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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 qui

by 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.

thaumasiotes 11y ago
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?
- logicallee 11y ago
  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 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...)

by pp19dd 11y ago

thaumasiotes 11y ago

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?

logicallee 11y ago
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 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...)