A Meta-Evaluation of Faithfulness Metrics for Long-Form Hospital-Course Summarization

Long-form clinical summarization of hospital admissions has real-world significance because of its potential to help both clinicians and patients. The factual consistency of summaries-their faithfulness-is critical to their safe usage in clinical settings. To better understand the limitations of state-of-the-art natural language processing (NLP) systems, as well as the suitability of existing evaluation metrics, we benchmark faithfulness metrics against fine-grained human annotations for model-generated summaries of a patient's Brief Hospital Course. We create a corpus of patient hospital admissions and summaries for a cohort of HIV patients, each with complex medical histories. Annotators are presented with summaries and source notes, and asked to categorize manually highlighted summary elements (clinical entities like conditions and medications as well as actions like "following up") into one of three categories: "Incorrect," "Missing," and "Not in Notes." We meta-evaluate a broad set of faithfulness metrics-proposed for the general NLP domain-by measuring the correlation of metric scores to clinician ratings. Across metrics, we explore the importance of domain adaptation (e.g. the impact of in-domain pre-training and metric fine-tuning), the use of source-summary alignments, and the effects of distilling a single metric from an ensemble. We find that off-the-shelf metrics with no exposure to clinical text correlate well to clinician ratings yet overly rely on copy-and-pasted text. As a practical guide, we observe that most metrics correlate best to clinicians when provided with one summary sentence at a time and a minimal set of supporting sentences from the notes before discharge.