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consider: 1) what data elements, while not covered by HIPAA regulations, still need to be protected as they move across data systems; 2) how to decide whether to encrypt and mask data that is both at-rest and in motion; and 3) how to impose masking consistently, so companies can validly analyze data from multiple sources with ease.
Data sets these days are often measured in petabytes. Both vendors and healthcare companies share the goal of “unbounded scalability.” In a recent article, Penn Medicine says it has increased its power to analyze data by linking many commodity computers to produce a petabyte-scale cluster. Penn can now improve its diagnosis of particular diseases, while cutting the time to produce an analysis by 24 hours.
Scale is relevant from a data processing perspective, which is why platforms such as Hadoop, Cassandra and others have become popular. But scale is also important from a data management perspective. In other words, companies not only need to ensure rapid data processing, they also need to ensure the universal availability of data sets of any size to all the people who need them. This is true whether the discussion revolves around a few megabytes emanating from a Runkeeper app to petabytes of clinical trial data.
Data sets are not only becoming larger in scale, but are also increasingly varied, ranging from highly structured clinical trial data to unstructured electronic health records. Companies need to make sure that their diverse data sets are available in native form, and not caged within different proprietary formats that interfere with optimal storage and retrieval.
Prepare Today. Lead Tomorrow.
According to the World Health Organization, worldwide healthcare spending grew 2.6 percent last year—and is expected to continue growing at an average rate of 5.3 percent each year for the next four years. In the U.S. alone, national healthcare spending is expected to surpass $3.2 trillion this year. Improved management of all these new health-related data sets will help streamline care, dramatically minimize inefficiencies, and in the end, reduce overall spend.
Technology bellwethers like Apple and Google are attacking health care challenges with initiatives such as ResearchKit and Calico. This signals a rapid convergence of technology, data and health care.
The time is now. Preparing today supports a tomorrow where data will continue to shape care and improve patient outcomes. Patterns uncovered by data analysis will improve diagnosis and treatment, but only if that data is accurate and readily available to those who need it.
Data will help fuel the next generation of personalized medicine and care. It will inform us in ways that were unimaginable not long ago, and certainly before many legacy systems were built and implemented. The challenges are great, but the opportunities are endless.