Enrollment in clinical trials predicts better survival in the most common cancer types including breast cancer, and testing and proving the efficacy of new treatments relies on successfully conducting clinical trials. However, approximately one fifth of cancer clinical trials fail due to insufficient patient enrollment, and only about 6% of adult cancer patients are enrolled onto clinical trials. Barriers remain for patient participation in clinical trials, especially for cancer patients. One specific barrier is trial identification and awareness of trial availability for both patients and providers.

This trial tests the hypothesis that by integrating clinical trial eligibility screening into part of routine care in a way that requires little effort and by making that screening site agnostic, the Blue-Button matching functionality will increase overall cancer clinical trial enrollment, and may also result in more diverse clinical trial participants that better reflect the U.S. cancer population. Moreover, regardless of changes in enrollment, the Blue-button screening may allow screening to be done more quickly with fewer human resources when compared to current methods. Identification of potential trial opportunities, however, is only the first barrier to trial enrollment, so this study includes additional examination of barriers subsequent to the identification of relevant trials by cataloging patient-level barriers that prevent enrollment of patients in identified trials.

The conduct of cancer clinical trials represents a key final step in testing and proving the efficacy of new treatments relies on successfully conducting clinical trials. However, one in five cancer clinical trials fail due to insufficient patient enrollment, and only about 6% of adult cancer patients are enrolled onto clinical trials, despite the fact that the majority of Americans view clinical trial participation favorably.

The large gap between the willingness of patients to participate in trials and actual trial participate rates suggests the prevalence of many barriers to participation in clinical trials, especially for cancer patients. One specific barrier is trial identification and awareness of trial availability for both patients and providers. Identification and recruitment to clinical trials can be difficult and time consuming. Matching cancer patients with trials requires a challenging amount of manual entry and/or manual review of trials, and frequently this is not integrated into existing clinical workflows. As a result, for many patients, a locally available trial may not be identified, or patients who are eligible to participate may not be asked to enroll. Taken together, only about one in four cancer patients will even have the option to participate in a clinical trial at their institution. Providers do not have the means to easily identify trials for their patients that are conducted outside the treating healthcare institution. One solution to help alleviate this problem is to provide basic site-agnostic trial screening capabilities using electronic health records (EHRs), which already exist in nearly all care settings.

ACS CAN, through a collaboration with the MITRE organization, has developed open-source integrated clinical trial screening functionality ("Blue-button") to address a lack of in-workflow tools for providers to prescreen cancer patients for clinical trials. Blue-button was developed through the Common Oncology Data Elements Extensions (CodeX) application of MITRE's Fast Healthcare Interoperability Resource (FHIR) accelerator (FHIR is a draft data standard developed by HL7 International). The Blue-button functionality works within existing EHRs to enable patients or providers to initiate a prescreen for relevant trials for a given patient within a specified radius of the practice. This is done by automatically extracting and sending eight, deidentified single-patient data elements to existing trial matching services which return potential trial matches. The eight patient data elements used for prescreening, referred to as the optimized patient data elements (OPDE), include age, cancer type, cancer subtype, presence of metastasis, stage, biomarkers, prior treatments, and performance status. The user of the application has the ability to add/edit any of the eight data elements that may be missing or incorrect prior to sending to services. The Blue-button tool (more formally known as the Clinical Trial Matching SMART on FHIR App) then sends the patient OPDE to Minimal Common Oncology Data Elements (mCODE) enabled clinical trial matching services.

Currently, many patients who would be willing to participate in a clinical trial are either not currently being screened or are only screened against limited onsite trials. Thus, the goal of this study is to examine whether the use of a Blue-button trial screening tool can increase the participation of patients in cancer clinical trials. The hypothesis is that the routine use of the site agnostic Blue-button matching functionality will make clinical trial screening easier for both sites and patients. In so doing, its use will not only increase overall cancer clinical trial enrollment but will be especially advantageous for increasing enrollment for historically underrepresented populations, resulting in more diverse clinical trial participants that better reflect the U.S. cancer population. Moreover, regardless of changes in enrollment, the Blue-button screening tool may allow screening to be done more quickly with fewer human resources when compared to current methods. Additionally, because this study follows patients longitudinally over time to record key details about trial decision-making, the study will examine patient-level barriers for individuals eligible for an available trial who decline to participate.

Inclusion Criteria:

• Adult cancer patients attending clinic visit at which they would be screened for clinical trial participation

Exclusion Criteria:

• under age 18 years

We will share all IPD that underlie results in a publication.

Beginning 3 months and ending 3 years after the publication of results

IPD will be shared following review and approval of planned analyses and execution of data sharing agreement.