OpenI plug-in provides a better UI for querying and downloading patient data from i2b2.
i2b2 (Informatics for Integrating Biology and the Bedside) is an NIH-funded scalable informatics framework. It enables researchers to use existing clinical data for discovery research and has wide international adoption.. It consolidates various clinical and biological (including genome) data and allows mapping to standard metadata. Once clinical research data sources aggregated into i2b2 framework are mapped to standard “ontologies”, i2b2 provides a web-based query interface to get patient counts matching various medical/biological conditions defined using standard terminology.
OpenI plug-in essentially integrates its business intelligence dashboard and reporting features to i2b2 data set. This way, instead of issuing queries one by one to find potential patient cohorts for a research, users get more of a "top down" view of the data, which they can slice and dice visually, and perform ad hoc anlaysis without having to write their own queries.
OpenI plug-in will require building OLAP cubes from the i2b2 data in order to support the ad hoc drag-and-drop type analysis you saw in the demo. Depending on your data volume, it can either be done on the same machine, or would require a separate server.
Based on how many concepts you currently have in your Concept dimension (e.g. demographics, diagnosis, labs, medication, etc) -- the ETL job in OpenI will need to be configured (written as a Kettle job, which is the ETL tool within Pentaho) to pull data out of i2b2 accordingly and lay it out in a dimensional data model, off which we will then define and process the OLAP cubes.
Additionally, we assume that there will be 1 server machine to install Pentaho with OpenI plug-in. We also need an relational database to house the dimensional data schema that is used by the OLAP cubes. This can either be done on the same server machine where Pentaho + OpenI plug-in is installed, or if you already have a managed relational database server, we would recommend using that instance since it should be easier for your DBA to manage this database.
So, architecturally speaking, we need a server-class machine (starting at usually a quad processor with 8 to 16 GB of memory) with adequate disk space (we prefer faster disk speeds or a SAN) to house the data coming from i2b2 and build the OLAP cubes. This machine will connect to your i2b2 database to periodically pull data out, and will serve an HTTP interface for your researchers to access the dashboard and reports. The machine will have the following key software components: