Using Artificial Intelligence to Personalize Caring Contact Messages for Recently Discharged Patients: Protocol for a Mixed-Methods Feasibility Study

Suicide risk is substantially elevated following discharge from a psychiatric hospitalization. Caring Contact (CC) messages are brief messages of hope, support and information sent post-discharge that can improve mental health outcomes, including suicidal ideation and behaviours. Patients who received CCs in previous studies indicated a desire for increased personalization. To support personalization in an efficient and scalable manner, we will pilot the training of a Large Language Model (LLM) that constructs tailored CCs using information extracted from patients electronic health records (EHRs) created during the index psychiatric hospitalization. This is a three-phase, mixed-methods study. In phase 1, psychiatric inpatient clinical staff members will be recruited to generate personalized CC messages from representative, de-identified samples of EHRs. This will be considered the control CCs. Clinical staff focus groups will then identify key components of a successful CC message. In phase 2, the control CCs and focus group feedback will be used to train an LLM on a large number of EHRs to produce personalized CC messages. The LLM will be given the same EHRs used in phase 1 to produce CC messages. In phase 3, we will recruit 30 patients with lived experiences (PWLE) of psychiatric hospitalization to evaluate and compare the control CCs with the LLM-generated messages to determine the acceptability of AI-generated CC messages. We hypothesize that LLM-generated CC messages will achieve acceptability ratings at least as positive as the control CC messages. Since CC messages are modifiable and can be altered to suit the needs of various clinical settings, the findings of this study can potentially be broadly generalizable.