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Appl Clin Inform 2025; 16(04): 1281-1291
DOI: 10.1055/a-2616-9858
Research Article

Extracting Social Determinants of Health from Dental Clinical Notes

Authors

  • Farhana Pethani

    1   Biomedical Informatics and Digital Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
    2   Data61, Commonwealth Scientific and Industrial Research Organisation, Sydney, Australia

  • Alec Chapman

    3   Informatics, Decision-Enhancement and Analytic Sciences Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, United States
    4   Division of Epidemiology, School of Medicine, University of Utah, Salt Lake City, Utah, United States

  • Mike Conway

    5   School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia
    6   Centre for Digital Transformation of Health, The University of Melbourne, Melbourne, Australia

  • Xiang Dai

    2   Data61, Commonwealth Scientific and Industrial Research Organisation, Sydney, Australia

  • Demiana Bishay

    7   The University of Sydney School of Dentistry, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia

  • Victor Choh

    7   The University of Sydney School of Dentistry, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia

  • Alexander He

    7   The University of Sydney School of Dentistry, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia

  • Su-Elle Lim

    7   The University of Sydney School of Dentistry, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia

  • Huey Ying Ng

    7   The University of Sydney School of Dentistry, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia

  • Tanya Mahony

    8   Oral Health Services, Nepean Blue Mountains Local Health District, Penrith, Australia

  • Albert Yaacoub

    7   The University of Sydney School of Dentistry, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
    8   Oral Health Services, Nepean Blue Mountains Local Health District, Penrith, Australia

  • Sarvnaz Karimi

    2   Data61, Commonwealth Scientific and Industrial Research Organisation, Sydney, Australia

  • Heiko Spallek

    7   The University of Sydney School of Dentistry, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia

  • Adam G. Dunn

    1   Biomedical Informatics and Digital Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia

Funding This project was supported by the Research Training Program Scholarship provided by the Australian Government, and the Postgraduate Top-up Scholarship provided by the Commonwealth Scientific and Industrial Research Organisation.
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Abstract

Objectives

In dentistry, social determinants of health (SDoH) are potentially recorded in the clinical notes of electronic dental records. The objective of this study was to examine the availability of SDoH data in dental clinical notes and evaluate natural language processing methods to extract SDoH from dental clinical notes.

Methods

A set of 1,000 dental clinical notes was sampled from a dataset of 105,311 patient visits to a dental clinic and manually annotated for information pertaining to sugar, tobacco, alcohol, methamphetamine, housing, and employment. Annotations included temporality, dose, type, duration, and frequency where appropriate. Experiments were to compare extraction using fine-tuned pretrained language models (PLMs) with a rule-based approach. Performance was measured by F1-score.

Results

For identifying SDoH, the best-performing PLM method produced F1-scores of 0.75 (sugar), 0.69 (tobacco), 0.67 (alcohol), 0.42 (housing), and 0 (employment). The rule-based method produced F1-scores of 0.70 (sugar), 0.69 (tobacco), 0.53 (alcohol), 0.44 (housing), and 0 (employment). The overall difference between PLMs and rule-based methods was F1-score of 0.04 (95% confidence interval −0.01, 0.09). SDoH were relatively rare in dental clinical notes, from sugar (9.1%), tobacco (3.9%), alcohol (1.2%), housing (1.2%), employment (0.2%), and methamphetamine use (0%).

Conclusion

The main challenge of extracting SDoH information from dental clinical notes was the frequency with which they are recorded, and the brevity and inconsistency where they are recorded. Improved surveillance likely needs new ways to standardize how SDoHs are reported in dental clinical notes.

Keywords

natural language processing - information extraction - electronic dental records - social determinants of health - dentistry

Protection of Human and Animal Subjects

Ethical approval and waiver of consent for this study was granted by the Nepean Blue Mountains Local Health District Human Research Ethics Committee to The University of Sydney on May 24, 2022 (2022/ETH00578). This study was conducted in accordance with the Australian National Statement on Ethical Conduct in Human Research (2007).


Data Availability

The data underlying this article cannot be shared publicly due to reidentification risks per ethics approval requirements.


Supplementary Material



Publication History

Received: 23 January 2025

Accepted: 20 May 2025

Accepted Manuscript online:
21 May 2025

Article published online:
03 October 2025

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