How Does AI Address Social Determinants of Health?

Author: Rasit Dinc

Introduction

Social determinants of health (SDoH) are the non-medical factors that influence health outcomes, such as the conditions in which people are born, grow, work, live, and age [1]. These factors, which include economic policies, social norms, and political systems, have a significant impact on health inequities. The COVID-19 pandemic, for instance, has disproportionately affected socially disadvantaged populations, highlighting the critical role of SDoH in public health [2].

In recent years, artificial intelligence (AI) has emerged as a promising tool for addressing SDoH and advancing health equity. By analyzing vast amounts of data, AI can help us understand the complex interplay between SDoH and health outcomes, identify at-risk populations, and develop targeted interventions. This article explores how AI is being used to address SDoH, the challenges and ethical considerations involved, and the future of AI in promoting health equity.

How AI Addresses Social Determinants of Health

AI offers several avenues for addressing SDoH:

Identifying At-Risk Populations and Personalizing Interventions

AI algorithms can analyze large datasets to identify individuals and communities at high risk for poor health outcomes due to SDoH. Machine learning models, for example, can predict a patient's risk of developing a chronic disease based on their socioeconomic status, education, and neighborhood. This allows for proactive, targeted interventions. Furthermore, AI can personalize these interventions to an individual's specific needs. For instance, an AI-powered mobile app could provide customized recommendations for healthy living based on a user's cultural background and access to resources.

Improving Resource Allocation and Extracting SDoH Data

AI can also optimize the allocation of resources to address SDoH by identifying areas with the greatest need for investment in housing, transportation, and other community resources. A significant challenge in addressing SDoH is the lack of structured data in electronic health records (EHRs). Natural language processing (NLP) can extract this information from unstructured clinical notes, providing a more complete picture of a patient's health. A recent study from Mass General Brigham demonstrated that generative AI models can effectively highlight SDoH in doctors’ notes, which can help identify patients who may benefit from social work support [3].

Challenges and Ethical Considerations

Despite its potential, the use of AI to address SDoH is not without its challenges:

Data Privacy, Security, and Algorithmic Bias

The use of AI in healthcare raises significant concerns about data privacy and security, especially when dealing with sensitive SDoH information. It is also crucial to address algorithmic bias. If AI models are trained on biased data, they can perpetuate or even exacerbate existing health disparities. To mitigate this, algorithms must be trained on diverse and representative datasets and regularly audited for bias.

The Digital Divide and the Need for a Human-in-the-Loop Approach

The digital divide—the gap between those with and without access to technology—could prevent equitable access to the benefits of AI in healthcare. It is essential to ensure that AI-powered tools are accessible to all, regardless of socioeconomic status. Furthermore, AI should be viewed as a tool to augment, not replace, human decision-making. A human-in-the-loop approach, where clinicians are involved in the development and oversight of AI tools, is essential for safe and effective implementation.

Conclusion

AI has the potential to be a transformative force in addressing SDoH and advancing health equity. By providing a deeper understanding of the complex factors that influence health, AI can help us create a healthier and more equitable world. However, it is crucial to navigate the challenges and ethical considerations thoughtfully and responsibly to ensure that AI is used to its full potential to improve the health and well-being of all.

References

[1] World Health Organization. (n.d.). Social determinants of health. Retrieved from https://www.who.int/health-topics/social-determinants-of-health#tab=tab_1

[2] Ong, J. C. L., Seng, B. J. J., Law, J. Z. F., Low, L. L., Kwa, A. L. H., Giacomini, K. M., & Ting, D. S. W. (2024). Artificial intelligence, ChatGPT, and other large language models for social determinants of health: Current state and future directions. Cell Reports Medicine, 5(1), 101356. https://doi.org/10.1016/j.xcrm.2023.101356

[3] Mass General Brigham. (2024, January 11). Generative Artificial Intelligence Models Effectively Highlight Social Determinants of Health in Doctors’ Notes. Retrieved from https://www.massgeneralbrigham.org/en/about/newsroom/articles/generative-artificial-intelligence-models-effectively-highlight-social-determinants-of-health-in-doctors-notes