This use case aims to make clinical trial participation equitable and easy for all patients and providers. Our approach is to develop mCODE-based open data standards and open APIs that enable interoperable, scalable, and accessible clinical trial matching services that are integrated into existing clinical workflows. This use case will drive awareness of and commitment to use these standards in the industry and improve clinical trial matching for patients and their care teams.

Documentation and an implementation guide will be created to guide matching services in becoming “mCODE-enabled”. This means the clinical trial matching service can receive mCODE records and analyze the records to make matches. Patients and providers will be able to send mCODE records to mCODE-enabled matching services and view the matches in tools they already use (patient data manager applications, EHRs, etc.).

Due to the complexity of clinical trial eligibility criteria, we are focusing on only the most important eligibility criteria in clinical trial matching, which we call our "optimized patient data elements" (OPDE). Our mCODE-enabled matching services will only be analyzing this list of OPDE - not the full patient record. While perfect matches won’t be found by using only the OPDE, the number of clinical trials a patient may match to will be filtered down to a manageable amount for the patient and provider to then manually review. The current list of OPDE includes: cancer type, cancer-subtype, age, biomarker status, stage, presence of metastasis, performance status, and past treatments. This list is being studied and refined. 

Ultimately, the goal of this use case is to ensure opportunities for patients to consider clinical trial participation regardless of where they are initially treated and to identify a larger pool of patients eligible to participate in trials. This will enable recruitment of a more diverse set of patients into trials with a cohort representative of the population of cancer patients. It will also enable clinical trials to meet enrollment targets sooner and lead to more clinical trials being completed in a timely, cost effective and efficient manner. This will ultimately result in greater and faster knowledge generation that can lead to better outcomes for all patients with cancer.

This FHIR implementation guide will use the US Core profiles. If this FHIR implementation guide is unable to use a US Core profile, we will request approval from the US Realm Steering Committee, and provide the US Realm Steering Committee an approved rationale for deviation in the implementation guide where applicable.