Health Care Fraud Takedown(fbi.gov)
fbi.gov
Health Care Fraud Takedown
https://www.fbi.gov/news/stories/2015/june/health-care-fraud-takedown/health-care-fraud-takedown
58 comments
Highest paid doctors in the world, and it still wasn't enough for them. And in case you believe their sob stories about Medicare not paying them enough, read this:
http://www.slate.com/blogs/moneybox/2013/01/23/chart_us_gove...
Only 19 of the 240 individuals were physicians. And most primary care doctors in canada make more money than I do, and I'm a surgeon.
As for the medicare thing, it's not just that medicare doesn't pay enough, it's that about half the time in my practice, medicare simply doesn't pay anything at all.
Finally, if you want to talk about fraud in medicine, the cited problem isn't the major fraud issue; it's just the most newsworthy (easily quantifiable money amount) and therefore an easier target for the men in black.
Not sobbing; I make a good living and am quite happy.
As for the medicare thing, it's not just that medicare doesn't pay enough, it's that about half the time in my practice, medicare simply doesn't pay anything at all.
Finally, if you want to talk about fraud in medicine, the cited problem isn't the major fraud issue; it's just the most newsworthy (easily quantifiable money amount) and therefore an easier target for the men in black.
Not sobbing; I make a good living and am quite happy.
> Only 19 of the 240 individuals were physicians.
Only?
How does that number compare to the %age of physicians in the total population of medical professionals?
And why does it matter that GPs in Canada earn more than a surgeon in the US?
Only?
How does that number compare to the %age of physicians in the total population of medical professionals?
And why does it matter that GPs in Canada earn more than a surgeon in the US?
The Canada thing is probably a rebuttal to the "highest paid doctors in the world" line in the original comment.
Medicare pays pretty reasonably. There is definitely ideological and other talking points in place, that I discussed here: https://news.ycombinator.com/item?id=7604698
Not always; ask any hospital if their anesthesiology department operates at a loss. The answer is almost always yes. You can't have surgery without anaesthesia, yet the cost of the service (including the market price of anaesthesiologists) outstrips the reimbursement.
i wonder how the medicare fraud strike force is currently doing this, and how we, as technologists can improve the process.
working around HIPAA makes this an particularly hairy problem but from what i understand it is still possible to create a compliant solution for hospitals, emr vendors, and insurance companies, and even patients to detect medical fraud.
i couldn't find much but maybe some work is already being done in this space?
working around HIPAA makes this an particularly hairy problem but from what i understand it is still possible to create a compliant solution for hospitals, emr vendors, and insurance companies, and even patients to detect medical fraud.
i couldn't find much but maybe some work is already being done in this space?
Benford's law is surprisingly effective in detecting unnatural patterns in data; I believe the IRS relies heavily on it. Then, alert eyes at the payment end, and encuragement to low-level clerical staff to cooperate in implicating their bosses or be left holding the bag. This works better for white-collar crime, since the criminal higher-ups are that much less likely to successfully put out a hit on persons who informed against them, though sometimes not for lack of trying.
https://en.wikipedia.org/?title=Benford%27s_law
https://en.wikipedia.org/?title=Benford%27s_law
> https://en.wikipedia.org/?title=Benford%27s_law
Now that is a weird Wikipedia URL. The normal one's /wiki/Pagename, and you sometimes see /w/index.php?title=Pagename, but /?title=Pagename? What produced that?
Now that is a weird Wikipedia URL. The normal one's /wiki/Pagename, and you sometimes see /w/index.php?title=Pagename, but /?title=Pagename? What produced that?
I wonder this is applied to find bad actors in the financial sector...
The FDIC has long recommended [1] mandatory vacation blocks as a fraud-detection tool:
[1] https://www.fdic.gov/news/news/financial/1995/fil9552.html
[2] http://www.marketplace.org/topics/business/easy-street/credi...
It is the FDIC's goal that all banks have a vacation policy which provides
that active officers and employees be absent from their duties for an
uninterrupted period of not less than two consecutive weeks. Such a policy
is considered an important internal safeguard largely because of the fact
that perpetration of an embezzlement of any substantial size usually
requires the constant presence of the embezzler in order to manipulate
records, respond to inquiries from customers or other employees, and
otherwise prevent detection.
