The Role of AI in Policy Design: A Case Study on Social Determinants of Health
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.
Learning Objectives
- Explain the challenges in translating evidence-based research on Social Determinants of Health (SDoH) into healthcare policies.
- Discuss strategies for leveraging AI and interdisciplinary collaboration to accelerate policy implementation that improves patient care and health equity.
Speaker
- Suresh Bhavnani, PhD (University of Texas Medical Branch)
Subtyping Social Determinants of Health in Cancer: Implications for Precision Healthcare Policies
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.
Learning Objectives
- Analyze how Social Determinants of Health (SDoH) co-occur to form distinct subtypes in the All of Us data.
- Estimate the risk associated with different SDoH subtypes and their impact on health outcomes.
- Evaluate the implications of identified SDoH subtypes for informing and shaping health equity policies.
Speaker
- Suresh Bhavnani, PhD (University of Texas Medical Branch)
SEDoH Information Extraction using Large Language Models
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.
Learning Objectives
- Describe the application of Large Language Models (LLMs) in extracting Social and Environmental Determinants of Health (SEDoH) from unstructured clinical notes.
- Evaluate the benefits, challenges, and limitations of using LLMs for identifying SEDoH in clinical data.
- Analyze the potential future applications of LLMs in improving patient-level risk prediction and monitoring outcomes across diverse subgroups.
Speaker
- David Davila-Garcia, BS (Columbia University Department of Biomedical Informatics)
Investigating the Impact of Social Determinants of Health on Diagnostic Delays and Access to Antifibrotic Treatment in Idiopathic Pulmonary Fibrosis
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.
Learning Objectives
- Identify the types of measurements used to assess the association between Social Determinants of Health (SDoH) and clinical outcomes.
Speaker
- Rui, Li, PhD (UT health)
Enhancing Cross-Domain Generalizability in Social Determinants of Health Extraction with Prompt-Tuning Large Language Models
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.
Learning Objectives
- Describe the limitations of fine-tuning strategies in cross-domain applications of large language models (LLMs) for extracting patient information from clinical narratives.
- Evaluate the effectiveness of prompt-tuned GatorTronGPT in improving cross-domain performance over traditional fine-tuned models.
Speaker
- Cheng Peng, 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
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
Learning Objectives
- Describe the design and implementation of an informatics-enabled framework with enhanced geospatial capabilities for environmental preparedness.
- Explain how the framework improves enterprise resiliency in response to extreme weather events.
- Communicate the outcomes and potential impact of using geospatial informatics to enhance environmental preparedness and disaster response efforts.
Speaker
- Sean Horman, MPA (CVS Health)
Continuing Education Credit
Physicians
The American Medical Informatics Association is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
The American Medical Informatics Association designates this online enduring material for 1.5 AMA PRA Category 1™ credits. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
Claim credit no later than March 10, 2028 or within two years of your purchase date, whichever is sooner. No credit will be issued after March 10, 2028.
ACHIPsTM
AMIA Health Informatics Certified ProfessionalsTM (ACHIPsTM) can earn 1 professional development unit (PDU) per contact hour.
ACHIPsTM may use CME/CNE certificates or the ACHIPsTM Recertification Log to report 2025 Summit sessions attended for ACHIPsTM Recertification.
Claim credit no later than within three years of the release date or within two years of your purchase date, whichever is sooner.