Evolving Data into Information Requires Standardization{1}


In his article, Perry Francisco, who has extensive Electronic Health Record experience, provides practical reasons for standardizing EHRs.   He mentions the Glascow Coma Scale which is a good example of how data should be structured and standardized.  GCS has standard definitions that are used for eye opening, motor, and verbal response using an atomic alpha data type.  When data is in a format that everyone can use and understand, it can easily be used for better analysis, research and ulitmately to provide the best practices for humanity.  He also provided an example of what is not standardized.   The figure shows the differences in documentation when standardization is not initiated.  You can see how an analysis might vary depending on the data quality.

This article is relevent to this week’s topic of Data Quality and Integration. It shows how important standardization is  to data quality and interpretation.  Not only for database construction but for everyday life.  For example, if you are sick and have more than one doctor each might interpret your information differently.  With standardization,  each person reading your record will have a better understanding of your condition.

I’ve been in Kaiser many times, not personally though, and I am amazed how much information they track for each patient.  Their systems seem amazing but then again I’m not working in the IT department.  It would be a great place to experience a complex database.

Francisco, P. (2011). The Quest for Quality: Turning Data into Information. Nursing Economic$, Vol. 29, Issue 2, p101-103, 3 p.