[1] https://www.fdic.gov/news/news/financial/1995/fil9552.html
[2] http://www.marketplace.org/topics/business/easy-street/credi...
Yep - don't know about the rest but my former employer, Morgan Stanley, has a mandatory vacation policy (MVP) in place. You must take two consecutive weeks off per year.
At a hackathon back in 2013, someone mentioned analyzing Medicare claim data to find impossible or improbable scenarios, like a doctor performing two lengthy procedures on the same day at hospitals 500 miles apart.
There was a famous case in LA of a couple of doctors working impossible shifts (more than 24 hours in a day, or 24 hours a day for over a week straight):
http://articles.latimes.com/2005/apr/26/local/me-kingdrew26
Eventually the facility was closed and some of the administrators were canned.
http://articles.latimes.com/2005/apr/26/local/me-kingdrew26
Eventually the facility was closed and some of the administrators were canned.
[deleted]
So here's what medical fraud looks like: http://i.imgur.com/jMvUqqK.jpg
Sorry, crappy excel graph, but, it was meant to be a quick and dirty look at 12 GB of prescription data that got analyzed by a few programs I wrote back in 2009, give or take a year. Took days to crunch numbers after it was written. Anyhow, looks like an imaginary city skyline, right?
Going from left to right, lets call it the X axis, are various diagnostic codes used to prescribe medication. So on the left side it's like code 400, on the right side 500. In between is 401.3, and so on. Been awhile, so can't remember the exact numbers but bear with me. The drugs range from opiates to diflucan for yeast infections, to whatever else. So you kind of see a distribution range that's normal.
On the Y-axis, are years. Here's the slightly confusing part of the graph: I striped 4-5 clinics worth of data on that axis. So on that axis, only 6 years of data are shown per clinic. What shows up after the first 6 long rows is a different clinic, and so on.
The Z-axis is a frequency of prescriptions. Like, how tall a tower is means how many prescriptions were written for a particular medication, by a particular clinic, on a particular year.
If you look at the nearest 6 long rows, that's 1 clinic, 6 years of operation and you see nothing but flat lines. No yeast infections, no eye drops, no steroids. Just some really insanely tall towers. One of the towers gets clipped from the graph because it's that insanely taller.
The tallest towers were the most expensive drugs and treatments that the government reimbursed the clinic for, so they took a shortcut and just went for those. The kicker is that they only got caught when we started investigating. There was a tip. Someone reported something weird about the clinic. So, we went up to the state and asked for an anonymized data dump of the clinic in question, and then absolutely nothing happened. The state stalled for 6 months before finally giving the data up. Turns out, they were only alerted to the fraud after we asked questions about the clinic, and they wanted to take corrective actions before disclosing anything to us so that it didn't seem like they were sleeping on the job.
I don't know what to say. I get it, this stuff is complicated, the data sets are huge, and there are more blindspots than you'd think. Lack of oversight is too strong of an accusation for me to wield, but there was definitely a fear of criticism. What I'm trying to say here is that computational detection is only a small fraction of the real issue. The bigger issue is the guarded cultural environment in which all these agencies exist, and without intimate knowledge of how they work and what is possible, there's no silver bullet.
Sorry, crappy excel graph, but, it was meant to be a quick and dirty look at 12 GB of prescription data that got analyzed by a few programs I wrote back in 2009, give or take a year. Took days to crunch numbers after it was written. Anyhow, looks like an imaginary city skyline, right?
Going from left to right, lets call it the X axis, are various diagnostic codes used to prescribe medication. So on the left side it's like code 400, on the right side 500. In between is 401.3, and so on. Been awhile, so can't remember the exact numbers but bear with me. The drugs range from opiates to diflucan for yeast infections, to whatever else. So you kind of see a distribution range that's normal.
On the Y-axis, are years. Here's the slightly confusing part of the graph: I striped 4-5 clinics worth of data on that axis. So on that axis, only 6 years of data are shown per clinic. What shows up after the first 6 long rows is a different clinic, and so on.
The Z-axis is a frequency of prescriptions. Like, how tall a tower is means how many prescriptions were written for a particular medication, by a particular clinic, on a particular year.
If you look at the nearest 6 long rows, that's 1 clinic, 6 years of operation and you see nothing but flat lines. No yeast infections, no eye drops, no steroids. Just some really insanely tall towers. One of the towers gets clipped from the graph because it's that insanely taller.
