The Urgent and Emergency Care system generates a wealth of clinical information, but our ability to harness this for public health planning and to address health inequalities is constrained by systemic data quality issues. Modern natural language processing (NLP), driven by the context-aware capabilities of transformer-based architectures and large language models, offers a transformative opportunity to bridge this gap. By training machines to interpret and structure context-rich clinical notes at scale, we can translate complex patient stories into data ready for research and systems intelligence that reflects the realities of real-world care. This technology offers a potential route to addressing health inequities in vulnerable populations, such as those presenting with crises related to mental ill-health, alcohol and drug use. Current reliance on structured but oversimplistic data often fails to capture the complex intersectionalities of clinical and social contexts. This is due to factors like diagnostic overshadowing and unrecorded multimorbidity, leaving these patients statistically obscured within routine datasets, which fail to accurately represent volume or complexity. This data invisibility perpetuates a cycle of inaccurate disease burden estimates, under-resourced services and flawed policy. By unlocking the detailed narrative data within unstructured notes, NLP could allow us to identify the acute social stressors and psychiatric contexts that are currently invisible, making these inequities visible and actionable.
Humphries et al. (2026) studied this question.