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3/12/2025 |
10:30 AM – 12:00 PM |
Urban
S22: Social Determinants of Health
Presentation Type: Podium Abstract
2025 Informatics Summit On Demand
Session Credits: 1.5
Session Chair:
Hongfang Liu, PhD - University of Texas Health Science Center at Houston
The Role of AI in Policy Design: A Case Study on Social Determinants of Health
2025 Informatics Summit On Demand
Presentation Time: 10:30 AM - 10:45 AM
Abstract Keywords: Social Determinants of Health, Advanced Data Visualization Tools and Techniques, Public Health Informatics
Primary Track: Data Science/Artificial Intelligence
Programmatic Theme: Real-World Evidence in Informatics: Bridging the Gap between Research and Practice
Although recent studies have identified how social determinants of health (SDoH) barriers1 co-occur to form high risk subtypes, it is unclear how they can be translated into healthcare policy. Here we conduct a case study to explore with a panel of policy experts, how evidenced-based research on SDoH can be translated into healthcare policies, and the properties of artificial intelligence (AI) methods that facilitate such a translation. This understanding could help to bridge the current gap between data scientists knowledgeable about the rationality underlying the scientific process but with little knowledge of policy making, and conversely policy analysts well-versed in the rationality underlying the policy making process but with little knowledge of AI methods. Such a nexus of AI and policy could help to accelerate the translation of evidence-based research into policies with broad impact to patient care.
Speaker(s):
Suresh Bhavnani, PhD
University of Texas Medical Branch
Author(s):
Brian Downer, PhD - University of Texas Medical Branch; Timothy Reistetter, PhD - University of Texas San Antonio; Joseph Igwe, MD, MPH - Baylor College of Medicine; Shyam Visweswaran, MD PhD - University of Pittsburgh; Sheela Gavvala, DO - Rice University; Sandra McKay, MD - University of Texas Houston; Christopher Kulesza, PhD - Rice University;
2025 Informatics Summit On Demand
Presentation Time: 10:30 AM - 10:45 AM
Abstract Keywords: Social Determinants of Health, Advanced Data Visualization Tools and Techniques, Public Health Informatics
Primary Track: Data Science/Artificial Intelligence
Programmatic Theme: Real-World Evidence in Informatics: Bridging the Gap between Research and Practice
Although recent studies have identified how social determinants of health (SDoH) barriers1 co-occur to form high risk subtypes, it is unclear how they can be translated into healthcare policy. Here we conduct a case study to explore with a panel of policy experts, how evidenced-based research on SDoH can be translated into healthcare policies, and the properties of artificial intelligence (AI) methods that facilitate such a translation. This understanding could help to bridge the current gap between data scientists knowledgeable about the rationality underlying the scientific process but with little knowledge of policy making, and conversely policy analysts well-versed in the rationality underlying the policy making process but with little knowledge of AI methods. Such a nexus of AI and policy could help to accelerate the translation of evidence-based research into policies with broad impact to patient care.
Speaker(s):
Suresh Bhavnani, PhD
University of Texas Medical Branch
Author(s):
Brian Downer, PhD - University of Texas Medical Branch; Timothy Reistetter, PhD - University of Texas San Antonio; Joseph Igwe, MD, MPH - Baylor College of Medicine; Shyam Visweswaran, MD PhD - University of Pittsburgh; Sheela Gavvala, DO - Rice University; Sandra McKay, MD - University of Texas Houston; Christopher Kulesza, PhD - Rice University;
Subtyping Social Determinants of Health in Cancer: Implications for Precision Healthcare Policies
2025 Informatics Summit On Demand
Presentation Time: 10:45 AM - 11:00 AM
Abstract Keywords: Social Determinants of Health, Advanced Data Visualization Tools and Techniques, Data Mining and Knowledge Discovery
Primary Track: Data Science/Artificial Intelligence
Programmatic Theme: Health Data Science and Artificial Intelligence Innovation: From Single-Center to Multi-Site
Although mortality rates for many cancers have declined over the last 20 years, large disparities in cancer-related outcomes persist among subpopulations. Numerous studies in cancer have identified strong associations between specific social determinants of health (SDoH) such as income insecurity, and outcomes such as significantly lower rates of breast screening. However, most people experience multiple SDoH concurrently in their daily lives. For example, limited access to education, unstable employment, and lack of insurance tend to frequently co-occur leading to adverse outcomes such as delayed medical care and depression. Here we analyze how SDoH co-occur across all participants in the All of Us program with a cancer diagnosis, and its implications for designing precision policies to enable more targeted allocation of resources.