The tallest towers were the most expensive drugs and treatments that the government reimbursed the clinic for, so they took a shortcut and just went for those. The kicker is that they only got caught when we started investigating. There was a tip. Someone reported something weird about the clinic. So, we went up to the state and asked for an anonymized data dump of the clinic in question, and then absolutely nothing happened. The state stalled for 6 months before finally giving the data up. Turns out, they were only alerted to the fraud after we asked questions about the clinic, and they wanted to take corrective actions before disclosing anything to us so that it didn't seem like they were sleeping on the job.
I don't know what to say. I get it, this stuff is complicated, the data sets are huge, and there are more blindspots than you'd think. Lack of oversight is too strong of an accusation for me to wield, but there was definitely a fear of criticism. What I'm trying to say here is that computational detection is only a small fraction of the real issue. The bigger issue is the guarded cultural environment in which all these agencies exist, and without intimate knowledge of how they work and what is possible, there's no silver bullet.
So it looks like you've got data for four clinics in there. Of the three non-fraudulent clinics, two show pretty elevated levels of that particularly lucrative code, the one that clips the ceiling for the fraudulent clinic. How much of that is fraud?
The fraudulent clinic has something really bizarre going on, too. In the first year (years being in order lavender, red, yellow, green, black, and peach), they've got a big spike at the ultra-lucrative code and some other big spikes at other codes. In year 2 (red), they've got just the one spike, a smaller version of their second-biggest spike from year 1. In year 3 (yellow), they've got one "spike", but it's tiny. In years 4 and 5 they've got practically nothing at all. What were they doing then? Didn't they want any money at some point in that three-year period?
The fraudulent clinic has something really bizarre going on, too. In the first year (years being in order lavender, red, yellow, green, black, and peach), they've got a big spike at the ultra-lucrative code and some other big spikes at other codes. In year 2 (red), they've got just the one spike, a smaller version of their second-biggest spike from year 1. In year 3 (yellow), they've got one "spike", but it's tiny. In years 4 and 5 they've got practically nothing at all. What were they doing then? Didn't they want any money at some point in that three-year period?
Sorry, user logicallee explained this better than I did. You're looking at 4-5 graphs put next to each other for comparison. The nearest flatland with the huge towers is the one troubled clinic.
Here's an annotated version of that, drawn by a child apparently: http://i.imgur.com/1dcuuXI.png
As you can see, data of the clinic we were investigating is the first 6 long rows, and ones behind it are clinics we were not investigating. We asked to compare a number of clinics so not to tip our hand, and the administration took half a year of paranoid data checking before giving it to us.
I know, not the most intuitive graph, but the graph was meant to be a diagnostic for only me, the person who composed the data. As you can see, a single glance at the graph revealed the problem, without involving any numerical analysis.
Here's an annotated version of that, drawn by a child apparently: http://i.imgur.com/1dcuuXI.png
As you can see, data of the clinic we were investigating is the first 6 long rows, and ones behind it are clinics we were not investigating. We asked to compare a number of clinics so not to tip our hand, and the administration took half a year of paranoid data checking before giving it to us.
I know, not the most intuitive graph, but the graph was meant to be a diagnostic for only me, the person who composed the data. As you can see, a single glance at the graph revealed the problem, without involving any numerical analysis.
thanks, though seemed to me thaumasiotes who I replied understood perfectly! In particular he is correct that "Of the three non-fraudulent clinics, two show pretty elevated levels of that particularly lucrative code, the one that clips the ceiling for the fraudulent clinic. How much of that is fraud?" which he only could tell by correctly interpreting the graph (reading across that drug's column) - and it's a good question.
His second paragraph was only about the one clinic in question, he ignored the other 3 in his second paragraph, though he wasn't explicit about this, and asked a year-over-year question about the drug, concerning clinic A only.
