AI & SDOH: The Future of Digital Health
AI is revolutionizing healthcare, but its true potential lies in understanding the complete picture of patient health. Integrating artificial intelligence with social determinants of health (SDOH) is paramount. This approach is enabling quicker disease detection and more accurate risk predictions,which can lead to better health outcomes,and is precisely the direction that News directory 3 is tracking. Discover how innovative tools like the HOUSES index, which analyzes housing data, are helping to uncover socioeconomic factors impacting patient care, even when customary measures are unavailable. By addressing the full spectrum of patient circumstances,from living conditions to access to care,we can achieve personalized healthcare and reduce health disparities.Discover what’s next in the world of AI and health.
AI Integration with Social Determinants Improves Health Outcomes
Artificial intelligence is increasingly capable of predicting medical conditions, from diabetic retinopathy to sepsis risk. Though, algorithms are most effective when they inform tangible interventions, according to John Halamka, M.D., president, Mayo Clinic platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform. Addressing medical issues requires understanding patients’ lives outside clinical settings, including social determinants of health (SDOH).
Health professionals are recognizing the importance of social context in health and illness, noted Simukai Chigudu of the University of Oxford. The Centers for Disease Control and Prevention (CDC) offers data to incorporate SDOH into public health and medical practice. One CDC initiative focuses on using electronic health records (EHRs) to integrate structured work details. For example, a house painter’s hypertension might suggest lead poisoning, or a night shift worker’s diabetes may be harder to control.
Mayo clinic is also researching SDOH’s impact. dr. Young Juhn, Director of the AI Program and Precision Population Science lab, has studied socioeconomic status and health since 2006. With NIH support, he developed the housing Based Index of Socioeconomic Status (HOUSES) index. This index uses housing data to understand health disparities, even when socioeconomic measures are absent from medical records.
the HOUSES index uses data points such as the number of bedrooms and bathrooms, square footage, and building value to assess socioeconomic status. It identifies patients at risk of poor health outcomes and inadequate healthcare access. For instance, Stevens et al. found that patients with higher HOUSES scores had a lower risk of kidney transplant rejection. Juhn and colleagues have found that HOUSES can predict 44 different health outcomes and behavioral risk factors.
Even with comprehensive SDOH data, algorithms may not eliminate healthcare disparities. Patients and providers might disregard recommendations due to cost,accessibility,or health literacy. Still, SDOH-enhanced algorithms can improve patient care by supplementing clinical metrics with social factors influencing access to care and long-term outcomes.
What’s next
Future advancements in AI and data integration promise more personalized and effective healthcare strategies, addressing not just the symptoms but also the underlying social factors impacting patient well-being.