who serves as associate vice president at ExdionRCM, is a recognized expert in business strategy and analysis, medical coding, collections, and client support. The company’s solution, ExdionACE, is a powerful yet simple AI-powered coding, revenue integrity, and CDI (Clinical Documentation Improvement) solution.
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