Basically there are new parameters to search for fraud detection with medical claims.  To cut through all of this, there’s more algorithms to run claims through and look for certain patterns.  That’s all this is basically in a nut shell.  Using the Optum predictive analytics, which to me a lot of the predictive analytics are not spot on as they use both credible and non credible data to create the so called value in the parameters of a claim. image Everyone needs some type of analytics to find and evaluate potential fraud today that’s a given but where I draw questions is what compiled the results of such analytics as we also end up with false positives and that’s been going on forever, way back before the name change of Ingenix to Optum.  The key here is course to find “real” fraud and not be a source of more red tape to hold up claim payments.  It’s a real race out there as to who can find and sell the most complete package of analytics to do the job..from the press release below…you can see the targeted areas. 

“The solution delivers broad detection capabilities including rules, flags, predictive modeling, text mining and social network analysis to identify possible instances of provider and consumer fraud, including multi-party fraud schemes and organized crime.”

The real key with all analytics is to keep everything in context and that’s not happening everywhere today.  I played around with “Recorded Future” which is a predictive service and they do identify trending but it’s not a perfect science by any means and to apply what they identify down to a personal level and score on it, brings such use out of context.  SAS already had a set of fraud detection analytics so now there’s more algorithms and parameters for medical claims to meet here and again, the quality of such without a load of false positives will determine the real success.  United made some additional money here it appears with the sale and more to tell shareholders as last I read they make less than half of their money today from policy sales and more in that area from obtaining management contracts.  United has so many subsidiaries anymore it’s hard to keep track.  Here’s a company they bought back in 2010 for one example.  SAS has their own fraud analytics as well and one just wonders how many fraud analytics are needed? 

Axolotl (A Subsidiary of Ingenix) Creates Reporting and Analytics Solution for Health Information Exchanges–Algorithms for HIE–Business Intelligence -Subsidiary Watch

I still take a wait and see point of view with value as they company still has many unsettled lawsuits out there with the analytics they used for years to low ball paying out of network charges and Aetna’s participation in the Ingenix (now Optum)  data was noted last week with their settlement of payment.  Software builds on itself and thus so to get where we are today looking at data elements of the past can be helpful as “Recorded Future” can go backwards in a sense to see what the past has been.  So what is the “social network portion of the analytics Optum is supplying one might ask and it’s been established that social network information is not credible as is clinical information by comparison.  

One More Court Case Settles for $120 Million With Aetna For Around To 13 to 15 Years of Short Paying Doctors Using Defunct United Healthcare (Ingenix) Data Base for Out of Network Reimbursements

I see this all the time with marketing on the web and the results of combining credible information with non credible leads to more flaws for us to fix on our own ticket and time. That was one of my items in the Killer Algorithm series about how corporate USA has turned us into “data chasers” while they make billions selling data. So depending how “flawed” some of this information will be, will also determine how much work is out there for the consumer to fix.  Here’s my own Killer Algorithm story with data mining…and yes the agent knows this is going on too and explains it as “that other division of the company”.  It’s amusing to me as I know exactly what happened with finding a match on a table when comparing data bases and the algorithm thought it was smart, but it was dumb when it put the new owners of my home, six months later as a second driver on my car insurance policy…I just tell you to look for this stuff and that’s all I did here. 

Insurance Company Data Mining With Automated Transactions? What Is Being Done With Consumer Data–My Flawed Corporate America Data Shows Up -Attack of the Killer Algorithms Chapter 45


Time will tell I guess on how this works and we will see what the percentage of false positives may be here as it comes up in all fraud detection software and there’s where we dig for the “value” to make sure the algorithms are on target as it’s very complex out there today.  Back in 2009 and I don’t know how this ended up totally but think the lawsuit is still going was a group of dermatologists who got caught up in the Ingenix algorithms and the “claim scoring” and it devastated several practices.  So again today, we need to ensure that there is “value” in the queries and the algorithms to make sure they don’t end up being in a position to where they wreak more havoc than actually finding fraud in some instances. 

Skins game With Dermatology Offices in California – All Insurance Carriers Quit Paying For Treatment Within a 5 Day Period

Last week just as another note, Untied is also looking at doctor’s pay to save money, right about the time we have Congress looking at the annual Medicare cut decision before the end of year (link below) which in the past has been a real mess as doctor’s don’t know what will happen and in one instance the date passed and then a decision was made and the delay cost Medicare and many practices all kinds of additional expenditures all claims were paid with the cuts and then had to be audited to reflect the changes from Congress, kind of an irony of sorts. image It cost Medicare, providers, software analytics companies all kinds of money to revert and I heard about that from many of my sources.  So below the MDs have the potential cuts coming both ways from insurers to the government and the IT infrastructure of the algorithms and queries do all the work with the actual accounting, but the people have to make sure to program the “machines” to do it and in essence this is a press release about computer code.  The price of all the analytics software too also drives up the cost of healthcare so hope we get what’s really needed.  BD 

UnitedHealthCare Looks at Doctor’s Pay for Savings, Nothing New There Been Doing It for Years But Keep In Mind We Have the Annual Medicare Cut Fix on the Floor Again with Congress–Timing?

EDEN PRAIRIE, Minn.--(BUSINESS WIRE)--Optum, an industry leader in health care payment integrity services, is working with SAS to further enhance its comprehensive health care anti-fraud, waste and abuse services. This enhanced solution combines detection, investigation, prevention, case development and recovery services to provide commercial health plans with a flexible approach to ensuring proper payments to care providers.

“Health care payers that adopt an enterprise approach to fraud prevention help their organizations realize immediate operational cost recovery, and enable greater savings over time”

While the vast majority of health care spending reflects the actual costs of patient care and medical services, the National Health Care Anti-Fraud Association (NHCAA) estimates that $60 billion is lost annually to health care fraud, waste and abuse. This figure includes such activities as billing for unperformed medical services; performing a medically unnecessary test or procedure; billing for more expensive medical services or procedures than the one conducted; or billing each stage of a procedure in place of a bundled rate.

The Optum solution uses SAS’s Fraud Framework and Optum’s deep health care expertise and extensive health care claims and fraud case datasets to identify and prevent instances of fraud, waste and abuse for payers. The solution delivers broad detection capabilities including rules, flags, predictive modeling, text mining and social network analysis to identify possible instances of provider and consumer fraud, including multi-party fraud schemes and organized crime.


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