My point was kind of tangential, that, INCIDENTALLY if the colors matched up in the rows (were repeated in the same order 4 times) you could look at it another way visually that you can't right now without counting by hand. Specifically, you could look at the aggregate trend for all four clinics year-over-year for the drug in question (the one with the spike) by seeing with your eye how the six colors move as you move your eye from Stripe A, to Stripe B, to Stripe C, to Stripe D. Right now, with your eye you can only tell or ask about year-over-year changes for a specific drug for clinic A, not for the other ones. If all four 2009's were peach, you could easily tell if there were 4 spikes in that year or just one. In fact in 2009 all four do seem to spike somewhat. Not being able to visually see aggregate year-over-year comparisons is probably the downside to the current presentation.
His second paragraph was only about the one clinic in question, he ignored the other 3 in his second paragraph, though he wasn't explicit about this, and asked a year-over-year question about the drug, concerning clinic A only.
My point was kind of tangential, that, INCIDENTALLY if the colors matched up in the rows (were repeated in the same order 4 times) you could look at it another way visually that you can't right now without counting by hand. Specifically, you could look at the aggregate trend for all four clinics year-over-year for the drug in question (the one with the spike) by seeing with your eye how the six colors move as you move your eye from Stripe A, to Stripe B, to Stripe C, to Stripe D. Right now, with your eye you can only tell or ask about year-over-year changes for a specific drug for clinic A, not for the other ones. If all four 2009's were peach, you could easily tell if there were 4 spikes in that year or just one. In fact in 2009 all four do seem to spike somewhat. Not being able to visually see aggregate year-over-year comparisons is probably the downside to the current presentation.
Ah, I see, and take your point. I should have worked on a more reader-friendly version of this graph so I just assume people don't understand its bizarre nature. But, my work had been done many years ago with the investigation.
Here's the part that stood way out even with that unsophisticated graph: the flat land between various prescription codes. It's just there. It draws the eye and makes you ask questions, which is what we did. Another dimension not pictured there is distribution of doctors vs prescriptions. Theirs stood out on that too.
Even in their busiest years, they didn't treat any common ailments with any degree of distributed variety. By contrast, rest of the clinics did business as usual: whoever walked through their door got treated for whatever random thing they had.
Here's the part that stood way out even with that unsophisticated graph: the flat land between various prescription codes. It's just there. It draws the eye and makes you ask questions, which is what we did. Another dimension not pictured there is distribution of doctors vs prescriptions. Theirs stood out on that too.
Even in their busiest years, they didn't treat any common ailments with any degree of distributed variety. By contrast, rest of the clinics did business as usual: whoever walked through their door got treated for whatever random thing they had.
Just based on eyeballing the graph, I'd say there's a cultural element to what codes get used, because individual clinics often show more or less activity at a particular code for all six years. Choosing a code is something of a gray area, so that's not necessarily malicious, but I think "whoever walked through their door got treated for whatever random thing they had" is slightly oversimplified -- the patients will have been treated appropriately, but local culture will have pulled them into being coded in certain ways over other, arguably equally-applicable ways.
(Clinics having their own "personality" in coding could also be explained by the clinics having locally well-recognized specialties. That's hard to evaluate without knowing which codes are which.)
(Clinics having their own "personality" in coding could also be explained by the clinics having locally well-recognized specialties. That's hard to evaluate without knowing which codes are which.)
Just a note on the data presentation :)
Your analysis shows why it's kind of a shame GP had to 'stripe' the years rather than having another dimension (i.e. the striping is such that long-row closest to us to long-row farthest from us goes clinic1-yr1, clinic1-yr2, clinic1-yr3, clinic1-yr4, clinic1-yr5, clinic1-yr6, clinic2-yr1, clinic2-yr2, etc: i.e. 1,2,3,5,6,1,2,3,4,5,6,1,2,3,4,5,6) That means that rows don't actually form a data dimension: rather, we are looking at four independent graphs that are put one after the other without spaces. (The first graph is rows 1-6 with the row dimension being the year, the second graph is rows 7-13 with the year reset, etc.) See note for another way to see this.
It might be visually possible to see what happened at other clinics in yr1, yr2, yr3, yr4, yr5, and yr6, but at the moment the only way to do this is to read long-rows 1, 7, 13, 19, and 24 which is not obvious, you have to count to know what is what.
It would help if the colors corresponded (row 1 and row 7 had the same colors, so that color forms the year dimension), then you could look at the image from the perspective of different colors and see if anything sticks out. For example, if you wanted to see what happened in year 4 or 5 (as you mention), then you could look at all of the greens and blacks. (This means to identify a specific clinic-year you have to go by row number rather than color, but that seems OK to me - nobody is going to consult the legend consisting of 24 colors anyway.)
