Clinical Fact Verification Through Semantic Snippets

Fact verification for clinical text often uses atomic decomposition, which splits responses into individual sentences before checking them against a knowledge base. This fragmentation is fatal to clinical logic. It discards vital dependencies such as lab reference ranges, dosage conditions, and causal chains. The MedSNIP framework moves away from this by bundling content into semantically coherent snippets before verification. By preserving the context of conditional medical statements, the framework increases precision in identifying false claims compared to atomic baselines.

The Logic of Predicates

MedSNIP verifies the full logical predicate to maintain clinical safety. Consider the instruction: "Do not take drug X if you have liver disease." Standard atomic systems treat the statement as two independent events: the drug reference and the liver condition. This discards the linguistic structure that binds the prohibition to the health condition. By providing the verifier with the full premise and conclusion in one pass, the system allows the model to process the complete constraint. In practice, MedSNIP yields measurable gains in false-class F1 metrics specifically within these conditional contexts, compared to atomic baselines that miss the causal link.

Constructing Verifiable Snippets

MedSNIP uses a two-stage process to transform raw text into verifiable units. First, the system builds a dependency tree for the input using the spaCy parser to map grammatical relationships. Second, a rule-based algorithm traverses these trees to identify discourse markers such as 'if', 'because', or 'after'.

When the parser flags a marker, the algorithm captures the marker and its dependent child nodes as a candidate span. To distinguish between necessary logical arguments and extraneous text, the algorithm treats the branch as a complete unit until it hits a coordinate conjunction—a word like 'and' or 'or' that links items of equal rank. The algorithm stops at these conjunctions because they typically signal the start of a new, independent clause that likely introduces a separate medical fact or list item. This recursive capture ensures the system keeps the condition and the conclusion together as one block, preventing unrelated instructions from entering the snippet and muddling the logic. The MedSNIP-Bench dataset consists of 276 consumer-health and clinical-vignette responses that were segmented into 2,524 human-annotated snippets to evaluate both boundary detection and fact verification accuracy.

Structural Trade-offs

MedSNIP achieves a 24–73% reduction in verification API calls by verifying these consolidated strings rather than individual sentences. A spaCy dependency parser is a lightweight, rule-based system that operates on a local CPU in milliseconds. The trade-off is mathematically favorable because the cost of this local parsing is orders of magnitude lower than the cost of multiple redundant LLM inference calls. For example, if a clinical paragraph contains four distinct but related clauses, an atomic system would execute four separate verification calls to an LLM. MedSNIP groups these into two logical snippets, halving the API overhead. This gain holds until the snippet length exceeds the optimal token window for the verifier, at which point the increased processing cost per token begins to offset the savings from reduced request frequency.

Limitations and Unknowns

MedSNIP relies on standard grammatical structures to group text effectively. Performance degrades when encountering clinical shorthand or electronic health record notes that lack clear syntax. The dependency parser struggles with nested, multi-layered dependencies, which leads to fragmented snippets that lose critical context. The system requires a minimum level of syntactic coherence; if the input is too sparse or uses non-standard abbreviations, the parser fails to identify discourse markers, causing the system to default to the less effective atomic verification. Future development must address how the system scales to implicit logical relationships that lack explicit markers, as the current rule-based heuristic is restricted to identifiable discourse keywords.