Exploring the Potential of Large Language Models for Automated Safety Plan Scoring in Outpatient Mental Health Settings

The Safety Planning Intervention (SPI) produces a plan to help manage patients’ suicide risk. High-quality safety plans – that is, those with greater fidelity to the original program model – are more effective in reducing suicide risk. We developed the Safety Planning Intervention Fidelity Rater (SPIFR), an automated tool that assesses the quality of SPI using three large language models (LLMs)—GPT-4, LLaMA 3, and o3-mini. Using 266 deidentified SPI from outpatient mental health settings in New York, LLMs analyzed four key steps: warning signs, internal coping strategies, making environments safe, and reasons for living. We compared the predictive performance of the three LLMs, optimizing scoring systems, prompts, and parameters. Results showed that LLaMA 3 and o3-mini outperformed GPT-4, with different step-specific scoring systems recommended based on weighted F1-scores. These findings highlight LLMs’ potential to provide clinicians with timely and accurate feedback on SPI practices, enhancing this evidence-based suicide prevention strategy.