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ACTIVE NOT RECRUITING
NCT07147023
NA

Better Real-time Information on Documentation of Goals of Care for Engagement in Serious Illness Communication

Sponsor: Dana-Farber Cancer Institute

View on ClinicalTrials.gov

Summary

The goal of this study is to test the accuracy of Large Language Model-generated serious illness communication (SIC) summaries, the feasibility of delivering the SIC summaries, and to collect perspectives on the SIC summaries from clinicians and participants with cancer. Large Language Models (LLMs) are artificial intelligence programs that can perform various natural language processing tasks.

Key Details

Gender

All

Age Range

18 Years - Any

Study Type

INTERVENTIONAL

Enrollment

60

Start Date

2025-09-15

Completion Date

2026-10-31

Last Updated

2026-02-09

Healthy Volunteers

No

Interventions

OTHER

LLM SIC Summary Email

A Large Language Model-based platform is integrated with the Health Vision Platform, a HIPAA-compliant, electronic health record data management system. Real-time summaries of prior SIC with be sent to clinicians caring for participants in the intervention arm. An email will be sent to the inpatient attending and responding clinician encouraging the team to review the SIC summary, discuss care preferences with the patient and ensure care is aligned with preferences and goals. If no SIC documentation is found, the email will instead encourage the outpatient oncologist to share information regarding undocumented SICs that may have occurred, as well as prompt inpatient and outpatient teams to engage the patient in SIC. For patients with no SIC documentation who are still admitted 72 hours later, the RA will send an additional email prompting SIC or asking the clinician to indicate that an SIC is not appropriate. Patients will also receive standard of care.

Locations (2)

Brigham and Women's Hospital

Boston, Massachusetts, United States

Dana-Farber Cancer Institute

Boston, Massachusetts, United States