The announcement is made but there’s not a lot of details on the process.  I am guessing that this could also be marketed to work with other systems as well as their own HER/HIE services they market. since we imageare talking software as a service.    What we are talking about is a large searching function here versus having to reconstruct  relational data base information to move across large data sets.  With HIEs there are different ways to connect and systems differ as well with either just connecting and some store and save the data as well when connecting to various hospital/doctor networks.  This will be interesting to follow up with when more details become available. 

The platform solution carries a patent in being the first of it’s kind in the industry.  Again being a platform to work with EHRs and HIE systems one reference number would be able to span across various records to from  several “connected” sources to consolidate on one dashboard or screen for all relative patient information if they were seen or treated at different facilities and locations.  You may have heard of Hadoop, the data base used for big data and there are several platforms being written in to work with the data, as a few weeks ago someone wrote an SQL platform so it would allow developers who are experience in SQL to create solutions using the language they are proficient with and utilize the information stored in a Hadoop data system.  BD

LOS ANGELES, CA, Mar 11, 2013 (MARKETWIRE via COMTEX) -- 4medica today introduced the industry's first Master Patient Index (MPI) engine based on big data technologies. A revolutionary, patented solution, the 4medica Big Data MPI increases precision by applying innovative technologies, while reducing duplication in patient information and scaling for the big data engendered by today's ACOs and HIEs.

The 4medica Big Data MPI is significantly more efficient, more accurate and less expensive than conventional MPIs. An inverted index algorithm and Big Data storage structure allow it to evaluate tens of millions of patients, searching, isolating and finding duplicates 100 times more efficiently.

The solution adjusts probability-scoring algorithms dynamically, employs multiple algorithms to evaluate different data sources and uses historic data to improve precision. Conventional relational-database-based MPIs would require massive computational resources to do the same tasks. Available as a cloud-based SaaS model or as a locally hosted MPI system, 4medica Big Data MPI reduces providers' technology investments, further lowering costs.


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