Getting paid is essential to practice success.
Making sure your practice gets paid correctly is one of the most important priorities of any practice leader. In the current healthcare space, 30% of medical practices are closing just this year, and everyone else is struggling to remain financially viable. On top of this, CMS continues to cut physician reimbursement, 3-4% every year despite rising inflation and practice expenses. This isn’t sustainable without making sure every dollar owed is collected.
The problem – it’s tough to get your arms around the large volume of data coming out of medical claims.
Getting paid for healthcare services performed has becoming exceedingly complex. Between making sure documentation is complete, ensuring compliance with ICD10 and CPT coding rules, monitoring numerous claims and remittances, and keeping up with constantly changing insurance plans – there is an overwhelming amount of data that your RCM team needs to keep track of. This is further compounded by the increasing rate of claims denials – 60% of medical group leaders reported an increase in claim denial rates in 2024. Finally, it’s becoming harder for practices to even hire experienced RCM staff with staffing shortages reported in 48% of medical practices – so making their existing team highly efficient by empowering them with advanced data analytics has become even more imperative. Practice leaders need advanced analytics tools that help them crunch numbers, and easily identify and track gaps in RCM performance.
The solution – Use advanced AI-driven revenue cycle management analytics in your practice. Immediately.
Practices are inundated with reimbursement data – from patient and service data, to initial claims, insurance remittances, and payment data. Yet, deploying advanced AI analytics can be yield immediate financial results – from identifying common payment errors; denials from claim errors; and variable payment behavior for standard services. Many of the data fusion and AI surveillance tools that some platforms deploy were developed in much more data intensive industries (think military or industrial engineering) and are now being leveraged effectively to track uncollected revenue and incorrect insurance payments.
Here are three common areas that practices are losing money – and where advanced RCM data analytics can help:
- Patient co-payments. Inefficient collection of patient co-payments at the time when clinical services are being provided can result in significant un-captured revenue over time. In fact, some reports suggest that practices do not collect co-payments at the time of service upto 67% of the time. However, it can often be challenging for any given practice to quantify how much money they are losing in uncollected co-payments and to identify which subgroups of patients that this is occurring in. Furthermore, patient payment collection strategies differ based on whether there are co-payments (to be paid at the time of service) or co-insurance and deductibles (that need to be calculated after service has been provided) – and most practices are lumping all of patient collections work together – when in fact, they should be segmented out. Accurate and real-time data analytics are essential to help practices determine whether or not to implement a front-office solution (which often includes improved workflows and better patient tracking), and to monitor how well the interventions have performed.
- Tracking unauthorized services. Many practices are also seeing a rise in pre-authorization requirements, post-approval insurance denials (even when pre-authorization is performed), and payer take-back of payments for services rendered – as reported by the American Medical Association. Practices need to be able to track specific denial codes representing authorization-related denials (e.g., CO96 or CO197) that may appear in denied claims – identify services that consistently get denied on insurance authorization, to implement improved pre-authorization clearance, quickly appeal denials based on authorization where appropriate, and adjust practice behavior to reduce delivery of unauthorized services when not covered by insurance plans. Finally, given the rise of payer take-back behaviors, practices need to be able to monitor their data from the prior 6-12 months to ensure that they have not had unauthorized service denial codes retroactively applied to their claims.
- Claim underpayment. Underpayment, when an insurance company pays an amount that falls short of the full contracted service provided, can be very difficult to detect. MGMA and others estimates that this can represent up to 7-11% of a practice’s revenue. Detecting underpayment can be very difficult, requiring practices to careful match claim payments to either historical expected payments for those services or compare their payments to their contracted rates. This an almost impossible task given the high volume of claims being handled by many practices, and the labor-intensive work required to track and compare claim payments. This is an area where advanced AI-driven RCM analytics tools are essential in being able to crunch a high volume of claims data and extract those claims that have been paid at rates lower than expected.
This is where MicroMD comes in. We’ve partnered with RevOps Health to make their next generation AI-driven revenue cycle management data platform available to track down when insurances aren’t paying our customers properly. Their team of engineers, data scientists, and healthcare professionals knows exactly where to look – and are making it so that it takes your team just a few minutes to find and track gaps (and money lost) in your RCM data. Are you AI-ready to take back control of your revenue data? Let us help you.
