In this chapter, the considerations related to the technical application of cancer synoptic reporting forms with SNOMED CT are presented. It explores the emerging use of FHIR for standardized model representation, comparing creating new FHIR resources to using FHIR Questionnaires. Benefits and limitations of each approach are outlined, with a focus on the practicality and advantages of FHIR Questionnaires in most scenarios.

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There is no specific FHIR Resource applicable for Synoptic Cancer Reporting, and the diversity of information required in different types of cancer makes it very difficult to use a single FHIR resource; this leads to the need for a specific FHIR resource for every kind of cancer.

Creating a new FHIR resource is a collaborative and iterative process. It requires significant engagement with the healthcare community to ensure that the resource is both technically sound and clinically relevant. The ultimate aim is to facilitate interoperability and improve patient care by representing health data in a standardized and meaningful way.

These are the typical steps involved in the creation of a new FHIR Resource:

1. Identification of Need :

   1. Consult with oncologists, pathologists, and IT professionals. Review current FHIR resources to ensure there's no overlap with existing structures concerning synoptic cancer reporting.

2. Initial Research :

   1. Gather templates and standards currently used in synoptic reporting. Identify unique data elements necessary for the report.

3. Drafting the Proposal :

   1. Define the data elements, structure, and relationships of the new resource. Document the purpose and use cases of the proposed resource.

4. Community Engagement :

   1. Share the draft proposal with relevant FHIR workgroups to gather feedback. Refine the proposal based on the insights and suggestions from the community.

5. Development & Prototyping:

   1. Utilize FHIR development tools to model and prototype the new resource. Ensure that it aligns with existing FHIR guidelines and conventions.

6. Documentation :

   1. Provide detailed information about the resource, including its purpose, structure, and examples, to assist future implementers.

7. Formal Review :

   1. Submit the resource for review by official FHIR governance bodies. Address any suggestions or concerns raised by HL7 committees.

8. Trial & Feedback:

   1. Implement the new resource in real-world healthcare settings. Gather feedback from these implementations and refine the resource accordingly.

9. Standardization Process :

   1. Push for the inclusion of the resource in future FHIR standards. Engage with the FHIR community and stakeholders to promote its adoption.

10. Maintenance : 1. Regularly review and update the resource, considering new clinical insights or technological advancements.

11. Promotion & Training: 1. Develop training materials or sessions for the new resource. Engage with health IT and clinical communities to increase awareness and understanding.

The decision to use FHIR resources for this Use Case will provide great specificity and level of detail in the captured data and great uniformity between implementations. However, the process of creating resource specifications from scratch is slow, taking many months to develop each resource. The stability of the resources, only updating in new FHIR versions and not independently, also complicates the need for continuous improvement or adaptations to new requirements.

Three approaches for cancer synoptic data recording are described in this section: paper-based forms, distributed electronic forms, and a centralized reporting platform. Each of these approaches has benefits and drawbacks, which are described below. In addition, the emerging Fast Healthcare Interoperability Resources (FHIR) model is an elegant hybrid of central registry reporting and electronic health system integration. Although FHIR implementations for cancer synoptic reporting are in pilot phases only, the approach is also described.

Paper-based Approach

Structured, synoptic cancer reporting can be realized using a paper-based system. Multiple organizations release paper-based forms for cancer synoptic reporting.

Pathologists can manually record their observations using these pro forma templates. This approach does provide structure and enhances the completeness of data records. However, it does not directly render cancer pathology data into computable form. That can only happen with a transcription or abstraction of the paper form into an electronic system that is encoded using SNOMED CT.

The limitations of this approach are readily apparent. Yet, in an environment where electronic health record systems are not readily available, this approach to pathology cancer reporting can be effective for completeness of reports for immediate use by clinical care teams, and these forms can be used by public health authorities to populate central cancer registries for surveillance and disease management efforts.