Speaker(s):
Suresh Bhavnani, PhD
University of Texas Medical Branch
Author(s):
Weibin Zhang, PhD - University of Texas Medical Branch; Rodney Hunter, PhD - Texas Southern University; Yong-fang Kuo, PhD - University of Texas Medical Branch; Susanne Schmidt, PhD - University of Texas San Antonio; Shyam Visweswaran, MD PhD - University of Pittsburgh; Brian Downer, PhD - University of Texas Medical Branch; Jeremy Warner, MD, MS - Brown University;
2025 Informatics Summit On Demand
Presentation Time: 10:45 AM - 11:00 AM
Abstract Keywords: Social Determinants of Health, Advanced Data Visualization Tools and Techniques, Data Mining and Knowledge Discovery
Primary Track: Data Science/Artificial Intelligence
Programmatic Theme: Health Data Science and Artificial Intelligence Innovation: From Single-Center to Multi-Site
Although mortality rates for many cancers have declined over the last 20 years, large disparities in cancer-related outcomes persist among subpopulations. Numerous studies in cancer have identified strong associations between specific social determinants of health (SDoH) such as income insecurity, and outcomes such as significantly lower rates of breast screening. However, most people experience multiple SDoH concurrently in their daily lives. For example, limited access to education, unstable employment, and lack of insurance tend to frequently co-occur leading to adverse outcomes such as delayed medical care and depression. Here we analyze how SDoH co-occur across all participants in the All of Us program with a cancer diagnosis, and its implications for designing precision policies to enable more targeted allocation of resources.
Speaker(s):
Suresh Bhavnani, PhD
University of Texas Medical Branch
Author(s):
Weibin Zhang, PhD - University of Texas Medical Branch; Rodney Hunter, PhD - Texas Southern University; Yong-fang Kuo, PhD - University of Texas Medical Branch; Susanne Schmidt, PhD - University of Texas San Antonio; Shyam Visweswaran, MD PhD - University of Pittsburgh; Brian Downer, PhD - University of Texas Medical Branch; Jeremy Warner, MD, MS - Brown University;
SEDoH Information Extraction using Large Language Models
2025 Informatics Summit On Demand
Presentation Time: 11:00 AM - 11:15 AM
Abstract Keywords: Social Determinants of Health, Knowledge Representation, Management, or Engineering, Natural Language Processing, Machine Learning, Generative AI, and Predictive Modeling, Data/System Integration, Standardization and Interoperability, Data Sharing/Interoperability
Primary Track: Data Science/Artificial Intelligence
Programmatic Theme: Harnessing the Power of Large Language Models in Health Data Science
In this study we evaluated the ability of ChatGPT-4o-mini to extract three social and environmental determinants of health (SEDoH) indicators (housing stability, substance use and socio-economic status) from clinical notes compared to a manually annotated reference standard, showing extraction with moderate accuracy, precision and recall. The model exhibited a moderate performance in identifying “socio-economic status” highlighting its potential for use in standardizing and integrating SEDoH data into healthcare systems.
