As third party payers around the country, Medicare and even the Office of Inspector General (OIG) put together a collective assault on healthcare fraud, my email keeps pinging and my phone keeps ringing from chiropractors around the country who are getting increasingly nervous about this present state of affairs. Not that I blame them. At no point in healthcare history has there been a greater focus on eliminating abuse, waste or fraudulent claims than over the last several years. What began as whispers and rumors of insurance companies thumbing through your records has now developed into the full blown reality of post-payment audits.
“Compliance” – formerly a term seldom often used until HIPAA was ushered in – is now a major buzzword in healthcare. Chiropractors, MD’s, PT’s – just about everyone – are running scared. We are no longer dealing with some back-alley, healthcare rent-a-cop who couldn’t cut it as a private eye snooping around clinics that have been known for years to be flagrant violators. No, the stakes are real, serious and technologically driven.
Enforcement entities, MEDICs, and Program Safeguard Contractors are dramatically increasing their use of claims data to identify fraud, waste and abuse (FWA). Some enforcement entities who use claims data to identify FWA are Zone Program Integrity Contractors (ZPICs), Medicaid Fraud Control (MFCUs) and Recovery Audit Contractors (RACs). Recently, I sat in a seminar sponsored by the Healthcare Compliance Association, and was kept on the edge of my seat for hours by the shocking details of how these agencies are blindsiding physicians across the US with incredibly large fines, post payment demands and enough mental anguish to send a 20 year old into immediate retirement from the practice of healthcare.
Doctor, this stuff is real.
ALL Your Data – At the Push of a Button
I am not your typical chicken little, but the facts are hard to ignore. If you haven’t taken steps to arm yourself for the battle already, wake up and get going. If you have, keep reading for more ammunition and be as proactive as possible.
Auditors produce descriptive statistics, predict regression models and perform clustering analyses to figure out what you are doing. In plain English, this means that with the push of a button, they know how your billing and coding compares to everyone else’s. These suspicious areas, discovered by running frequencies and means on the data, could be flagged (by the evil forces of the insurance companies or by your own team of angels) for deeper statistical exploration to determine if potential fraud, waste or abuse exists.
To put this in more plain English, a frequency is used to display the number of occurrences of an event. In chiropractic, the best comparable descriptor would be what is commonly referred to as a “procedure count.” Examples include the number of times you billed a 98940, 98941 or 98942. Frequencies can provide general information on multiple aspects of the data that point to areas where problems may exist. For example, an insurance plan may learn from a frequency analysis that your most commonly used chiropractic code just happens to be 98942 – the most expensive code to bill for the chiropractic spinal adjustment.
You – An Outlier?
Through the use of some statistical analysis, the plan can then explore this area further by counting the number of times other providers in your area bill for the same procedure. The gathered data will then allow the insurance plan to determine if you have a higher frequency of billing the 98942 than your peers. A deeper analysis of the mean — defined as the central location of the data – can then be used to determine the average values of a field of data, such as beneficiary payment amount. In other words, a third party payer can run a Mean Analysis (truly!) and easily figure out what your “case average” is compared to other docs in your area.
Use of frequency or mean analysis by themselves can provide a payor with some broad descriptive data, but combining these with a Standard Deviation can provide a meaningful look at how your statistics stack up to providers in your region. As you may remember from your statistics class, Standard Deviation is a measure of how widely spread the values are in a data set. Locating the mean value (average) can give a payer a fairly accurate picture of Outliers – those who are a number of standard deviations from the mean.
Now, let’s put all this math to reality. For example, the average beneficiary payment amount is calculated through a means analysis to be $50 per claim. Three claims, though, are found to have a payment of $250, $435 and $875. These three claims can then be flagged by the insurance plan for further exploration, because their payment amounts are substantially higher than the average payment amount for the data set. Outliers can then be examined more closely to determine if any aberrant patterns or potential fraud exist. Advanced Statistical Methods To dig a little deeper in their relentless pursuit of getting their money back, a payor may produce even more detailed results using advanced statistical methods such as
Deadly Tools — Regression Models and Clustering Analyses.
Regression Models are used to predict a given dependent variable. For example, the dependent variable could be the amount of Medicare reimbursement made to a particular provider. The model is used to identify providers who received significantly more from Medicare than others. While this sounds like the same fundamental analysis as indentifying Outliers, the use of Regression Models can allow a third party payor to incorporate more advanced factors that might influence the amount of money made by a given provider.
The real key to use of Regression Models is its ability to provide predictions for the user. In other words, a Regression Model may analyze a data set from a group of providers and determine that providers whose claims typically exceed $XXX also have a higher frequency of claims submitted or a higher cross utilization rate (i.e. the patients not only see you, the DC, more often, but they utilize other providers more often too.) Predictions can be made for all factors in the model and the payor may even choose to establish hypotheses for the selected dataset, for which they may act upon later in determining new provider policies, utilization review or post-payment audits.
Cluster Analysis is another advanced statistical tool used on claims data to ultimately locate areas of potential fraud or overpayment. Cluster Analysis involves the classification of millions of data values into several “like” data groups. The goal is to effectively organize the data into similar clusters to identify differences between or among clusters.
For example, provider specialties of “Physical Medicine” could be organized into clusters where several like specialties are grouped into one cluster such as Chiropractic, Physical Therapy, Osteopathy, Massage Therapy and Physiatry. The group of specialties would be submitted to a large analysis of data wherein similar codes for claims are used to determine utilization patterns or payment trends. Cluster analysis is an effective method for third party payors to “comparison shop” among providers when dealing with several million claims records.
It can then be used to drive future benefit packages, reimbursement rates and or even inclusion/exclusion of certain services within a health plan Where the Rubber Meets the Road So what’s the bottom line in reference to all this complex math, unfamiliar terminology and just plain scary stuff? If you feel like “Big Brother” is watching you, then you are correct. Third party payors are not only watching you to determine if they can catch you doing something fraudulent, they are watching you (in comparison to others) to see if they can save a buck or two.
What Chiropractors Can Do to Protect Themselves
My purpose is sharing this bit of “doom and gloom” with you is not to boil your blood pressure over the “evil” insurance companies or even the obvious flaws of the healthcare system in general. On the contrary, my intent is to arm you with information so that you are aware of your “enemy” and so that you can design a strategic battle plan to ensure your own survival.
1. Track Your Stats — If the payers are looking at them, so should you! Most practice management software provide usage reports that would show how many units of different procedures you billed so you can see where you stand. Unfortunately, most DC’s fail to use these in any meaningful way. Start.
2. Keep informed — you must be aware of payer policy changes, in addition to Medicare changes, and rules and regulations. These not only help you keep your license but keep you paid. On the other hand, if you show your ignorance by repeatedly billing something incorrectly, you may actually cause yourself to get audited. After all, the payer can tell you don’t know the rules, why not send a team to see what else you are doing wrong?
3. Don’t Automatically Assume Guilt – despite the fact that payers wield a big stick and have vastly greater resources than you, this does not automatically mean that you are in the wrong. There is a significant difference between being an outlier (you look different) versus someone who has done something wrong. Granted, once you are labeled an outlier and audited, your billing, coding and documentation must prove that you are right. But you still have a chance. And the payers DO make mistakes. So don’t throw in the towel if and when you get an audit letter and head for the Cayman Islands. Instead, calmly review what the payer is stating and formulate your defense.
P.S. If, by chance, you are faced with an Audit presently, don’t delay! Procrastination will not make your case go away!! I am happy (sort of) to discuss this issue with you. See the CHIROPRACTIC AUDIT CONSULTING section of my website for more info.