Electronic data capture tools can be designed in a way that encourages accurate data collection. Elements of data collection tools that promote quality are discussed in this section.
Many electronic data collection tools allow you to designate required fields. Users are required to complete these fields before submitting the instrument. Required fields should be used to capture information essential to the project, and can be included in surveys completed by participants and forms that study staff use to enter data.
Reminders and Prompts
Reminders and prompts alert users about incomplete fields and can be used as an alternative to required fields. The user has the opportunity to review and complete this field, but can advance regardless.
Required fields are a useful and necessary feature, but it is important to test your instruments and make sure required fields are appropriate, as demonstrated in this scenario:
Your registry data collection tools are complete and you decided to make every field required. What could go wrong?
In your participant-facing survey, you ask a participant for their last recorded blood pressure. The participant does not know his blood pressure. He is now left with two options (1) stop taking the survey without finishing or (2) enter a value that may be incorrect. This is a problem, because the data are either incomplete or incorrect.
There are two ways to avoid this issue:
Free Text Fields
Free texts fields are text fields that allow users to enter any value. Free text fields can be difficult to analyze and answers can be very heterogeneous. Therefore, it is important to carefully consider the use of free text fields.
Let’s say you want to survey participants about symptoms they experience.
To collect this information, you could include a survey question like this: “Please list the symptoms you experienced in the past week” and allow them to answer in a free text field. Your results will likely be quite varied, with some participants writing full paragraphs describing their symptoms and others writing a very brief list. There may also be typos or misspellings that are difficult to understand.
Alternatively, you could include this survey question: “Please select the symptoms you experienced in the past week (check all that apply),” followed by a list of the most common symptoms. With this approach, you receive data that can more easily be compared, but if a participant experienced symptoms not on the list, they will not be able to report it.
Both of these approaches could be appropriate depending on the needs of your registry and overall data collection plan.
Many data collection tools allow users to specify the format or content type for a text field. This feature can help promote data accuracy by reducing unintentional errors. Below are some examples of field validation:
Let’s say you have crucial information, such as a medical record number, which must be entered by you team correctly for every participant. No matter how great your team is, typos happen, so consider using duplicate data entry. This means that study staff must enter crucial information twice to ensure accuracy.
Many concepts can be collected using multiple field types. For example information about age can be collected by asking for birthdate (using a “date picker”), age (using free text), or age range (using radio buttons). See Figure 1 for examples of all three options.
The most appropriate field type depends on the needs of your study. In this example, birthdate is most specific, but also personally identifiable information, and, depending on the source, protected health information. Age gathered through a free text field may be appropriate for situations requiring deidentified data. Age range is likely less helpful for analysis, but can be useful for describing general demographics of participants.
Figure 1: Three possible ways to collect information about age. The best method depends on the needs of your study.
The use of validated and standardized instruments is considered best practice when relevant tools are available, as they support high-quality data collection and enable results to be compared across studies. Examples of validated instruments include the Short Form Health Survey (SF-36), Patient Health Questionnaire 9 (PHQ9), and a variety of Patient-Reported Outcomes Measurement Information System (PROMIS) measures. If you create a new scale or survey instrument and resources allow, it is recommended that you validate it appropriately according to standard process in your field.
The REDCap Shared Library contains existing, validated instruments you can use for your registry. Look for the red star, which denotes a curated instrument approved by the REDCap Library Oversight Committee.
After deciding what data to collect and how to collect those data, it is important to test the tools you created. Below are some suggestions for testing your data collection tools:
Carefully considering data needs, data sources, data collection processes, and tools is important for planning for data quality. Doing so can reduce the long-term burden of maintaining and cleaning data in your registry. However, even with the best planning, there may be unexpected errors. Strategies for identifying and addressing these errors include:
If your registry requires participant follow-up, it is especially important to have accurate and complete contact information. Strategies include:
For other suggestions on how to support registry retention and follow up, see participant retention.