arXiv:2604.13502v2 Announce Type: replace Abstract: Social Determinants of Health (SDOH) refer to environmental, behavioral, and social conditions that influence how individuals live, work, and age. SDOH have a significant impact on personal health outcomes, and their systematic identification and management can yield substantial improvements in patient care. However, SDOH information is predominantly captured in unstructured clinical notes within electronic health records, which limits its direct use as machine-readable entities. To address this issue, researchers have employed Natural Langua
The increasing maturity of reasoning LLMs and the critical need to extract actionable data from unstructured clinical notes concurrently drive this application. Healthcare systems are also under pressure to improve patient outcomes by addressing social determinants of health more effectively.
This development allows for the automated identification and management of Social Determinants of Health, a critical step towards more personalized and preventative healthcare, enabling better patient outcomes and resource allocation.
Previously unquantifiable and unstructured SDOH data within clinical notes can now be systematically extracted and analyzed, transitioning it from qualitative observation to machine-readable, actionable entities.
- · Healthcare providers
- · AI/LLM developers
- · Public health organizations
- · Patients
- · Manual data extraction services
- · Healthcare systems slow to adopt AI
Healthcare systems gain a granular understanding of individual and population-level SDOH risk factors directly from existing data.
Proactive interventions and resource allocation can be tailored based on identified SDOH, leading to improved health equity and reduced healthcare costs.
The aggregation of structured SDOH data at a population level could inform public policy and urban planning to address systemic health disparities more effectively.
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