As it happens, it's a chore to count out what is year 4 and 5 for the other clinics and we lose this very important dimension visually. (Quick: tell me the tendency among all clinics as you move from year lavender, to red, to yellow, to green, to black, to peach in clinic 1).
However, I don't think that excel would have let you define colors in a custom way like this.
--
NOTE: You can tell that the rows don't form a data dimension, because it would be a mistake to connect all the points, like a topographic map -- like this: https://alastaira.files.wordpress.com/2011/04/image31.png -- . If you did that you should have a break between rows 6 and 7, between 12 and 13; and between 18 and 19 ---- because the slope between these specific rows is meaningless. On the other hand, if you DID have four such broken graphs next to each other, and within each graph they followed (repeated) the same color order, it would be easier to compare years. To further identify the color dimension the colors could move more predictably along the color scale (e.g. roygbi - red, orange, yellow, green, blue, indigo...)
Your analysis shows why it's kind of a shame GP had to 'stripe' the years rather than having another dimension (i.e. the striping is such that long-row closest to us to long-row farthest from us goes clinic1-yr1, clinic1-yr2, clinic1-yr3, clinic1-yr4, clinic1-yr5, clinic1-yr6, clinic2-yr1, clinic2-yr2, etc: i.e. 1,2,3,5,6,1,2,3,4,5,6,1,2,3,4,5,6) That means that rows don't actually form a data dimension: rather, we are looking at four independent graphs that are put one after the other without spaces. (The first graph is rows 1-6 with the row dimension being the year, the second graph is rows 7-13 with the year reset, etc.) See note for another way to see this.
It might be visually possible to see what happened at other clinics in yr1, yr2, yr3, yr4, yr5, and yr6, but at the moment the only way to do this is to read long-rows 1, 7, 13, 19, and 24 which is not obvious, you have to count to know what is what.
It would help if the colors corresponded (row 1 and row 7 had the same colors, so that color forms the year dimension), then you could look at the image from the perspective of different colors and see if anything sticks out. For example, if you wanted to see what happened in year 4 or 5 (as you mention), then you could look at all of the greens and blacks. (This means to identify a specific clinic-year you have to go by row number rather than color, but that seems OK to me - nobody is going to consult the legend consisting of 24 colors anyway.)
As it happens, it's a chore to count out what is year 4 and 5 for the other clinics and we lose this very important dimension visually. (Quick: tell me the tendency among all clinics as you move from year lavender, to red, to yellow, to green, to black, to peach in clinic 1).
However, I don't think that excel would have let you define colors in a custom way like this.
--
NOTE: You can tell that the rows don't form a data dimension, because it would be a mistake to connect all the points, like a topographic map -- like this: https://alastaira.files.wordpress.com/2011/04/image31.png -- . If you did that you should have a break between rows 6 and 7, between 12 and 13; and between 18 and 19 ---- because the slope between these specific rows is meaningless. On the other hand, if you DID have four such broken graphs next to each other, and within each graph they followed (repeated) the same color order, it would be easier to compare years. To further identify the color dimension the colors could move more predictably along the color scale (e.g. roygbi - red, orange, yellow, green, blue, indigo...)
> Quick: tell me the tendency among all clinics as you move from year lavender, to red, to yellow, to green, to black, to peach in clinic 1
The other clinics show reasonable self-similarity in years 3, 4, and 5. But the fraudulent clinic is reporting almost nothing at all. Not whatever codes it reported in the past, not whatever codes are worth the most money, not even randomly selected codes -- nothing. It's true that that might not be interesting if the other clinics showed similar behavior, but they don't. (And actually, I think "no medical demand for a three-year period" would be pretty interesting too.)
The other clinics show reasonable self-similarity in years 3, 4, and 5. But the fraudulent clinic is reporting almost nothing at all. Not whatever codes it reported in the past, not whatever codes are worth the most money, not even randomly selected codes -- nothing. It's true that that might not be interesting if the other clinics showed similar behavior, but they don't. (And actually, I think "no medical demand for a three-year period" would be pretty interesting too.)
yes, it's unusual. I'd also like to be able to interpret that group of skyscrapers toward the right of the chart, for the third clinic. It's far more than what any other clinics prescribe (including the fraudulent clinic, which doesn't prescribe that drug) and also it is far more than anything else that that clinic prescribes. It is also fairly static year over year within that clinic. So what gives?
