1. To control text size within the Form Definition in FDN, there is no specific mention of how this can be handled directly in the provided documents. For tables and text-related widgets in dashboards, several options including font size control and text style specifications can be defined (examples include slider controls or input boxes). For implementation in FDN, it might follow similar principles where elements like font size or attributes could be abstracted or extended in certain ways depending on the platform [1].

2. Addressing the caching request for sections and page-based caching as opposed to loading the entire page: Specific techniques for caching sections dynamically are not provided in the retrieved information about FDN usage. However, general principles like using temporary tables or optimized database queries could be applicable to efficiently cache individual sections or pages and improve loading performance [2][3].

You may need to explore further in Community discussions or documentation specific to FDN standards to achieve detailed resolution.

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To achieve your goal of filtering results based on EMS/Ambulance patient arrivals in the ED and using a lookup table for filtering based on PV1;19 values, you can consider the following approach:

1. Using a SearchTable:

   • To create your lookup table, you can base it on the EnsLib.HL7.SearchTable class. This class allows for indexing specific fields within HL7 messages for faster query and rule processing.
   • You would typically:
     1. Copy the EnsLib.HL7.SearchTable class to a new custom class.
     2. Modify or extend the XData block to include specific fields you want to index, such as PV1;19.
     3. Assign this custom SearchTable in your production to the relevant components (like business rules or message routers) [1][2][3][4].

2. Creating the Lookup Table:

   • Your custom SearchTable would define how the PV1;19 values in incoming ADT messages are stored and indexed.
   • Examples and modifications to SearchTables can be found in related documentation and community discussions, allowing you to adjust fields such as PV1:19 for filtering purposes [3][4].

3. Referencing the Table in Business Rules:

   • Once the table is populated with relevant messages based on filtering from PV1;19, you can create business rules that reference this lookup table to decide whether subsequent result messages should be sent or filtered.
   • Ensure your business rule uses the encounter number present in the result messages and checks for its presence in the SearchTable before sending the result to the vendor [4].

4. Populating Data into the SearchTable:

   • Use the BuildIndex() method to populate indices against past messages after configuring the SearchTable. Future messages will be evaluated in real-time as they pass through the production components configured with the SearchTable [4][5][6].

This approach allows for high-performance filtering and aligns with the described requirement of checking result messages against previously processed ADT messages to identify valid cases.

If additional help is needed with specific code examples or validation, the Developer Community or detailed examples provided in documentation can provide further support.

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Based on the scenarios related to logical patients, resource associations, filtering, and business identifiers, there are two approaches available:

1. Custom Operations:

   • A custom operation in InterSystems IRIS enables you to design tailored logic that can filter data, manage tasks, and connect observations or conditions dynamically. This ensures that specific business rules are implemented and executed exactly as intended. Attributes like task ID, patient ID, filtering by date, and associations between tasks and resources can be programmed directly within this operation [1][2].

2. Production Listener:

   • A listener in production is designed to automate and continuously handle incoming data based on established rules. A listener enables asynchronous processing as data arrives and can be used to create tasks, link resources, and apply filters as per the workflow. This is commonly used for applications needing real-time resource management [1][2].

Both methods have merits depending on the workload and business needs:
- Custom operations allow greater control over conditional application logic and interactions with data, especially for complex requirements.
- Listeners are better for production environments requiring automated and continuous data handling and integration with multiple systems.

Based on your scenario, if many transformations and checks are needed per interaction—custom operations might be preferable. Whereas, for scalable, real-time processing, opting for a listener in production is more suitable.

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[1][2]

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