- REDCap (Research Electronic Data Capture) is a browser-based, metadata-driven software solution and workflow methodology for designing clinical and translational research databases.1
- The Center for Health Insights (CHI), in collaboration with UMKC IT services, supports the use of REDCap in clinical research performed by UMKC researchers and their collaborators.
- Data collection is customized for each study or clinical trial by the research team with guidance from CHI team.
- REDCap is designed to comply with HIPAA regulations.
- Accelerates research by limiting repeated effort and centralizing processes.
Planning a clinical study involving data capture?
- Contact us at email@example.com to receive additional information on REDCap.
- Preview our REDCap Overview video.
- Review our Comprehensive REDCap Guide located in the right sidebar or the FAQ’s provided below.
The following text, recommended by Vanderbilt with modifications for UMKC, can be copied into the data management section of the grant submission.
The Center for Health Insights of the University of Missouri-Kansas City (UMKC) will be used as a central location for data processing and management. Vanderbilt University, with collaboration from a consortium of institutional partners, has developed a software toolset and workflow methodology for electronic collection and management of research and clinical trial data. REDCap (Research Electronic Data Capture) data collection projects rely on a thorough study-specific data dictionary defined in an iterative self-documenting process by all members of the research team with planning assistance from the Center for Health Insights. The iterative development and testing process results in a well-planned data collection strategy for individual studies. The REDCap system provides secure, web-based applications that are flexible enough to be used for a variety of types of research, provide an intuitive interface for users to enter data and have real time validation rules (with automated data type and range checks) at the time of entry. These systems offer easy data editing with audit trails and reporting, monitoring and querying patient records, and an automated export mechanism to common statistical packages (SPSS, SAS, Stata, R/S-Plus).
The following text, recommended by Vanderbilt with modifications for UMKC, can be copied into the data management section of the IRB application.
The Center for Health Insights of the University of Missouri-Kansas City (UMKC) will be used as a central location for data processing and management. Vanderbilt University, with collaboration from a consortium of institutional partners, has developed a software toolset and workflow methodology for electronic collection and management of research and clinical trial data. REDCap (Research Electronic Data Capture) data collection projects rely on a thorough study-specific data dictionary defined in an iterative self-documenting process by all members of the research team with planning assistance from the Center for Health Insights. The iterative development and testing process results in a well-planned data collection strategy for individual studies. REDCap servers are housed in a local data center at the University of Missouri-Kansas City and all web-based information transmission is encrypted. REDCap was developed specifically around HIPAA-Security guidelines. REDCap has been disseminated for use locally at other institutions and currently supports 900+ academic/non-profit consortium partners on six continents and over 138,000 research end-users (www.project-redcap.org).
Please cite the publication below in study manuscripts using REDCap for data collection and management. We recommend the following boilerplate language provided by the REDCap Consortium:
Study data were collected and managed using REDCap electronic data capture tools hosted at the Center for Health Insights of the University of Missouri–Kansas City (UMKC).1 REDCap (Research Electronic Data Capture) is a secure, web-based application designed to support data capture for research studies, providing 1) an intuitive interface for validated data entry; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for importing data from external sources.
1Paul A. Harris, Robert Taylor, Robert Thielke, Jonathon Payne, Nathaniel Gonzalez, Jose G. Conde, Research electronic data capture (REDCap) – A metadata-driven methodology and workflow process for providing translational research informatics support, J Biomed Inform. 2009 Apr;42(2):377-81.
Link to article: http://www.sciencedirect.com/science/article/pii/S1532046408001226
- First, verify your project has been adequately tested to perform as intended.
- All request will be responded to within 48-72 hours. Please allow adequate time for your project to be reviewed and approved for Production in your timeline.
- If your study requires IRB approval letter or exemption determination and you have not already done so, send the IRB approval or exemption for your project to firstname.lastname@example.org. IRB approval or exemption is required for these projects before they are approved for Production. See the next question if you need assistance on how to do this.
- Remember that moving to Production erases all existing practice records, calendar events, and all other associated practice data.
