The IT Side of the ICD-10 Implementation Equation

Last month I attended the AHIMA Convention in Atlanta, and everywhere I looked it was all about ICD-10. With less than a year left to implement, there will be an increasingly frenzied push to make the Oct. 1, 2014 date. I hope organizations won’t be short-sighted when planning for the IT side of the implementation equation. All too often, solutions are put into place with the goal of meeting an immediate need without a lot of thought about long-term implications. Next year the immediate need will be success at processing claims coded under ICD-10. I worry that within many organizations once that is done the “box will be checked” so to speak and the project will be seen as a success. Obviously in the short-term this part has to go well, but what comes next?

Big Data: Beyond the Buzz

There is a lot of buzz these days about the application of big data and machine learning in variety of industries, including healthcare. Statistics have always played an important part in many aspects of healthcare, from determining effective treatments and evaluating new drugs, to analyzing patient populations for risk. So if statistical analysis is already commonplace in healthcare, why all the hype about big data and machine learning? How are they any different than things we already do? I believe we’re only just beginning to catch a glimpse of how these technologies will transform our industry over the coming decade.

In the past, data for use in analysis has been hard to come by. Because the methods used for analysis worked on a sample or subset of data, it was necessary to randomly select data and to control noise and differences in that data. The acquisition and selection of said data could be time consuming and costly. Even today one might have to manually search paper records and charts to find patients meeting the criteria needed for one’s research. In recent years data is increasingly available in electronic form. This has been driven by the improvement and lower cost of technology, as well as by incentives (such as those contained in the ARRA act) that promote more widespread technology adoption. If anything, the challenge is now becoming that there is too much data to be sifted through by “traditional” statistical approaches. Data now comes from a dizzying number of sources both inside and outside of the care setting that will only continue to grow, hence the term “big data.