by Angela Guess
Adam Lorant recently wrote in Datanami, “Modern data science can unlock new innovation in healthcare, bioinformatics, genetic research, and other related fields. New personalized medicine programs, for instance, can identify previously unrecognized disease risk factors by applying analytics to vast amounts of genomic and clinical data. Hospitals can pore through EMR and operational data to pinpoint sources of infection. Public health agencies can use longitudinal population data to more accurately inform policy. These are just a few examples. But all depend on one basic premise: many more researchers and analysts having access to much more data. And many healthcare organizations are still a long way from reaching that goal.”
Lorant goes on, “In most organizations, there is no single place where data resides. Rather, data is diffused across file servers and databases in various locations and multiple formats (Word documents, PDFs, spreadsheets, relational databases, EMRs) that can’t be easily consolidated. Investigators are intimately familiar with the barriers this presents to effective research, and they’re trying to deal with it in various ways: Open-source, web-based services that allow researchers to store some data centrally, but limit the formats and volume of data that can be collected. Enterprise data warehouse suites that can centralize information, but take months of planning and massive capital investment to get up and running. ‘Do-it-yourself’ Hadoop database software projects that leave administrators with the equivalent of a 1,000-piece LEGO kit with no instructions.”
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