Thanks for interpreting that while I slept. Excel's 3D graph feature was just the quickest way to render this, since I was already tired of waiting for data to get reformatted.
Believe the original data was just a set of forms, each printed page stating prescriptions for a patient session. Basically nothing you could perform frequency counts without recomposing into a database, and that took days.
Believe the original data was just a set of forms, each printed page stating prescriptions for a patient session. Basically nothing you could perform frequency counts without recomposing into a database, and that took days.
Mind linking where you got the data?
Sorry, this wasn't publicly available for download. After arm-wrestling the senior administrators for months and months, a reporter and I literally drove to pick it up and were given a set of DVDs by the State of Maryland health department, Mental Hygiene Administration, and a ton of other acronyms these people fall under.
Probably not the same source, but some interesting data available here - https://data.cms.gov/utilization-and-payment-explorer
There is a lot of public information available about this stuff. I would start with looking into Zone Integrity Program Contractors (for Part A and B), Medicare Drug Ingretity Contracts (Part D), and Recovery Audit Contractors (Part A and B, mostly focusing on over and under paymentments through periodic auditing).
There are also groups you can find out more information on that handle fraud for medical labs that fall under CLEA regulation (Labs/PINS group) and ambulatory services fraud. There is also a contractor that's focused on provider screening services.
It's a pretty big subject to sum up how they're doing this but it does involve some interesting analysis techniques and there are plenty of pain points that could probably be improved.
It's really interesting and exciting work for technologist who want to get in on it.
There are also groups you can find out more information on that handle fraud for medical labs that fall under CLEA regulation (Labs/PINS group) and ambulatory services fraud. There is also a contractor that's focused on provider screening services.
It's a pretty big subject to sum up how they're doing this but it does involve some interesting analysis techniques and there are plenty of pain points that could probably be improved.
It's really interesting and exciting work for technologist who want to get in on it.
Perhaps big data companies like Palantir are assisting with this?
Possibly. But just as likely they are running their own Hadoop platform and have hired some data scientists.
Most large organisations these days are running their own analytics platform.
Most large organisations these days are running their own analytics platform.
Does analyzing this actually require joining in large data sets -- that is, larger than will fit on a single machine?
I'd always assumed that the records involved weren't very large, but I don't know much about the problem space, so I'm not sure if other data gets joined in in a way that benefits from cluster-based analysis.
I'd always assumed that the records involved weren't very large, but I don't know much about the problem space, so I'm not sure if other data gets joined in in a way that benefits from cluster-based analysis.
I forget the exact numbers, but a single year's worth of Medicare part D claims data will be on the order 1TB. That doesn't include the beneficiary and provider datasets (which links patients and doctors) which you'll need to join against. Also when detecting fraud like this, you may want to include the other Medicare parts (A, B, C) which are oftentimes larger than part D (being that D is the newest). So this leaves you manipulating on the order of 10TB for single year analysis. Finally, since Medicare bills can be corrected up to 3 years, you may end up joining multi-terabyte datasets.
Medicare is one of the largest health schemes in the world in an industry known for massive amounts of paperwork. It's a humongous data set.
Yes, plus some sort of analytics-focused data warehouse (Teradata, etc.). I am almost certain though their analytics team is outsourced to a major contractor like Lockheed Martin.
My favorite is using Benford's law to find anomalous digit distributions in phony numbers.
It's a very reliable method and it is surprising that with the amount of publicity mentioning this either in passing or as a direct cause for a further investigation that it remains effective. After all, you'd imagine that wanna-be fraudsters would 'Benford-Proof' their numbers.
This is a very hard problem. Not only do you have to find a distribution that follows the law, the numbers still have to make sense in context (changing a 1 hour consult/doctor visit to a 9 hour consult). With election fraud you are usually up against a state statistician who at least tried to 'Benford-Proof' their numbers, so then the challenge is to find patterns of this Benford-proofing. For instance, Benford's law can be extended to the second or third digit, exposing the 2009 Iranian elections: "The data give very strong support for a diagnosis that the 2009 election was affected by significant fraud" https://en.wikipedia.org/wiki/Results_of_the_Iranian_preside...