Distributed Approach: Electronic Health Record and Laboratory Information System Integration

This approach requires that publishers of cancer pathology data sets render their protocols (pro forma templates) into a format that can be ingested and used by EHR and LIS software platforms. The EHR/LIS vendor software then use these electronic representations to create an electronic version of the paper form for the user to complete as part of their usual reporting workflow.

Benefits of this approach:

• Structured, encoded cancer pathology reporting is fully integrated into usual pathology documentation/reporting workflows

• Centralized management and distribution of curated content

• Ability to customize workflow within institutional EHR/LIS

• Data can be incorporated into the EHR/LIS data structure for clinical decision support, analytics, and electronic reporting to public health registries

Limitations of this approach:

• Relies on a standard interoperability framework

• The approach relies on software vendors to implement content in accordance with the publisher's intent

• The approach relies on software vendors to incorporate encoded pathology cancer data into EHR/LIS data models

• Variation in implementation quality and capability

Example

In the United States, the College of American Pathologists (CAP) developed a process to render their published reporting protocols into a XML documents that electronic health records (EHR) and Laboratory Information System (LIS) vendors. This approach was unique to the US and Canada for many years. It is now expanding into other parts of the world through middleware vendors that customize cancer pathology datasets for incorporation into EHR/LIS workflows. It is the longest-standing electronic method of capturing cancer pathology reports in a computable fashion at the time of report generation.

In this approach, pathologists interact with a centralized application rendering the specific reporting form. Upon completion of the form, the data is fully encoded and stored within the central cancer registry. A PDF or other electronic form of the report is sent back to that pathologist for incorporation into the patient's medical record.

Benefits of this approach:

• Central management of cancer pathology reporting data sets

• Immediate incorporation into the registry

• Sophisticated user interface logic is easily incorporated into the user interface to optimize workflow efficiency, ensuring only the required data elements are presented for pathologist recording.

• Possible to integrate workflow with EHR/LIS as done by PALGA

Limitations of this approach:

• Requires "leaving" EHR/LIS to complete the report. Can be additional work for the pathologist

• Documentation/ entry of data is required in both the EHR and the central web portal

• External report may be returned only as a PDF or other non-computable form

• Computable data not available for local clinical decision support or analysis

Example

This approach is used in the Netherlands by PALGA and is emulated in other nations, which use a centralized web portal for cancer pathology reporting. Here, pathologists navigate to a web portal managed by national cancer registries.

Sharing FHIR Questionnaires

FHIR Questionnaires can be shared between implementations, as their design does not depend on other FHIR resources or external information models. In this way, it is possible to create a repository of FHIR Questionnaires that anyone can access. In the case of Cancer Synoptic Reporting, one group can take the responsibility of transforming the paper-based forms into FHIR Questionnaires and making them available for the community of users.

As an example, the SNOMED International Synoptic Cancer Reporting Clinical Reference Group maintains a GitHub repository with FHIR Questionnaires in: https://github.com/IHTSDO/cancer-synopting-protocol-fhir-questionnaires

This repository records all versions of the questionnaires, with the ability to identify changes and download them for local implementations. Using the collaborative tools in the GitHub repository, it is possible to fork the repository to introduce local customizations, report and discuss bugs and problems in the "Issues" section, and propose questionnaire improvements or changes using the pull request feature.

Rendering Questionnaires in a Clinical Application

It is possible to dynamically create data entry user interfaces based on questionnaire definitions, introducing rich and flexible data capture in clinical applications. Some ready-made open-source libraries can simplify incorporating these functions in clinical software.

This is an example of a Prostate Cancer Form rendered using an open-source library:

SMART on FHIR Integrations

The allow the safe incorporation of a questionnaire rendering "plugin" into commercial clinical software. These plugins are executed locally on the client servers and don't share information with external parties.

The National Library of Medicine of the US has published an example SMART Application that can be adapted to render any questionnaire using this standard:

A clinical application records diagnoses, observations, and procedures. Capturing the same information using a FHIR Questionnaire can lead to model duplication, which risks inconsistencies and complicates data retrieval and analytics later. FHIR Standards proposed by the Structured Data Entry group can help minimize these risks and facilitate the integration of the questionnaires with the rest of the information model.

