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

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.

Participant Inclusion Criteria: * Dana-Farber Cancer Institute participants * Admission to an inpatient solid tumor medical oncology service at BWH (including beds that are considered to be DFCI beds within BWH) * Age ≥ 18 years * Mortality prediction ≥ 40% * Admitted on a Sunday after 4pm, Monday or Tuesday before 4pm. These time restrictions are necessary for the workflow of this pragmatic pilot trial, as an individual will need to send out the intervention emails and interview patients approximately 48-72 hours after admission, both of which need to occur during the work week. 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: * inpatient attending on the medical oncology team * outpatient Dana-Farber medical oncologist