ACTIVATE: AI-driven Clinical-trial Trial-Information and Viability Assessment Tool for EHRs (ACTIVATE)

AI-driven Clinical-trial Trial-Information and Viability Assessment Tool for EHRs (ACTIVATE)

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Cancer
• Intervention / Treatment: Other: MatchMiner-AI Artificial Intelligence Tool

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.

• Study Type: Interventional
• Enrollment (Estimated): 70000
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Kenneth L Kehl, MD; Phone Number: 617-632-4550; Email: kenneth_kehl@dfci.harvard.edu

Study Locations

• United States
  • Massachusetts
    • Boston, Massachusetts, United States, 02215
      • Dana-Farber Cancer Institute

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

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.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Other
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: None (Open Label)

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
No Intervention: Standard MatchMiner-AI Workflow; Oncologists can access the MatchMiner-AI frontend website to obtain a list of clinical trial options for all participants.
Experimental: Proactive AI-Triggered Notifications; 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.	Other: MatchMiner-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.; Other Names: MatchMiner-AI Pipeline

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Proportion clinical trials	The effect of TrialMatch notifications is defined as the proportion of new systemic therapy starts which are clinical trials.	Assessment will occur at the end of the 1.5 year duration of the intervention.

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Proportion clinical trials by race	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.	Assessment will occur at the end of the 1.5 year duration of the intervention.
Proportion clinical trials by ethnicity	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)	Assessment will occur at the end of the 1.5 year duration of the intervention.
Proportion clinical trials by age	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.	Assessment will occur at the end of the 1.5 year duration of the intervention.

Collaborators and Investigators

• Sponsor: Dana-Farber Cancer Institute
• Collaborators: National Cancer Institute (NCI)
• Investigators: Principal Investigator: Kenneth L Kehl, MD, Dana-Farber Cancer Institute

Study record dates

Study Major Dates

• Study Start (Estimated): June 1, 2026
• Primary Completion (Estimated): June 30, 2030
• Study Completion (Estimated): June 30, 2030

Study Registration Dates

• First Submitted: November 14, 2025
• First Submitted That Met QC Criteria: November 14, 2025
• First Posted (Actual): November 18, 2025

Study Record Updates

• Last Update Posted (Actual): April 28, 2026
• Last Update Submitted That Met QC Criteria: April 27, 2026
• Last Verified: April 1, 2026

More Information

Terms related to this study

• Keywords: Cancer; Artificial Intelligence; Electronic Health Records; Clinical Trial Enrollment
• Additional Relevant MeSH Terms: Neoplasms
• Other Study ID Numbers: 25-413; 1R37CA295653-01A1 (U.S. NIH Grant/Contract)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: The Dana-Farber / Harvard Cancer Center encourages and supports the responsible and ethical sharing of data from clinical trials. De-identified participant data from the final research dataset used in the published manuscript may only be shared under the terms of a Data Use Agreement. Requests may be directed to: kenneth_kehl@dfci.harvard.edu. The protocol and statistical analysis plan will be made available on Clinicaltrials.gov only as required by federal regulation or as a condition of awards and agreements supporting the research.
• IPD Sharing Time Frame: Data can be shared no earlier than 1 year following the date of publication
• IPD Sharing Access Criteria: Contact the Belfer Office for Dana-Farber Innovations (BODFI) at innovation@dfci.harvard.edu
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No