I've become an information hoarder. As I spend more time thinking about Information Management and speeding the move to better technical systems, I am amazed how general the principals of design are between the different industries.
Here is a noobs (i.e. me) "plain spoken" understanding of a key term in managing patient data across hospitials and for predicative analytics and personal health decison making.
Here is a noobs (i.e. me) "plain spoken" understanding of a key term in managing patient data across hospitials and for predicative analytics and personal health decison making.
Level setting (i.e. in general the definition of Clinical data warehousing) Clinical data warehousing is a patient identifier organized, integrated, historically archived collection of data.
For the most part the purpose of CDW is as a database for hospitals and healthcare workers to analyze and make informed decisions on both individual patient care and forecasting where a hospital’s patient population is going to need greater care (i.e. patient’s are showing up as obese; therefore the need for specific hospital programs to fight diabetes are a good idea).
Data warehousing in healthcare also has use in preparing for both full ICD-10 and meaningful use implementation. For example; McKesson through its Enterprise intelligence module probably has plenty of CDW management capabilities the only interested in meeting the upcoming ICD-10 and meaningful use deadlines. These kinds of worries are only for US hospitals. However since Canada requires ICD-10 compliance for all EMR systems this does present a benefit to Canadian healthcare.
In principal since data warehousing at its core is about building a relational database and should be EMR supplier agnostic. Since McKesson is an ICD-10 and meaningful use- ready supplier, the database itself should conform to standards that would allow general solutions to be used. This article goes through some of the potential benefits and pain points. It is tailored to clinical trials but the underlying message that building a CDW is a ongoing procedure is the same for other uses.
One example of how this may be done is Stanford’s STRIDE; they used HL7 reference information model to combine their Cerner and Epic databases. This is part of a larger opensource project that may be an option if an organization has some development expertise.
Since the main user of CDWs tends to be the people doing the analysis (current buzzwords for search for analytics include:BI, Predictive analytics, enterprise planning, etc) it is probably useful for Health IT professionals to understand its WHO and WHAT the CDW is for within the organization...i.e. have a full blown Information Governance plan that places a value on information not just a risk assessment.
