The Impact of Distributed Data Management Systems in Healthcare

Medicine doctor hand working with modern computer interface as medical concept

A distributed database management system (DDBMS) used when there are large datasets.  It is a centralized application that manages a distributed database like it is stored all on the same computer.  The logically interrelated databases are used to make the distribution of data transparent to users.  On the other hand, a centralized database system is a database that keeps data all in one single database at one single location.  There are pros and cons to DDBMS versus a centralized database system as it relates to system architecture, system functions and suitable applications.

Many companies have multiple locations that may benefit from DDBMS.  For example, consider a company, like HCA with hospitals located across the country.  Each state may have a different database that holds medical records, appointment history, etc.  The management at each hospital can query that data but the corporate office can also perform queries across the country.  Also, as new hospitals are added to the system, those hospitals can be added to the network without messing up the operations of other sites.

Another benefit of DDBMS includes that users can access data stored at other sites.  For example, use the example of a hospital that is trying to understand the number of falls occurring across the system.  In this instant, that data maybe needs to be queried often from the Chief Medical Officer that then can have the data placed at the site near her potentially enhancing the speed of the database access.

Also, if there is a DDBMS failure at one of the hospital sites, for example, that does not make the entire system breakdown.  DDBMS, unlike a centralized DBMS can function even with these local failures.

However, unlike a centralized DBMS, there is more complexity with a DDBMS in the sense that it hides from the user the distributed nature and allows data replication which if unmanaged can create challenges in reliability and performance.

Another serious risk in the hospital example with DDBMS is security.  In the world of HIPPA where data breaches can be very expensive and brand damaging, it is harder to control security when it is not a centralized approach.

Also, with the field of evidence based medicine advancing (algorithms that determine best case scenarios for treatment plans given the inputs), DDBMS may prove more challenging in execution.

Do the pros outweigh the cons? What has been your experience?

#BigData #DataAnalytics #DDBMS

 

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