falcon health

Welcoming Prince Baawuah, DrPH, MPH, CSM? to the team to strengthen our healthcare program integrity mission! At falcon health, our mission is to ensure healthcare programs operate as intended, with minimal fraud, waste, abuse, and data errors. Healthcare payments work in amazingly complex ways, and systems have only grown more layered over time. Fighting FWA is a big part of what we do, but understanding the programs' original intent and keeping them aligned with their intended directions is equally important. This can be surprisingly challenging when there are always those looking to game the system. Neil Ahuja and I have known Prince for some time. As many in the industry know, Prince is deeply passionate about educating others on the complex inner workings of Medicare Advantage—one of the largest government healthcare programs—and has extensive knowledge in this space. We share the same mission: making systems work as intended and simplifying complex concepts so people don't get confused. After numerous virtual and in-person interactions, we realized one thing was clear—we needed to work together! So, please join me in welcoming Prince. He will be leading our Medicare Advantage solutions at Falcon (and also joining as a builder at mimilabs). Falcon, about us: https://lnkd.in/eezB_E3C mimilabs, about us: https://lnkd.in/eWt8RDTN

Medicare Beneficiary ID changes for reason If you work for Medicare Accountable Care Organizations, like Medicare Shared Savings Program or ACO REACH, you're familiar with CCLF - the claims data that Centers for Medicare & Medicaid Services share with ACOs to help analyze and reduce costs while improving care quality. Among the many files in the CCLF packet, one often overlooked gem is CCLF9, the member cross-reference file. The documentation notes: "The MBI can change if initiated by the beneficiary due to multiple reasons like fraud or identity theft. Any claims submitted after the MBI change occurs will carry the new or most recent MBI." How many members actually change their IDs? Quite a lot, it turns out. Seniors are constantly targeted by scammers, with fraudsters continuously researching ways to obtain Medicare Beneficiaries' information. I decided to examine if there are any temporal patterns to these ID changes - specifically, whether certain events correlate with member ID change requests. What you see in the graph is revealing: the number of ID changes (y-axis) plotted over time (x-axis) shows massive spikes in June 2018 and January-February 2024. Interestingly, perhaps coincidentally, these align with two major healthcare fraud events. While members may have various reasons for changing their IDs, I suspect these national-level fraud cases significantly influenced the surge in change requests. Medicare members often feel the direct impact of these fraud cases - and they may be more aware of what's happening than we realize.

What is the fastest way to figure out someone is deceased? This problem has been bothering me for some time. Healthcare payments are covered while someone is alive, and not covered after death, but knowing the exact date of death early is difficult—the data often comes very late. This timing gap creates a window where payments might continue after someone has passed away. Most importantly, early detection can prevent improper payments and potentially uncover fraudulent activities that take advantage of this delay in information reporting. When someone dies, information typically flows from: Funeral home → Social Security Administration → Medicare and other government agencies Each step creates delays that accumulate. Plus, getting access to the death master table requires resources and time. My experiment: I scraped Legacy.com (an online obituary aggregator) to test a faster approach. I collected recent obituaries from counties in five major Medicare states: CA, FL, TX, NY, and PA. Was it worth it? From the data, I extracted both obituary publication dates and dates of death. The frequency chart shows most obituaries are published within a week of death—meaning the time lag is quite minimal! I'll continue experimenting with how useful this data could be. I invite you to explore this as well! Data is available at mimilabs.ai! mimilabs sign up link: https://lnkd.in/eEhVFGKJ

ACO Leaders: Remember UTI Catheter A4352 and A4353? They're Still Active in CY2024! ?? Quick Check Needed: Run your CY2024 CCLF data for these two catheter codes. While CMS removed them from shared savings calculations for CY2023, the January-February 2024 claims are still included in your benchmark and performance year calculations. What We're Seeing: 1. High billing activity continued through January-February 2024 2. Sudden stop after the Washington Post article (classic sentinel effect!) The Background You Know: The Washington Post's February 2024 investigation [1] exposed the DME fraud scheme we've all been tracking. The Centers for Medicare & Medicaid Services responded by creating the "Significant, Anomalous, and Highly Suspect (SAHS)" classification and removed these claims - but only for CY2023. Why This Matters Now: The CY2025 PFS final rule [3] specifically states: "Establish an approach to identify SAHS billing activity occurring in CY 2024 or subsequent calendar years, and specify approaches to mitigating the impact of the SAHS billing activity on Shared Savings Program financial calculations in CY 2024 or subsequent calendar years. Under this approach we will exclude payment amounts from expenditure and revenue calculations for the relevant calendar year for which the SAHS billing activity is identified, as well as from historical benchmarks used to reconcile the ACO for a performance year corresponding to the calendar year for which the SAHS billing activity is identified." Let's Take Action: If you see the same pattern, join us! falconhealth.ai is compiling supporting materials for CMS to address these early 2024 claims. Share your findings in the comments or DM us (me and Neil Ahuja) - let's get these claims addressed for CY2024. Let's get this removed from the expenditure!! #Medicare #ACO #MSSP #ACOReach #ProgramIntegrity [1] https://lnkd.in/e5dwvmdX [2] https://lnkd.in/eHjtNCAn [3] https://lnkd.in/euTdTynB

