Chat GPT 4 Assesses a Table of 54 Line Item Z Codes for "MRD" Tests at MOLDX

Using companies like Natera and Inivata, I assembled a 54-row table of Z codes for Minimal Residual Disease tests at MolDx.   (Codes ranged from about 30 to 130 words long).   I asked Chat GPT 4 to assess what's going on.

See master blog here:

https://www.discoveriesinhealthpolicy.com/2024/07/is-moldx-doing-amazing-new-things-with.html

### [Start Chat GPT 4]

The table you've provided lists molecular tests for minimal residual disease (MRD) in oncology, indicating whether they are covered by Medicare (MolDx program). The descriptors for each test are quite extensive and variable. Here's an analysis of what MolDx might be aiming to achieve with these detailed, yet inconsistent descriptors:

Goals of MolDx with Detailed Test Descriptors

1. Precise Identification:

   • Unique Test Characteristics: Each test descriptor includes specific details about the test's methodology, target biomarkers, patient population, and intended use. This level of detail helps in precisely identifying what each test does, which is crucial for coverage decisions.
   • Differentiation: By providing detailed descriptions, MolDx ensures that each test can be clearly distinguished from others, even if they target similar conditions. This differentiation is essential for evaluating the uniqueness and potential value of each test.

2. Clinical Context:

   • Clinical Application: Descriptions often include the clinical context in which the test is used, such as monitoring specific types of cancer. This helps in assessing the test's relevance and utility in clinical practice, which is critical for coverage decisions.
   • Intended Use: Clearly stating the intended use helps determine if the test is appropriate for the intended patient population and if it addresses an unmet medical need.

3. Regulatory and Reimbursement Clarity:

   • Compliance: Detailed descriptors ensure that the tests meet regulatory requirements and provide the necessary information for reimbursement processes.
   • Documentation: Comprehensive descriptions serve as thorough documentation for coverage decisions, providing a clear rationale for why a test is covered or not.

4. Evidence and Validation:

   • Scientific Validity: Detailed descriptions often include the technological basis and the scientific evidence supporting the test. This information is crucial for evaluating the test’s validity and reliability.
   • Outcomes: Including information on the test's impact on patient outcomes helps in assessing its clinical utility and cost-effectiveness.

Issues with Non-Systematic Descriptors

1. Lack of Consistency:

   • The variability in descriptors makes it challenging to compare tests directly. A more standardized approach could improve clarity and facilitate easier comparisons.

2. Complexity:

   • Lengthy and complex descriptions may obscure key information. Simplified, yet comprehensive, descriptors might improve understanding and accessibility for decision-makers and other stakeholders.

3. Potential for Misinterpretation:

   • Inconsistent terminology and structure can lead to misinterpretation of the test’s purpose and utility, potentially affecting coverage decisions.

Conclusion

MolDx appears to be aiming for a comprehensive and detailed documentation of MRD tests to support precise, evidence-based coverage decisions. However, the non-systematic nature of the descriptors might hinder clarity and comparability. Standardizing the format and simplifying the language could enhance the effectiveness of these descriptions. ​