The use of automatic population helps to reduce the pain of having to fill in the same information 'yet again' by allowing a form to automatically fill in answers already known to the EHR or other data source. The user can then verify that the information is still correct (and revise if necessary) rather than needing to fill out the information all over again (and possibly accidentally omitting or incorrectly entering some data).

Data extraction procedures allow data captured in a QuestionnaireResponse to be extracted and used to create or update other FHIR resources, enabling the data to be more easily searched, compared, and used by other FHIR systems.

Read more about the Structure Data Capture guides .

FHIR Questionnaires contain direct references to SNOMED CT concepts as the codes for questions or responses and references as part of ECL expressions. With each new release of SNOMED CT, it is necessary to validate all referenced content to detect inactivations and make any necessary replacements.

SNOMED International provides a tool for validating terminology bindings, allowing FHIR Questionnaires to be uploaded and inactive codes to be replaced using historical associations published with each release.

In the realm of oncology, the standardized capture of diagnostic, treatment, and outcome data is paramount. FHIR (Fast Healthcare Interoperability Resources) provides a compelling solution through its Questionnaire resource, tailor-made to document information in a structured and interoperable manner. Recognizing the critical role of synoptic reporting in cancer care—a method that ensures consistent and comprehensive documentation—we've compiled a set of FHIR Questionnaire examples specifically geared towards cancer synoptic reporting.

These examples demonstrate how the FHIR framework can be adeptly employed to encapsulate vital oncological data, ranging from tumor characteristics to treatment modalities. For oncologists, pathologists, and health IT professionals aiming to enhance the quality and consistency of cancer reporting, these examples serve as both a guide and a starting point for technical implementation.

These FHIR Questionnaires can be downloaded as JSON files and viewed and edited using any compatible editor. We recommend using the NLM Form Builder ().

Also, SNOMED International provides a utility to store questionnaires in a standard FHIR server and to validate the SNOMED Terminology Bindings to manage updates of SNOMED versions:

Creating a FHIR Questionnaire for synoptic cancer reporting provides a simple and expedited way of representing custom information models with the flexibility of a dynamic specification that can adapt quickly to any required change. Each cancer type will use a new questionnaire definition in this approach, with some sections in common and some sections with specific content. Open-source tooling is available for authoring questionnaire definitions, and the questionnaires can be easily rendered in a clinical application for supporting data capture.

The information entered in a FHIR questionnaire can be shared as a FHIR Questionnaires Response or transformed into a bundle of specific FHIR resources like Observations Resources and others.

Creating a new questionnaire requires several methodical steps. Here’s a step-by-step guide to help you accomplish that:

1. Define the Scope :

   • Understand the specific type of cancer and the information that needs to be captured in the synoptic report.

   • Determine the purpose of the Questionnaire: Is it for diagnostic purposes, treatment monitoring, research, etc.?

2. Gather Information :

   • Gather all the relevant clinical guidelines, standard synoptic templates, and any other resources that can guide the creation of the Questionnaire.

   • Consider input from oncologists, pathologists, and other stakeholders.

3. Design the Questionnaire :

   • Start by identifying the main sections or groups of the Questionnaire. For instance, patient information, tumor characteristics, treatment history, etc.

   • For each section, define the questions, possible answers, and any constraints.

   • Use the appropriate FHIR data types for each question. For instance, use date

4. Use FHIR Tools :

   • Utilize tools like the or any other FHIR-compatible tool to help design, visualize, and test your Questionnaire.

   • These tools can help ensure the Questionnaire is constructed correctly according to FHIR standards.

5. Incorporate Conditional Logic :

   • If certain questions should only appear based on the answers to previous questions, incorporate this conditional logic. For instance, if a specific treatment is selected, additional questions related to that treatment may be necessary.