FWA: Prevention (or early detection) is better than cure Have you read this article? "Arizona Couple Pleads Guilty to $1.2B Health Care Fraud" by HHS Office of Inspector General [1]. And let me show you a different chart I put together. The chart below shows how much the Centers for Medicare & Medicaid Services paid for HCPCS Q4xx (includes various skin grafts) that went through Arizona. Yes, not the entire nation. Just Arizona (carrier number = 03102). Look at the skyrocketing revenue curve - I wish it were my start-up revenue ;) Now, the question for Medicare ACO leaders - this $1.2 billion wasted, what does it mean to your ACOs? - The $1.2B incident in Arizona - how about the other states? - What is the exposure amount for your ACOs? - Would this be correctly adjusted for your shared savings in 2023 and 2024? - Is this the only case? Or possibly more? - What is your strategy to prevent incidents like this from happening in the future? - How would you think about preventing or early detecting these cases? - Do you want to understand the potential savings of preventing these types of incidents? Healthcare is a complex ecosystem, and bad actors always find loopholes in the reimbursement system. However, one thing is clear: Prevention is better than cure. This applies to more than just medicine; it also applies to Fraud, Waste, and Abuse. ???? ?????? ?????? ?? ???????????????? ?????? ???????????? ?????? ???????? ???? ?????????????? ?????? ?????????? ?????????????????? ???????? ????, ???? ???????? ???? ???????? ???? ??????! ???? ?????? ?????????????????? ?????? ??????????????, ???? ???????????? ?????????????? ????????????????, ?????? ???? ?????????? ???????? ???? ???????? ?????? ?? ?????????????? ???? ?????????? ???????? ?????? ?????????? ???????? ???? ?????????????????? ?????? ?????? ?????????????? ?????????????????? ???????? ????, falcon health. If you are in, send an email to [email protected] or DM Neil Ahuja or me! [1] https://lnkd.in/eBZUY86G

$437M Found in Ophthalmology Overutilization - ML Researchers, Let's Uncover More! People often ask me about the value of publicly available datasets, which I love collecting and analyzing at mimilabs. I sometimes struggle to answer not because I can't find value, but because there are so many fascinating possibilities - you just need imagination and domain expertise to unlock them! Check this out - I found this paper published early this year: "Utilization Analysis and Fraud Detection in Medicare via Machine Learning," by Mahan Tajrobehkar, Xin Guo, Diane Nguyen, Nikita Chigullapally, Varun Shravah, Shannon Yee, Abishek Chozhan, Kathy Hoang (she/her), Robert Chang, Carolina Gutierrez, and Scott Lee [1]. The team used public CMS datasets + expert-labeled overutilizers and frauds. Through ML models, they identified potentially fraudulent providers in ophthalmology. Here's the kicker: $437.1M attributed to overutilization in ophthalmology alone! My head is spinning with possibilities. At mimilabs, I've curated tons more datasets across specialties and facilities. While I'm finding interesting billing and practice patterns for our falcon health clients daily, I know I need more hands on deck. This isn't just about business - it's about protecting our healthcare systems and taxpayer money. So here's my call: ML researchers passionate about healthcare integrity - come join mimilabs! Let's explore these datasets, uncover insights, and share findings together. The potential impact is huge! Please share this post to help us connect with more ML researchers! Let's make this happen! ?? #HealthcareFraud #MachineLearning #Healthcare #DataScience #AI #PublicHealth #Medicare #Medicaid #ResearchCollaboration #OpenData #HealthTech #Innovation #FraudDetection #mimilabs [1] https://lnkd.in/eWrDrPAw