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Overview
The Data Exporter functionality enables an i2b2 user to request patient data for the patients found by a desired query. In the configured workflow, data is requested and generated through new breakdown types that can be selected in the Run Query dialog. Users will see "data request" breakdowns, which send an e-mail to a predefined data manager requesting a type of data. The data manager (Manager user role) processes the request by rerunning the query with a special "data export" breakdown. The patient data is exported as a file and stored in a specified location for retrieval.
The patient data export types, request options, and file specifications are configured in the database using specific request type entries and configuration parameters in the database.
Data Export Workflow
Data Export -Create Data Table (User process)
The Create Data Table feature allows User to design a table of variables of interest using the Design table feature. The table can then be saved as a template that can be loaded and re-used to request data export.
Design Table
The DESIGN TABLE panel allows User to create a data table with list of variables of interest.
User logs into the webclient and accesses Data Export tool under the Analysis Tools plugin
The design table panel displays list of predefined variables that will be included by default in the data export file. These predefined variables are Gender, Age, Race, and Ethnicity
From the Ontology Terms tab, drag and drop additional variables of interest onto the DESIGN TABLE grid. These variables will be added to the list of variables included in the data export. The predefined variables are locked and cannot be unchecked since they are required for every data table. Other variables can be deleted under Actions column to exclude them from the data set file.
Aggregation method for the variable can be applied by clicking on the Value in the Aggregation Method column to assign aggregate options to each variable
AVAILABLE AGGREGATE OPTIONS
The availability of aggregation options depends on the type of concept it aggregates on.
Example:
- Aggregation options for a non-numeric variable such as Diagnosis are Existence (Yes/No), Count: Number of Concepts, Count: Number of Dates, Count Number of Encounters, Count Number of Facts, Count Number of Providers, Date (First), Date (Most Recent), Most Frequent Concept (Codes)
- Aggregation options for concepts that support numeric values such as labs include Average, Min, Max, etc. in addition to all the functions available to normal variables
Aggregation Options | Explanation |
Existence (Yes/No) | Whether the patient has an observation of this concept. This is the default option. |
Count: Number of Concepts | Total number of concepts |
Count: Number of Dates | Total number of dates for the participant |
Count: Number of Encounters | Total number of encounters for the participant |
Count: Number of Facts | Total number of observations |
Count: Number of Providers | Total number of providers for the participant |
Date (First) | Date of earliest observation |
Date (Most Recent) | Date of the most recent observation |
Count | Total number of observations |
All Concepts (Names/Text) | All concept names are listed |
Most Frequent Concept (Names/Text) | Most frequent concept name |
All Concepts (Codes) | All concept codes are listed |
Most Frequent Concept (Codes) | Most frequent concept code |
Minimum Value | Minimum value of all numeric values observations |
Maximum Value | Maximum value of all numeric values observations |
Median Value | Median value of all numeric values observations |
Average Value | Average value of all numeric values observations |
Mode (Most Frequent Value) | Most frequent value for numeric, enum, blob observations |
List of All Values | List of all values for numeric, enum, blob observations |
Preview and Save Table
Once the data table design is complete
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