7 ms·

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

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?

Axsuul 11y ago
Perhaps big data companies like Palantir are assisting with this?
- threeseed 11y ago
  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.
  - ipsin 11y ago
    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.
    - navait 11y ago
      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.
    - jhorey 11y ago
      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.
  - jhorey 11y ago
    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.
kevin_thibedeau 11y ago
My favorite is using Benford's law to find anomalous digit distributions in phony numbers.
- jacquesm 11y ago
  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.
  - compbio 11y ago
    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_presidential_election,_2009#Initial_Digit_Distribution.2FBenford.27s_Law https://en.wikipedia.org/wiki/Results_of_the_Iranian_preside...
anigbrowl 11y ago
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
- newman314 11y ago
  I wonder this is applied to find bad actors in the financial sector...
  - dbarlett 11y ago
    The FDIC has long recommended [1] mandatory vacation blocks as a fraud-detection tool: 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. The idea has spread in recent years [2]. [1] https://www.fdic.gov/news/news/financial/1995/fil9552.html https://www.fdic.gov/news/news/financial/1995/fil9552.html [2] http://www.marketplace.org/topics/business/easy-street/credit-suisse-makes-life-little-harder-aspiring-rogue-traders http://www.marketplace.org/topics/business/easy-street/credi...
    - dopeboy 11y ago
      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.
- 11y ago
dbarlett 11y ago
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.
- deleted 11y ago
  [deleted]
- mturmon 11y ago
  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 http://articles.latimes.com/2005/apr/26/local/me-kingdrew26 Eventually the facility was closed and some of the administrators were canned.
pp19dd 11y ago
So here's what medical fraud looks like: http://i.imgur.com/jMvUqqK.jpg 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.
- 11y ago
rak 11y ago
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.

Axsuul 11y ago

Perhaps big data companies like Palantir are assisting with this?

threeseed 11y ago
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.
- ipsin 11y ago
  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.
  - navait 11y ago
    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.
  - jhorey 11y ago
    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.
- jhorey 11y ago
  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.

kevin_thibedeau 11y ago

My favorite is using Benford's law to find anomalous digit distributions in phony numbers.

jacquesm 11y ago
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.
- compbio 11y ago
  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_presidential_election,_2009#Initial_Digit_Distribution.2FBenford.27s_Law https://en.wikipedia.org/wiki/Results_of_the_Iranian_preside...

anigbrowl 11y ago

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

newman314 11y ago
I wonder this is applied to find bad actors in the financial sector...
- dbarlett 11y ago
  The FDIC has long recommended [1] mandatory vacation blocks as a fraud-detection tool: 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. The idea has spread in recent years [2]. [1] https://www.fdic.gov/news/news/financial/1995/fil9552.html https://www.fdic.gov/news/news/financial/1995/fil9552.html [2] http://www.marketplace.org/topics/business/easy-street/credit-suisse-makes-life-little-harder-aspiring-rogue-traders http://www.marketplace.org/topics/business/easy-street/credi...
  - dopeboy 11y ago
    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.
11y ago

dbarlett 11y ago

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.

deleted 11y ago
[deleted]
mturmon 11y ago
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 http://articles.latimes.com/2005/apr/26/local/me-kingdrew26 Eventually the facility was closed and some of the administrators were canned.

pp19dd 11y ago

So here's what medical fraud looks like: http://i.imgur.com/jMvUqqK.jpg 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.

11y ago

rak 11y ago

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.