6. Iterative Testing :

   • Test the Questionnaire in a FHIR-compatible system.

   • Collect feedback from potential users, like pathologists or oncologists.

   • Make necessary revisions based on feedback and testing results.

7. Integrate with EHR Systems :

   • Ensure that the Questionnaire can be integrated into Electronic Health Record (EHR) systems or any other health IT system where it will be used.

   • Consider aspects like how the data will be extracted, how it will be presented to clinicians, and how it will be stored.

8. Training & Education:

   • Once the Questionnaire is ready, provide training to potential users.

   • Create educational materials, guidelines, and best practices for completing the Questionnaire.

9. Continuous Review & Updates:

   • As medical knowledge evolves and new guidelines emerge, the Questionnaire should be reviewed and updated accordingly.

   • Set up regular intervals (e.g., annually) to review and make necessary modifications.

10. Interoperability & Sharing:

    • Consider sharing the designed Questionnaire with the broader medical and FHIR community. This can aid in standardization and promote interoperability.

11. Documentation:

    • Ensure you document the design choices, versions, and updates of the Questionnaire. This documentation is crucial for maintaining and updating the Questionnaire in the future.

12. Compliance & Ethics:

    • Ensure that the Questionnaire meets legal, ethical, and regulatory standards, especially when dealing with sensitive health data.

Remember, the aim of creating a FHIR Questionnaire for synoptic cancer reporting is to capture standardized, structured, and clinically relevant information that can be easily shared, analyzed, and utilized for patient care. It’s crucial to keep end-users in mind throughout the process and prioritize clarity and ease of use.

HL7’s Fast Healthcare Interoperability Resources (FHIR) has emerged as an industry standard for representing and exchanging electronic health data. When dealing with synoptic cancer reports, there are different ways to represent the information using FHIR resources. Two of the options include creating a new FHIR resource or using the FHIR Questionnaire resource. The decision between creating a new FHIR resource or using the FHIR Questionnaire largely depends on the specific needs of the project, the desired level of granularity, and the available resources for development and maintenance. A thorough analysis considering both the short-term implementation and long-term maintenance aspects will help guide the best approach.

New FHIR Resource for Synoptic Cancer Report

Pros

1. Tailored Representation: Creating a bespoke resource allows for a more tailored and granular representation of the specific data elements in synoptic cancer reports.

2. Improved Semantics: Custom semantics can be built into the new resource, ensuring that the meaning of the data is captured more precisely.

3. Standardized Structure: With a custom resource, all implementers would follow the same structured format, promoting consistency across systems.

4. Optimized Queries: Custom indices can be built into the resource, potentially optimizing query performance.

1. Development Time: Creating a new resource requires more time and effort, from design to validation to publication.

2. Adoption Barrier: Introducing a new resource might create an adoption barrier, as systems need to be updated to recognize and process this new entity.

3. Maintenance: There’s a need to maintain and update the new resource in line with FHIR’s evolution and updates in clinical knowledge.

4. Interoperability Challenges

1. Pre-existing Structure: Leveraging the FHIR Questionnaire means using an already defined and recognized resource, potentially speeding up development.

2. Flexibility: Questionnaires are inherently flexible and can be adapted to capture various kinds of data, including that of synoptic cancer reports.

3. Broad Adoption: Since the Questionnaire resource is already a part of the FHIR specification, many systems will already support it, potentially easing integration efforts.

1. Generalized Semantics: As a generic tool, Questionnaires might not capture the specific semantics of synoptic cancer reports as precisely as a dedicated resource.

2. Potential Complexity: Capturing complex clinical data in a questionnaire format can become unwieldy or confusing.

3. Less Optimized: Queries might be less efficient when searching for specific data elements in a generic Questionnaire compared to a custom resource.

The following sections will outline both methods. However, it's important to note that in most cases, the advantages of using FHIR Questionnaires outweigh the reasons for developing new FHIR Resources. The upcoming implementation examples will, therefore, be provided as FHIR Questionnaires.