Automated Identification of Eviction Status From Electronic Health Record Notes
Open accessAbstract
Objective: Evictions are important social and behavioral determinants of health. Evictions are associated with a cascade of negative events that can lead to unemployment, housing insecurity/homelessness, long-term poverty, and mental health problems. In this study, we developed a natural language processing system to automatically detect eviction status from electronic health record (EHR) notes. Materials and Methods: We first defined eviction status (eviction presence and eviction period) and then annotated eviction status in 5000 EHR notes from the Veterans Health Administration (VHA). We developed a novel model, KIRESH, that has shown to substantially outperform other state-of-the-art models such as fine-tuning pretrained language models like BioBERT and Bio_ClinicalBERT. Moreover, we designed a novel prompt to further improve the model performance by using the intrinsic connection between the 2 subtasks of eviction presence and period prediction. Finally, we used the Temperature Scaling-based Calibration on our KIRESH-Prompt method to avoid overconfidence issues arising from the imbalance dataset. Results: KIRESH-Prompt substantially outperformed strong baseline models including fine-tuning the Bio_ClinicalBERT model to achieve 0.74672 MCC, 0.71153 Macro-F1, and 0.83396 Micro-F1 in predicting eviction period and 0.66827 MCC, 0.62734 Macro-F1, and 0.7863 Micro-F1 in predicting eviction presence. We also conducted additional experiments on a benchmark social determinants of health (SBDH) dataset to demonstrate the generalizability of our methods. Conclusion and Future Work: KIRESH-Prompt has substantially improved eviction status classification. We plan to deploy KIRESH-Prompt to the VHA EHRs as an eviction surveillance system to help address the US Veterans’ housing insecurity.
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NLP was applied in five studies to extract SDOH information from unstructured clinical notes.
“NLP was applied in five studies to extract SDOH information from unstructured clinical notes [28, 29, 33, 35] and to compare NLP performance against structured EHR data [27].”
KIRESH-Prompt achieved MCC 0.747 for eviction period prediction.
“KIRESH‐Prompt achieved MCC 0.747 for eviction period prediction; outperformed BioBERT and BioClinicalBERT; planned deployment as VHA eviction surveillance system”
KIRESH-Prompt outperformed BioBERT and BioClinicalBERT.
“KIRESH‐Prompt achieved MCC 0.747 for eviction period prediction; outperformed BioBERT and BioClinicalBERT; planned deployment as VHA eviction surveillance system”
Yao et al. [29] developed an eviction detection model that outperformed state-of-the-art biomedical language models.
“Yao et al. [29] developed an eviction detection model that outperformed state‐of‐the‐art biomedical language models.”
Yao et al. [29] reported a Matthews Correlation Coefficient of 0.747 for eviction status prediction.
“Yao et al. [29] reported a Matthews Correlation Coefficient of 0.747 for eviction status prediction”
Yao et al. [29] and Sato et al. [35] relied solely on clinical note text.
“Yao et al. [29] and Sato et al. [35] relied solely on clinical note text”
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- Authors
- Yao, Z., Tsai, J., Liu, W., Levy, D. A., Druhl, E., Reisman, J. I., & Yu, H.
- Affiliations
- University of Massachusetts Amherst (US)
- United States Department of Veterans Affairs (US)
- University of Massachusetts Lowell (US)
- Center for Health Optimization and Implementation Research (US)
- Corresponding author
- Yu, Hong
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2023-05-02
- Volume
- 30
- Issue
- 8
- Pages
- 1429-1437
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC10354775
- OpenAlex
- W4377094629
- Cited by
- 15
- References
- 28
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 3 · 2023: 3
- FWCI
- 1.41
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Institute of Nursing Research
- National Institute of Mental Health
- National Institutes of Health (1R01NR020868, R01MH125027)
- National Center on Homelessness
- Department of Veterans Affairs Homeless Programs Office
- US Department of Veterans Affairs
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- natural language processing, social determinants of health, housing insecurity, homelessness, veterans, prompt engineering, temperature scaling, class imbalance
- MeSH terms
- Ill-Housed Persons, Housing, Humans, Electronic Health Records
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