Harnessing Natural Language Processing and High-Dimensional Clinical Notes to Detect Goals-of-Care and Surrogate-Designation Conversations

Advance care planning, involving goals-of-care and surrogate-designation conversations, is crucial for patient-centered care. However, determining the optimal timing and participants for these conversations remains challenging. This study explored the frequency, timing, and predictors of documenting two advance care planning elements, goals-of-care and surrogate-designation conversations, in clinical notes for patients with advanced illness. In this retrospective observational study, we leveraged high-dimensional data and natural language processing (NLP) to analyze clinical notes and predict the presence or absence of advance care planning conversations. We included notes for patients treated at a Midwestern United States hospital who had advanced chronic conditions and eventually passed away. We manually labeled a gold-standard dataset (