Representing Structured Clinical Data

  • Prakash M. Nadkarni
Part of the Health Informatics book series (HI)


Electronic medical record (EMR) systems illustrate all of the challenges of designing and maintaining large systems. In complexity, their database designs rival those of the Enterprise Resource Planning (ERP) applications. For the large institutional EMRs, the similarity extends to price as well as to the level of institutional commitment required to change your existing processes. When you invest in such a system, you are betting the bank: it’s extremely expensive to change your mind, and deployment failures from ­inadequate customer preparedness can be spectacular. Consider the following issues:
  • Like ERP applications, EMR systems need to interface with a wide variety of third-party software, and they may even interface with ERP applications for areas such as accounting and inventory. However, in terms of database design, their complexity is of a different type. Specifically, an EMR’s customers will demand the need to capture new kinds of data that may not have existed at the time the EMR was purchased. We consider a couple of these situations.


Electronic Medical Record Narrative Text Database Engine Bitmap Index Modern Programming Language 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Prakash M. Nadkarni
    • 1
  1. 1.School of MedicineYale UniversityNew HavenUSA

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