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Project Overview
Problem:
- Digital oncology measure development/authoring has been constrained by an ecosystem of disparate and unstructured data, as well as a lack of digitalized collections and oncology models to support interoperability.
- Consequently, the majority of oncology quality measures data collection has required manual abstraction resulting in significant burden, and an overall lack of opportunity to share this unstandardized information across systems or organizations.
- Notably, this has also resulted in the authoring and availability of digitalized clinical quality measures for oncology lagging behind other clinical domains.
Description:
Target Outcome:
- Create a solution that demonstrates the ability to author and evaluate digital measures using FHIR data model with mCODE profile extensions and data elements for value-based programs and clinical quality improvement in the Oncology domain.
- This use case will focus on the beginning of a measure’s life cycle and start with quality measure authoring.
- Initial project scope will focus efforts to:
- Author and develop a process for setting up necessary data elements in discrete data fields, to be used for data collection and aggregation.
- Prove that measures can be authored and executed using mCODE, and FHIR, and the correct measure result can be obtained
- This use case will start with the authoring and development of test case bundles using a single measure initially for phase 0
Value:
- The clinical oncology subject matter expertise of the CodeX community, paired with the mCODE profiles and FHIR IGs, should allow for the authoring of these quality metrics and provide a proof of concept for measure developers
- Specifically, there is significant value in demonstrating that quality measure authoring of oncology measures will be easier and more feasible using a FHIR- (and mCODE)-based solution.
- With the expansion of programs focusing on collection of meaningful oncology quality measure data, such as the VA initiatives, the Advancing Cancer Care MVP (MIPS), in support of the Federal Cancer Moonshot Initiative, and the Enhancing Oncology Model (EOM), this CodeX use case team wishes to provide solutions using HL7 FHIR and mCODE to provide a path to data collection with reduced burden.
Project Plan Anchor rttd public project plan rttd public project plan
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Phase | Milestone | Timeline |
|---|---|---|
| Discovery | Use case identification | Complete = in 2 months? |
| Planning | Plan out high level project plan, deliverables, success measures, high level timeline, key stakeholders, etc | Complete = in 6 months? |
| Phase 0 | Test the Telligen Quality Measures Engine (QME) Tooling, as a proof of concept, to determine if QME is successful in authoring and evaluating:
Process:
| Complete = in 6-8 months? |
| Phase 1 | Expand portfolio of VA quality measures and repeat measure mapping, authoring, test bundle creation and testing steps Current Measures include:
| TBD |
| Phase 2 | Expand portfolio by aligning to oncology quality measures in federal models and among oncology steward programs
In addition to aligning to federal models, longer-term VA measures include:
| TBD |
| Phase 3 | Source real world Clinical Data Repository endpoints for measure data source | TBD |
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Quick LinksPlease refer to Quality Measures - Proposal for additional use case details mCODE Implementation Guide (STU 2) |