Cancer
Conditions
Keywords
Cancer, Clinical Trial Enrollment, Electronic Health Records, Artificial Intelligence
Brief summary
This study aims to develop and evaluate ACTIVATE, an AI-driven tool for clinical trial information and viability assessment using electronic health records (EHRs). The project will leverage retrospective and prospective EHR data to build and validate algorithms that identify potentially eligible participants for clinical trials and facilitate trial matching.
Detailed description
ACTIVATE is a pragmatic health system intervention designed to improve clinical trial matching and accrual using AI-driven tools integrated with EHR data. The study will first retrospectively analyze data from approximately 70,000 participants who initiated new systemic therapy at Dana-Farber Cancer Institute since 2016 to develop and validate the MatchMiner-AI pipeline. For the prospective evaluation, all DFCI patients' medical record numbers (MRNs) will be randomized into control and intervention groups. The intervention group will receive proactive notifications to treating oncologists when AI models detect progressive disease and a high probability of starting new treatment, including a ranked list of potential clinical trial options. The control group will continue with standard MatchMiner-AI workflows.
Interventions
OTHERMatchMiner-AI Artificial Intelligence Tool
Oncologists receive email notifications containing a ranked list of potential clinical trial options when AI models detect progressive disease, in addition to standard MatchMiner-AI access.
Sponsors
Dana-Farber Cancer Institute
National Cancer Institute (NCI)
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
OTHER
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* 3.1 The potentially eligible patient population includes any adult (≥18 years old) with a cancer diagnosis receiving care at DFCI. No direct patient recruitment will occur as part of this protocol; all data will be obtained retrospectively or prospectively from routine clinical documentation and electronic health records. TrialForecast will involve aggregate queries of this dataset for cohort size estimation. The randomized interventional component (TrialMatch) is a health system level email "nudge" to treating oncologists providing a list of clinical trial options for patients who have progressive disease based on their imaging reports as detected using our previously developed, validated, and deployed AI model for that purpose. 23-25 Secondary outcomes in our study will include oncologist satisfaction with information delivered via these pipelines. All DFCI oncologists at any DFCI-owned/operated site (Longwood, Chestnut Hill, and regional campus sites) will be eligible to use our pipeline and may receive notifications about clinical trial options for their patients. In 2024, there were approximately 593 such oncologists who had outpatient appointments with at least one patient. Clinicians will constitute study participants as well, since they will have the opportunity to provide feedback on our pipeline to be analyzed by the study team. * 3.2 Our project will focus on adults with cancer treated at DFCI, as above. We will not have any mechanism for identifying, targeting, or excluding pregnant women or prisoners.
Exclusion criteria
* 3.2 Our project will focus on adults with cancer treated at DFCI, as above. We will not have any mechanism for identifying, targeting, or excluding pregnant women or prisoners.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Proportion clinical trials | Assessment will occur at the end of the 1.5 year duration of the intervention. | The effect of TrialMatch notifications is defined as the proportion of new systemic therapy starts which are clinical trials. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Proportion clinical trials by race | Assessment will occur at the end of the 1.5 year duration of the intervention. | The effect of TrialMatch notifications is defined as the proportion of new systemic therapy starts which are clinical trials. The outcome will be stratified by race categories of: American Indian/Alaska Native, Asian, Native Hawaiian or Other Pacific Islander, Black or African America, White, and More than One Race. |
| Proportion clinical trials by ethnicity | Assessment will occur at the end of the 1.5 year duration of the intervention. | The effect of TrialMatch notifications is defined as the proportion of new systemic therapy starts which are clinical trials. The outcome will be stratified by ethnicity (Hispanic or non-Hispanic) |
| Proportion clinical trials by age | Assessment will occur at the end of the 1.5 year duration of the intervention. | The effect of TrialMatch notifications is defined as the proportion of new systemic therapy starts which are clinical trials. The outcome will be stratified by Age categories of: 18-29 years, 30-39 years, 40-49 years, 50-59 years, 60-69 years, 70-79 years, 80-89 years, and 90+ years. |
Countries
United States
Contacts
CONTACTKenneth L Kehl, MD
PRINCIPAL_INVESTIGATORKenneth L Kehl, MD
Dana-Farber Cancer Institute
Outcome results
None listed