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.
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.
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.
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.
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.
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.
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.