If someone bills Medicare for something for me, do I not get a record of that, indicating the transaction?
I'd expect to have received something saying that "provider X was paid for you to have equipment Y (or counseling session Z). If this does not seem accurate, please call 1-800-McGruff". I'd think that would alert people about fraud far more quickly, no?
I'd expect to have received something saying that "provider X was paid for you to have equipment Y (or counseling session Z). If this does not seem accurate, please call 1-800-McGruff". I'd think that would alert people about fraud far more quickly, no?
Indeed; being disabled, I'm on Medicare rather early for it, and I get informed of everything that's billed and associated with me for Medicare Part B, doctor's office etc. sorts of things (haven't had to go to the hospital so I don't know about the original Part A). I also have deductibles to pay, including before my Medigap insurance kicks in.
That said, they could be billing fictitious people, or people sick enough the extra stuff is easy to miss with all the other charges ... and that's very possibly how many of these fraudulent outfits were found. There are plenty of us with personalities in the direction of OCD, my 79 year old mother for instance, who's I'll admit is pretty healthy and was a RN before I was born, wouldn't miss anything. Last time I was in the ER for a nasty fall I later called them up to query a charge of $90 for a TDa?P inoculation, and was satisfied with their explanation that they had to keep plenty of non-expired medicines on hand for everything, including unexpected events. In their case, they had been the primary hospital that received the injured from the 9/11 attack on the Pentagon, and closer to home, a lot more people would have died than the ~160 in the EF-5 tornado that hit my home town and took out the other hospital, if they hadn't had ample supplies (see http://stormdoctor.blogspot.com/2011/06/first-response-mode-... for an idea of just how intense that was)
That said, they could be billing fictitious people, or people sick enough the extra stuff is easy to miss with all the other charges ... and that's very possibly how many of these fraudulent outfits were found. There are plenty of us with personalities in the direction of OCD, my 79 year old mother for instance, who's I'll admit is pretty healthy and was a RN before I was born, wouldn't miss anything. Last time I was in the ER for a nasty fall I later called them up to query a charge of $90 for a TDa?P inoculation, and was satisfied with their explanation that they had to keep plenty of non-expired medicines on hand for everything, including unexpected events. In their case, they had been the primary hospital that received the injured from the 9/11 attack on the Pentagon, and closer to home, a lot more people would have died than the ~160 in the EF-5 tornado that hit my home town and took out the other hospital, if they hadn't had ample supplies (see http://stormdoctor.blogspot.com/2011/06/first-response-mode-... for an idea of just how intense that was)
I used to work for a CMS contractor that was involved in th is kind of work. The level of effort put into fraud, abuse, and waste prevention for Medicare and Medicaid are amazing. There's still a lot of room for improvement, but it was a memorable experience working in that field.
It's rather disgusting that these people were fraudulently billing mental health services, seeing as how there are many real people who actually need these services. I'm thinking of veterans of the last 14 years of war. And anyone else who legitimately needs help.
Yes it is disgusting, maddening, and in a way, bizarre considering that Medicare reimbursement for psychiatric services is shamefully inadequate. (No question, Medicare reimbursement does not come close to covering the costs of providing mental health care, hence many professionals won't take Medicare patients.)
That makes it hard to understand such fraudulent claims, yet the FBI statement documents claims or payments for psychotherapy that wasn't provided. Seems like a poor way to defraud the government. The only thing I can think of is billing a large volume of false claims and thinking low-value services like mental-health care would slip under the radar.
I'm very glad to see a few "scam artists" were caught. They deserve what's coming to them, but the ongoing occurrence of fraud suggests fundamental problems remain unaddressed.
That makes it hard to understand such fraudulent claims, yet the FBI statement documents claims or payments for psychotherapy that wasn't provided. Seems like a poor way to defraud the government. The only thing I can think of is billing a large volume of false claims and thinking low-value services like mental-health care would slip under the radar.
I'm very glad to see a few "scam artists" were caught. They deserve what's coming to them, but the ongoing occurrence of fraud suggests fundamental problems remain unaddressed.
Honestly, it's embarrassing that people who pretend to be professionals would do something like this. I agree that there needs to be structural changes to how we address this issue. I'm not read up enough on the structure of the problem to propose solutions, but I do know that some people who need the help might get some help, but eventually run into a wall.
