Research Patient Data Registry - RPDR

The new mi2b2 workbench was very recently released, offering new features and improvements. This release enables researchers of the Partners Community to couple the use of the Research Patient Data Registry (RPDR) to the mi2b2 workbench’s ability to access Radiology material stored in the Partners’ network PACS systems.

What is RPDR?

RPDR is a centralized clinical data registry, or data warehouse. It gathers data from various hospital legacy systems and stores it in one place. Researchers access this data using the RPDR online Query Tool with user-defined queries of RPDR data for aggregate patient totals and, with proper IRB approval, obtain detailed clinical data. Through the RPDR process and the new mi2b2 workbench, researchers can now request Radiology image data along with their patient set. The data delivered to them will include the information they need in order to access the PACS via their personalized mi2b2 workbench. Other features of the newest mi2b2 workbench involve ensured patient protection, direct access to patients' Radiology reports, improved user interface, and a much more automated process of retrieving imaging material.
 
A comprehensive guide of the Image Request procedure can be found here.

Scripts

We have been building python scripts to help with the organizing, formatting, and searching of data returned from an RPDR query or obtained with the mi2b2 workbench.

pyRPDR

https://github.com/nareynolds/pyRPDR

 
Intended audience: These modules were written to help in the exploration, organization, and management of a RPDR data.
 
Intended use: This reads known RPDR data text files, and records it into an SQLite database. This shows the timeline of RPDR events from a specific patient. This allows you to record notes about each patient into the same database.
 
Details: This was written for Python 2.7.X This relies on sqlite3 ( https://docs.python.org/2/library/sqlite3.html ) extensively. The default location of the SQLite database is the directory of the RPDR data. The format of known RPDR tables are defined in the file "rpdrdefs.py" Warning: the format of the text files returned by the RPDR are frequently changed. All common use cases are presented in the file "examples.py"

pyDicomManager

https://github.com/nareynolds/pyDicomManager
 

Intended audience: These modules were written to help in the exploration, organization, and management of a large number of DICOM files.
 
Intended use: This copies or moves DICOM files into a human-readable filetree. This records DICOM header data into an SQLite database with series-level granularity. This allows you to record notes about each series into the same database. This allows you to export selected DICOM files out of the filetree to a location of your choice.
 
Details: This was written for Python 2.7.X This relies on pydicom ( https://code.google.com/p/pydicom/ ) and sqlite3 ( https://docs.python.org/2/library/sqlite3.html ) extensively. All common use cases are presented in the file "examples.py" Settings, such as the location of managed filetree and which DICOM tags are recorded can be set in the file "dicommanagersettings.py" The default location of the SQLite database is in this directory. The default location of the managed filetree is in "data/DICOM". An example of the filetree structure can be seen in "data/DICOM_example_filetree". The python executable "manage_dicoms.py" is a convenience script to execute DicomManager.manage(). It is also a good example of how to use these modules.