Speaker(s):
David Davila-Garcia, BS
Columbia University Department of Biomedical Informatics
Author(s):
Adam Wilcox, PhD - Washington University in St. Louis; Ty Skyles, BS Candidate - Washington University in St. Louis;
2025 Informatics Summit On Demand
Presentation Time: 11:00 AM - 11:15 AM
Abstract Keywords: Social Determinants of Health, Knowledge Representation, Management, or Engineering, Natural Language Processing, Machine Learning, Generative AI, and Predictive Modeling, Data/System Integration, Standardization and Interoperability, Data Sharing/Interoperability
Primary Track: Data Science/Artificial Intelligence
Programmatic Theme: Harnessing the Power of Large Language Models in Health Data Science
In this study we evaluated the ability of ChatGPT-4o-mini to extract three social and environmental determinants of health (SEDoH) indicators (housing stability, substance use and socio-economic status) from clinical notes compared to a manually annotated reference standard, showing extraction with moderate accuracy, precision and recall. The model exhibited a moderate performance in identifying “socio-economic status” highlighting its potential for use in standardizing and integrating SEDoH data into healthcare systems.
Speaker(s):
David Davila-Garcia, BS
Columbia University Department of Biomedical Informatics
Author(s):
Adam Wilcox, PhD - Washington University in St. Louis; Ty Skyles, BS Candidate - Washington University in St. Louis;
Investigating the Impact of Social Determinants of Health on Diagnostic Delays and Access to Antifibrotic Treatment in Idiopathic Pulmonary Fibrosis
2025 Informatics Summit On Demand
Presentation Time: 11:15 AM - 11:30 AM
Abstract Keywords: Social Determinants of Health, Fairness and Disparity Research in Health Informatics, Clinical Decision Support for Translational/Data Science Interventions
Primary Track: Clinical Research Informatics
Programmatic Theme: Emerging Best Practices for Clinical Research Informatics Operations
Idiopathic pulmonary fibrosis (IPF) is a rare disease that is challenging to diagnose. Patients with IPF often spend years awaiting a diagnosis after the onset of initial respiratory symptoms, and only a small percentage receive antifibrotic treatment. In this study, we examine the associations between social determinants of health (SDoH) and two critical factors: time to IPF diagnosis following the onset of initial respiratory symptoms, and whether the patient receives antifibrotic treatment. To approximate individual SDoH characteristics, we extract demographic-specific averages from zip code-level data using the American Community Survey (via the U.S. Census Bureau API). Two classification models are constructed, including logistic regression and XGBoost classification. The results indicate that for time-to-diagnosis, the top three SDoH factors are education, gender, and insurance coverage. Patients with higher education levels and better insurance are more likely to receive a quicker diagnosis, with males having an advantage over females. For antifibrotic treatment, the top three SDoH factors are insurance, gender, and race. Patients with better insurance coverage are more likely to receive antifibrotic treatment, with males and White patients having an advantage over females and patients of other ethnicities. This research may help address disparities in the diagnosis and treatment of IPF related to socioeconomic status.
Speaker(s):
Liwei Wang, MD, PhD
UTHealth
Author(s):
Rui Li, Phd - UT health; Qiuhao Lu, Ph.D. - University of Texas Health Science Center at Houston; Andrew Wen, MS - University of Texas Health Sciences Center at Houston; jinlian wang, PhD - UTHealth; Sunyang Fu, PhD, MHI - UTHealth; Xiaoyang Ruan, PhD - The University of Texas Health Science Center at Houston; Liwei Wang, MD, PhD - UTHealth; Hongfang Liu, PhD - University of Texas Health Science Center at Houston;
2025 Informatics Summit On Demand
Presentation Time: 11:15 AM - 11:30 AM
Abstract Keywords: Social Determinants of Health, Fairness and Disparity Research in Health Informatics, Clinical Decision Support for Translational/Data Science Interventions
Primary Track: Clinical Research Informatics
Programmatic Theme: Emerging Best Practices for Clinical Research Informatics Operations
Idiopathic pulmonary fibrosis (IPF) is a rare disease that is challenging to diagnose. Patients with IPF often spend years awaiting a diagnosis after the onset of initial respiratory symptoms, and only a small percentage receive antifibrotic treatment. In this study, we examine the associations between social determinants of health (SDoH) and two critical factors: time to IPF diagnosis following the onset of initial respiratory symptoms, and whether the patient receives antifibrotic treatment. To approximate individual SDoH characteristics, we extract demographic-specific averages from zip code-level data using the American Community Survey (via the U.S. Census Bureau API). Two classification models are constructed, including logistic regression and XGBoost classification. The results indicate that for time-to-diagnosis, the top three SDoH factors are education, gender, and insurance coverage. Patients with higher education levels and better insurance are more likely to receive a quicker diagnosis, with males having an advantage over females. For antifibrotic treatment, the top three SDoH factors are insurance, gender, and race. Patients with better insurance coverage are more likely to receive antifibrotic treatment, with males and White patients having an advantage over females and patients of other ethnicities. This research may help address disparities in the diagnosis and treatment of IPF related to socioeconomic status.
