Clinical NLP increasingly relies on electronic health record (EHR) datato detect suicidal behaviors, treating clinical documentation as morereliable ground truth than social media. We argue that this framingobscures how EHR-based suicidality datasets encode a particularoperationalization of suicidality, shaped by who authors the data,how episodes are bounded, and how ambiguity is resolved. We groundthis argument in a case study of the ScAN dataset,built over MIMIC-III clinical notes. We show how governanceconstraints, ICD-based cohort selection, single-annotator labeling,and hospital-stay-level aggregation produce labels that foregroundclinician judgment, treat suicidality as a bounded episode, andassume that intent can be reliably inferred from documentation. Alinguistic analysis demonstrates that identical labels subsumeheterogeneous clinical framings differing in temporality, negation,and uncertainty, and that labeling patterns differ across insurancestatus. We argue the clinical NLP community should examine theassumptions embedded in suicidality datasets before interpretingtheir labels as ground truth.

Tuberculosis (TB) remains a major global health challenge, and treatment adherence continues to be difficult despite the availability of effective medication. While Digital Adherence Technologies (DATs) have improved monitoring and care coordination, prior deployments highlight unmet needs for timely, personalized, and emotionally supportive communication outside clinical settings. We develop and iteratively refine a Spanish-language TB treatment-support chatbot through multiple rounds of internal expert evaluation. The system separates three core functions: (i) TB information support grounded in curated resources, (ii) coping-oriented support inspired by Dialectical Behavior Therapy (DBT), and (iii) safety-critical crisis handling via a deterministic, non-generative pathway. These components are implemented within a routed architecture with shared conversational state. Iterative evaluation identified recurring failure modes in unstructured conversational systems, including weak grounding, poor multi-turn continuity, and inconsistent safety behavior. Addressing these issues motivated explicit routing, state tracking, and task-specific prompting. Our findings suggest that in clinical support settings, reliable conversational behavior depends on structured interaction design and explicit control over routing, memory, and safety, rather than on model capability alone.