Things to consider when picking your tool

Learning curve

How easy is it to learn how to use the tool for data extraction? Do your data extractors already know how to use it?  How quickly could they learn how to use a new tool? One consideration might be the number of projects you plan on working on with your data extractor team.

If it is multiple projects, it may be worth spending the time getting everybody through the learning curve. If you anticipate working with your data extractor team for just one or two projects, it may not be worth spending time on this.


Once you’ve entered all of your study data into your data extraction tool, how easy is it to export the data in a format that statistical software can read and  analyze?


How much does it cost to use this tool? Don’t forget to factor in how much time you might save; the hours your team spends on a less expensive solution might cost you more than the cost of an effective tool.

Ability to Work Collaboratively

How easy is it for you to access the study data once it’s been extracted by one or more of your data extractors?

Does the software you’re using automatically share data (e.g., SRDR or DistillerSR) or will you have to manually organize data extraction forms (e.g., Word or Excel)? Also, how many people can save study data using the same data extraction tool at the same time?

For example, in our experience, circulating multiple Excel files among team members can create confusion and loss/contamination of data.

With these pointers in mind, let’s take a closer look at some data extraction tool options.