- Once your project has been approved for Production, you will receive an email alerting you to the new status and you may start collecting real data.
In order to approve all human subjects research projects in REDCap for production mode (real data collection), we must have a PDF copy of your IRB eProtocol Approval Letter (even if the Approval Letter states that it is an Exemption Determination).
- Access your IRB eProtocol at https://umkc.keyusa.net/.
- Once you have logged in, click on the protocol ID (this will open a pop-up window)
- Click on “Open in View Mode” and click “OK”.
- In the toolbar on the left side of the pop-up window screen, click on Event History.
- A table will open up to the right with 4 columns: Date, Status, View Attachments, and Letters. In the Letters column, click on the “Approval Letter” link.
- The PDF document will be downloaded to your computer (probably to your default “Downloads” folder). Send this PDF file to your CHI contact, or to email@example.com.
This list provides general guidelines for REDCap database design. When creating a database, keep in mind optimizing for data entry and data analysis.
- Describe the expected input data as well as possible.
- Provide as much information as possible to assist in data entry. Use The Field Label and Field Notes to describe the kind of data intended to be captured in a given data entry field.
- Keep a codebook.
- Create a codebook that describes each variable by name according to the type of data (numeric, date/time, character); the units of measurement (grams, feet, micrograms per deciliter); the purpose of collection and its relationship to other data. The REDCap Data Dictionary should be used as a starting point for the codebook.
- Use the REDCap identifiers to help with HIPAA compliance.
- There are 18 pieces of information that must be marked as identifiers in a REDCap Data dictionary per HIPAA policies.
- Capture the consent information as part of the REDCap project.
- Use a form to store consent information. Important fields to include are:
- Confirmation the subject consented
- Consent date
- Who consented the subject
- Location of the signed consent form
- Use a form to store consent information. Important fields to include are:
- Avoid/reduce the use of free text fields.
- These fields can be difficult to analyze. Use categorical response (dropdown, radio button, checkbox) field types when possible to reduce risk of data entry error. If these fields are not feasible, use text fields with validation (date, phone, email, integer, or number) when possible to reduce the use of free-text fields.
- Do not mix data types.
- Learn about Tidy Data
- Use validation whenever possible with REDCap text fields (e.g., range checking, date format…).
- To improve data quality, use REDCap validation rules. For example, set minimum and maximum values that can be accepted. In addition, use rules to ensure that valid dates are entered.
- Identify units of measure whenever possible.
- Do not expect data entry person to know expected units or formats. Units for measurements should be clearly identified whenever possible. Avoid abbreviations of units of measurements.
- Use field types that minimize changing from keyboard to mouse.Use standard measures and codes.
- Use the REDCap Shared Library as one resource for standard instruments
- Consider recording race and ethnicity according to the current NIH guidelines
- Consider LOINC for laboratory values
- Consider SNOMED-CT for clinical terms and ICD-9 or ICD-10 for medical diagnoses and procedures. ICD-10 is recommended for new data, ICD-9 for historic data.
- Be consistent when assigning numerical codes.
- For example, if “unknown” is coded as 99 in one response, it should be coded as 99 wherever it appears in the database. Note that generally Yes = 1 and No = 0. The numerical code does not affect the order that choices are displayed in the REDCap data entry form.
- Avoid missing values.
- Consider a defined missing value. Missing data can substantially reduce the total analysis sample for many statistical analyses. Does a blank value mean a value still needs to be collected, a value was forgotten, the value is not available, or the value is not applicable? Missing values might be missing for several different reasons and the reasons they are missing might be relevant to the study outcomes.
- Group related variables on the same form.
- Put variables collected together on the same form to improve data entry workflow. Putting demographics together and labs together on separate forms makes data entry more reliable.
1 Paul A. Harris, Robert Taylor, Robert Thielke, Jonathon Payne, Nathaniel Gonzalez, Jose G. Conde, Research electronic data capture (REDCap) – A metadata-driven methodology and workflow process for providing translational research informatics support, J Biomed Inform. 2009 Apr;42(2):377-81.