Speaker(s):
Liwei Wang, MD, PhD
UTHealth
Author(s):
Rui Li, Phd - UT health; Qiuhao Lu, Ph.D. - University of Texas Health Science Center at Houston; Andrew Wen, MS - University of Texas Health Sciences Center at Houston; jinlian wang, PhD - UTHealth; Sunyang Fu, PhD, MHI - UTHealth; Xiaoyang Ruan, PhD - The University of Texas Health Science Center at Houston; Liwei Wang, MD, PhD - UTHealth; Hongfang Liu, PhD - University of Texas Health Science Center at Houston;
Enhancing Cross-Domain Generalizability in Social Determinants of Health Extraction with Prompt-Tuning Large Language Models
2025 Informatics Summit On Demand
Presentation Time: 11:30 AM - 11:45 AM
Abstract Keywords: Machine Learning, Generative AI, and Predictive Modeling, Social Determinants of Health, Informatics Research/Biomedical Informatics Research Methods
Primary Track: Data Science/Artificial Intelligence
Programmatic Theme: Proactive Machine Learning in Biomedical Applications: The Power of Generative AI and Reinforcement Learning
The progress in natural language processing (NLP) using large language models (LLMs) has greatly improved patient information extraction from clinical narratives. However, most methods based on the fine-tuning strategy have limited transfer learning ability for cross-domain applications. This study proposed a novel approach that employs a soft prompt-based learning architecture, which introduces trainable prompts to guide LLMs toward desired outputs. We examined two types of LLM architectures, including encoder-only GatorTron and decoder-only GatorTronGPT, and evaluated their performance for the extraction of social determinants of health (SDoH) using a cross-institution dataset from the 2022 n2c2 challenge and a cross-disease dataset from the University of Florida (UF) Health. The results show that decoder-only LLMs with prompt tuning achieved better performance in cross-domain applications. GatorTronGPT achieved the best F1 scores for both datasets, outperforming traditional fine-tuned GatorTron by 8.9% and 21.8% in a cross-institution setting, and 5.5% and 14.5% in a cross-disease setting.
Speaker(s):
Cheng Peng, PhD
University of Florida
Author(s):
Cheng Peng, PhD - University of Florida; zehao yu - University of Florida; Kaleb Smith, PhD - NVIDIA; Weihsuan Jenny Lo-Ciganic, PhD - University of Florida; Jiang Bian, PhD - University of Florida; Yonghui Wu, PhD - University of Florida;
2025 Informatics Summit On Demand
Presentation Time: 11:30 AM - 11:45 AM
Abstract Keywords: Machine Learning, Generative AI, and Predictive Modeling, Social Determinants of Health, Informatics Research/Biomedical Informatics Research Methods
Primary Track: Data Science/Artificial Intelligence
Programmatic Theme: Proactive Machine Learning in Biomedical Applications: The Power of Generative AI and Reinforcement Learning
The progress in natural language processing (NLP) using large language models (LLMs) has greatly improved patient information extraction from clinical narratives. However, most methods based on the fine-tuning strategy have limited transfer learning ability for cross-domain applications. This study proposed a novel approach that employs a soft prompt-based learning architecture, which introduces trainable prompts to guide LLMs toward desired outputs. We examined two types of LLM architectures, including encoder-only GatorTron and decoder-only GatorTronGPT, and evaluated their performance for the extraction of social determinants of health (SDoH) using a cross-institution dataset from the 2022 n2c2 challenge and a cross-disease dataset from the University of Florida (UF) Health. The results show that decoder-only LLMs with prompt tuning achieved better performance in cross-domain applications. GatorTronGPT achieved the best F1 scores for both datasets, outperforming traditional fine-tuned GatorTron by 8.9% and 21.8% in a cross-institution setting, and 5.5% and 14.5% in a cross-disease setting.
