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About
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.
Full description
The study will enroll up to 70 Dana-Farber patients admitted to Brigham and Women's hospital who have an elevated mortality prediction at admission. The research study procedures include randomization to intervention or control arms on admission (3:1 intervention to control). For patients in the intervention arm, a large language model query will summarize serious illness communication documentation in their medical record screening from the prior 6 months. A summary of the documentation will be sent to the inpatient and outpatient oncology clinicians within 24 hours of admission; these clinicians will be asked to review the summaries, discuss with the patient and incorporate into care plans as appropriate. For patients with no SIC documented on day 4 of the admission, a second email will be sent. Control patients will receive usual care, which is no email sent to their oncology teams. All patients will be offered to participate in an interview on day 4 of their admission about their communication with their care teams. For patients in both arms, inpatient and outpatient oncology attendings will be sent an email survey regarding SIC for the patient and, if applicable, the utility of the SIC summaries.
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Inclusion and exclusion criteria
Participant Inclusion Criteria:
Participant Exclusion Criteria:
-Patients with elective inpatient admissions (typically for chemotherapy or other treatments)
Clinician Inclusion Criteria:
-The clinician fills one of the following roles for the enrolled patient:
Primary purpose
Allocation
Interventional model
Masking
60 participants in 2 patient groups
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Central trial contact
Christopher Manz, MD
Data sourced from clinicaltrials.gov
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