Expectations around bigdata’s impact on healthcare is leaping ahead of reality and some good thoughts are being expressed. However, healthcare has already had significant amounts of analytics applied to it. The issue is not that larger sets of data are critical, but that the sharing and integration of the data are the critical parts for better analysis. Bigdata does not necessarily solve these problems although the bigdata fever may help smash through these barriers. Over 15 Blues and most of the major nationals have already purchased data warehouse appliances and advanced systems to speed-up analysis, so its not necessarily performance or scalability that is constraining advances built on data-driven approaches. And just using unstructured text in analytics will not create a leapfrog in better outcomes from data. ...

In my last blog I reviewed some recent patents in the AML/CTF space. They describe what I consider some very rudimentary analytics workflows–fairly simple scoring and weighting using various a-priori measures. Why are such simple approaches patentable? To give you sense of why I would ask this question, there was a great trumpeting of news around the closing of a $6b money laundering operation at Liberty Reserve. But money laundering (including terrorism funding) is estimated at $500 billion to $1 trillion per year. That’s alot of badness that needs to be stopped. Hopefully smarter is better. ...

I was recently reviewing some anti-money laundering (AML) patents to see if any had been published recently (published does not mean granted). Here’s a few links to some patents, some granted some applied for: Method and system to evaluate anti-money laundering risk (published 2005) Methods of and systems for money laundering risk assessment (2006 granted) Method of ranking politically exposed persons and other heightened risk persons and entities (granted 2011) Multi-Channel Data Driven, Real-Time Anti-Money Laundering System For Electronic Payment Cards (published 2013) All of the patents describe a general purpose system of calculating a risk score. The risk score is based on several factors. ...

I receive Google Alerts on BigData in Healthcare as well as social customer service (these are aligned with my professional activities). I received two alerts recently: Why Social Customer Service is the Next Bandwagon to Jump On Social Media Isn’t the Place for Customer Service There are many more like this although I admit, the number of alerts I receive touting social customer service is much larger. ...

I was chatting with someone about health exchanges and they mentioned about building risk models at the individual level. Certainly that’s a good thing. However, according to the Act, 2013 will have adjusted community ratings. Ratings will be based on a few factors at the individual level. The obvious variables are present: smoking and such. But the other variables will include things like age, family size and geography. This is for non-grandfathered (i.e. new) plans and will be in effect for companies less than 100 employees. ...