Speaker(s):
Cheng Peng, PhD
University of Florida
Author(s):
Cheng Peng, PhD - University of Florida; zehao yu - University of Florida; Kaleb Smith, PhD - NVIDIA; Weihsuan Jenny Lo-Ciganic, PhD - University of Florida; Jiang Bian, PhD - University of Florida; Yonghui Wu, PhD - University of Florida;
Description and Real-World Outcomes of a Centralized Technology-based Solution to Improve Geospatial Data Capture and Enterprise Resiliency During Extreme Weather Events
2025 Informatics Summit On Demand
Presentation Time: 11:45 AM - 12:00 PM
Abstract Keywords: Geographical Information Systems (GIS), Public Health Informatics, Data Integration, Advanced Data Visualization Tools and Techniques, Social Determinants of Health
Primary Track: Translation Bioinformatics/Precision Medicine
Programmatic Theme: Real-World Evidence in Informatics: Bridging the Gap between Research and Practice
We describe key components of an informatics-enabled, geospatially enriched framework to support operational resiliency and preserve continuity of care for a large, integrated healthcare enterprise. Real-world outcomes from Hurricane Beryl highlight accelerated hyperlocal response enabled by precise geographic identifiers to inform targeted actions and efficient resource distribution, including localized risk assessment, targeted emergency alerts, granular damage assessment, streamlined communication with local partners, and data-informed response and recovery plans. Key competencies required to execute on this framework include a rich data foundation with interoperability; advanced analytics; connectivity in the healthcare ecosystem, including a nationwide community footprint; benefit design; and subject matter expertise
Speaker(s):
Sean Horman, MPA
CVS Health
Author(s):
Amanda Zaleski, PhD, MS - Aetna; Kelly Jean Craig, PhD - CVS Health; Sean Horman, MPA - CVS Health; Patrick Getler, MS - CVS Health; Joshua Wright, Bachelor of Science - CVS Health; Eleanor Beltz, PhD, ATC - Aetna Medical Affairs, CVS Health; Samson Williams, MS - CVS Health; Dorothea Verbrugge, MD - CVS Health; Sreekanth Chaguturu, MD - CVS Health;
2025 Informatics Summit On Demand
Presentation Time: 11:45 AM - 12:00 PM
Abstract Keywords: Geographical Information Systems (GIS), Public Health Informatics, Data Integration, Advanced Data Visualization Tools and Techniques, Social Determinants of Health
Primary Track: Translation Bioinformatics/Precision Medicine
Programmatic Theme: Real-World Evidence in Informatics: Bridging the Gap between Research and Practice
We describe key components of an informatics-enabled, geospatially enriched framework to support operational resiliency and preserve continuity of care for a large, integrated healthcare enterprise. Real-world outcomes from Hurricane Beryl highlight accelerated hyperlocal response enabled by precise geographic identifiers to inform targeted actions and efficient resource distribution, including localized risk assessment, targeted emergency alerts, granular damage assessment, streamlined communication with local partners, and data-informed response and recovery plans. Key competencies required to execute on this framework include a rich data foundation with interoperability; advanced analytics; connectivity in the healthcare ecosystem, including a nationwide community footprint; benefit design; and subject matter expertise
Speaker(s):
Sean Horman, MPA
CVS Health
Author(s):
Amanda Zaleski, PhD, MS - Aetna; Kelly Jean Craig, PhD - CVS Health; Sean Horman, MPA - CVS Health; Patrick Getler, MS - CVS Health; Joshua Wright, Bachelor of Science - CVS Health; Eleanor Beltz, PhD, ATC - Aetna Medical Affairs, CVS Health; Samson Williams, MS - CVS Health; Dorothea Verbrugge, MD - CVS Health; Sreekanth Chaguturu, MD - CVS Health;