Data entry rule characteristics
Characteristic | Description |
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Creating | You create the following rules in the Rules tab: |
About | A data-entry rule is a rule that checks whether data is valid or that sets the value of an item based on a calculation. Different actions occur as a result of the evaluation of the rule expression. For example, an email can be sent or a query can be created. A data-entry rule is part of a study object and not a separate component that is attached to the study object. Rules can be part of study designs, study elements, study events, forms, and items. Because rules are part of study objects, when you reuse a study object from the library, you reuse the rules that are defined on that study object.
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Types | You can create the following types of rules. Depending on the rule, it might be better to use an intrinsic rule, an expression rule, or a function. |
Structure | All data-entry rules consist of three parts.
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A rule that is part of a study object can refer only to:
This concept is called scope. | |
Running order | You cannot set the order in which data-entry rules are run. |
Rule deployment | Rules created with two or more different actions are deployed to the InForm application as multiple rules. For example, a rule might call for:
A different rule is created in the InForm application for each type of action. In the previous example, three rules are created:
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Deciding among hard-coded values, constants, and data mappings | Oracle recommends that you do not use hard-coded values or hard-coded constants in rules, as the rule might be less reusable. Consider using constants or data mappings instead. A data mapping is another name for an item that has been added to a data series and has a global scope for the purposes of rule creation. If the value of an item is constant for every subject in a study, such as lab normal ranges, use constants. For example, if you expect blood pressure ranges to be uniform for all subjects in a study, you can use a constant for blood pressure. If you want to capture dynamic values, such as gender, which varies from subject to subject, use data mappings. For example, acceptable hemoglobin ranges differ for men and women, so it makes more sense to use data mappings for these values in a rule. |
Testing rules | You create test cases in the Rule Test Cases dialog box to test data-entry rules, workflow rules, and global conditions before deploying a study. Depending on the rule, you might create several or many test cases. You can create test cases either after writing each rule or after all rules are finished. To make sure that a rule is written correctly, consider writing and running a single test case for it, so you do not write many test cases for a rule that needs to be modified. |
Naming conventions | You are not required to use a naming convention for rules, but conventions can help you distinguish one type of rule from another and avoid duplicate RefNames. If you want to adopt a naming convention, consider prefixing rules with the following:
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