AI Holds Promise for Improving Health Outcomes
Researchers at Mass General Brigham have revealed a breakthrough study in the potential use of large language models to extract social determinants of health from electronic health records (EHRs). These social determinants, such as employment, housing, and transportation, often go underdocumented in structured EHR data, posing a significant challenge for comprehensive research and clinical care.
According to the study published in npj Digital Medicine, the researchers explored the role of natural language processing in automating the extraction of social determinant information from clinical texts. This could revolutionize data collection and resource allocation, ultimately leading to improved patient care.
The study focused on extracting six categories of social determinants from the text, including employment, housing, transportation, parental status, relationship, and social support. The researchers also addressed algorithmic bias, finding that fine-tuned models are less sensitive to demographic descriptors compared to traditional models.
This groundbreaking research shows that these developed models are not only capable of identifying patients with adverse social determinants of health, but also surpass the capabilities of structured diagnostic codes.
This breakthrough has the potential to improve real-world evidence, aid in patient care, and contribute to a deeper understanding of health disparities driven by social factors. The implications of this study are vast and have the potential to significantly impact the healthcare industry for the better.
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