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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

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Evaluating Large Language Models for Extracting Social Determinants of Health in Substance Use Disorder Notes.

Mollie Hobensack¹, Hwayeon Danielle Shin², Anicca Liu³

¹Vanderbilt University Medical Center, Nashville, TN, USA.

Studies in Health Technology and Informatics

|May 23, 2026

Summary

Automated extraction of social determinants of health (SDoH) using few-shot prompting improves large language model accuracy for substance use disorder (SUD) patients. Further research is needed to address limitations and improve generalizability.

Keywords:

Large Language Models Social Determinants Substance Use Disorder

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Area of Science:

Natural Language Processing
Health Informatics
Substance Use Disorder Research

Background:

Automated extraction of social determinants of health (SDoH) is crucial for identifying patient needs and informing treatment.
Substance use disorder (SUD) care can benefit from timely identification of social factors impacting patient outcomes.

Purpose of the Study:

To evaluate the effectiveness of few-shot prompting in enhancing large language model (LLM) performance for SDoH extraction from clinical notes.
To compare few-shot prompting against zero-shot prompting for SDoH identification in patients with SUD.

Main Methods:

Utilized few-shot prompting techniques with LLMs to extract SDoH information from electronic health records.
Compared the performance metrics of few-shot prompting against zero-shot prompting in the context of SUD patient discharge summaries.

Main Results:

Few-shot prompting demonstrated improved accuracy in extracting SDoH compared to zero-shot prompting.
Identified persistent challenges including low inter-annotator agreement and model hallucinations.

Conclusions:

Few-shot prompting shows promise for automated SDoH extraction in SUD care.
Future research should focus on domain-specific model adaptation, active learning, and validation across diverse patient populations to overcome current limitations.