For a second I thought it is about how hospitals bill tens of thousands dollars only to settle for 5% of the total with insurance company (if you are lucky to have one, otherwise you are screwed)
Which is the real scam, but somehow billing $20 for an aspirin pill is A-okay.
I dare you not to notice some patterns with the names:
http://www.justice.gov/opa/documents-and-resources-june-2015...
My impression is that money lost to fraud is a tiny fraction of money lost to legal wasteful spending, lawyers fees, and drug company profits.
Is there any reason to believe these law enforcement activities are an efficient use of time and labor?
Is there any reason to believe these law enforcement activities are an efficient use of time and labor?
The larger portion of the value of criminal determent is the crimes it actually deters. We would have to calculate the cost of those crimes (tough to do-- the cost to society is not the same as the amount stolen, after all, since the money goes in the pockets of the criminals) that would have been committed if it was well known there was no enforcement.
If it cost less than than $712 million to recover this $712 million, it was more than justified. Most law enforcement doesnt have nearly that ROI.
These were major frauds -- $712MM in fraud by 243 people.
These were major frauds -- $712MM in fraud by 243 people.
I never understand arguments like this.
You do realise that companies/organisations are capable of doing more than one thing at a time.
You do realise that companies/organisations are capable of doing more than one thing at a time.
Yes: it was 700 million dollars. And are you suggesting that law enforcement's time would be better spent investigating legal activities?
712M is 0.15% of the entire yearly Medicare budget. Maybe there's bigger waste that we are still fighting, but that's a hell of a lot to be losing to fraud, and they doubtless haven't caught all the fraudsters yet.
I think they're effective insofar as proposals for reform are often deflected by allegations of fraud and deliberate waste - 'if you can't root out the rampant fraud why should we trust you to reform a broken system?!'
You see this argument in immigration politics a lot too - 'we can't discuss reform until the border is secured.' I agree that meaningful reform would probably substantially reduce the profitability of fraud and make it easier to detect as well, but that's political reality for you.
You see this argument in immigration politics a lot too - 'we can't discuss reform until the border is secured.' I agree that meaningful reform would probably substantially reduce the profitability of fraud and make it easier to detect as well, but that's political reality for you.
A few years back, my wife was finishing up classes to become a cardiovascular technician. Part of the process to become fully certified is kind of a chicken/egg scenario - to become ARDMS certified, you need to work under a cardiologist for a year - but most cardiologists don't want to hire someone that isn't already ARDMS certified. Anyhow - she was pretty frustrated in how hard it was becoming to get a foot in to the industry when she finally heard back from one practice. The interview is conducted in an apartment on the 50th floor of a luxury building in downtown Manhattan - there were a few other people there just doing work on laptops. She gets the job, but she immediately felt something was off - but she wasn't sure what.
For the most part she would help in the "office" and do billing and coding from some other offices this doctor owned. Sometimes she would be sent out to one of these offices to actually scan people (which is what she actually enjoyed and wanted to be doing). And sometimes the doctor would take the whole team of people working in the "office" out to very fancy dinners where no expense was spared.
A few months (I think it was about 6 months - but could be wrong) in to this job, my wife needed a day off for her own doctors appointment, and it turned out to be a good day to take off - the office was raided by a joint task force of the FBI and Secret Service - they took the main guy in to custody, confiscated all the laptops, etc. My wife got a call later that afternoon from one of the other girls telling her what happened. Eventually my wife was asked to come in to talk with the FBI twice - they had pictures of her outside of the building smoking - knew what she had eaten at one of the dinners, etc. She was cleared from any involvement since she really had no clue what was actually happening.
In the end, the guy was arrested for Medicare and Medicaid fraud - he had purchased a radiological service from a doctor that was retiring, and assumed that doctors identity. He was charged with billing out $32 million in services that were never performed - $20M to Medicaid, $12M to Medicare. It definitely seems they were on to him for a little while at least - though I can't find any more details besides the original press regarding his arrest.
Sadly, my wife lost this time and does not feel right putting it on her resume - so she remained in the chicken and egg scenario and eventually she went back to school to broaden her applicable skills so she can actually work in the field - and hopefully, actually get that certification...