The United States invests nearly double the amount on health care compared to the average OECD country; however, it faces the unfortunate reality of having the worst health outcomes. The issue of health care quality and accessibility is particularly pronounced among marginalized communities and in rural America. While over twenty percent of Americans live in rural areas, the presence of physicians there is limited to just under one-tenth of the total. The federal government predicts a shortage of over 10,000 physicians in rural areas by 2025. Similarly, racial minority communities encounter significant health care barriers, as evidenced by higher rates of chronic disease and premature death compared to white Americans. Many factors contribute to the health disparities and inefficiencies in the U.S. health care system, however, this analysis will focus on the role artificial intelligence (AI) can have in possibly combatting such issues.
The emergence of AI has brought about revolutionary changes across various industries, significantly enhancing efficiency. Literature highlights the incorporation of technological advancements in health care as a means to achieve cost savings and improve health outcomes. Notably, AI can facilitate the integration of precision medicine and social determinants of health, enabling the effective management of population health through extensive dataset analysis and processing, including electronic health records and patient social determinants of health. This approach allows for the formulation of personalized treatment plans. An example of this is the successful implementation of IBM Connect360 and IBM Watson Care Manager by government agencies in Sonoma County, California. Through the deployment of an integrated data hub and care-management solution during a community-wide crisis, health officials and clinicians developed a streamlined and coordinated response for supporting socially disadvantaged groups and displaced individuals.
Chronic disease stands as the primary cause of death and disability in the United States. However, recent breakthroughs in machine learning, particularly in cancer research, offer promise in addressing this issue. Cancer treatment is known for its exorbitant costs, lengthy duration, and low success rates. Machine learning has proven especially effective in medical cancer imaging analysis and screening, as demonstrated in research by Kourou et al.
Approximately 66 million individuals, constituting 20 percent of the U.S. population, do not speak English at home. Effective communication between physicians and patients is paramount; however, language barriers often hinder this relationship. Many individuals within this population, particularly first-generation immigrants, have reported a reluctance to seek regular care due to such barriers. Consequently, patient dissatisfaction ensues, negatively impacting the overall effectiveness of the health care system. Leveraging supportive AI language tools, physicians and patients can engage in real-time conversations automatically translated into their preferred language, ensuring seamless communication.
Telemedicine and remote monitoring have witnessed a surge in usage, particularly during the pandemic. As AI advancements continue to progress, these capabilities will further strengthen. The widespread adoption of personal health tracking devices opens up avenues for extending these practices to clinical environments. Due to advances in AI, real-time monitoring becomes feasible, facilitating proactive doctor-patient interactions and reducing the occurrence of adverse incidents. Furthermore, this data collection empowers clinicians to make more informed decisions, ultimately improving patient outcomes.
However, much work remains to be done to ensure that these AI tools deliver the same or higher quality of care as the rest of the health care team. Previous studies have raised concerns regarding the health care system's ability to navigate the unique technical requirements of AI, as well as the implications for training programs, algorithm biases, and accessibility in rural or underserved areas lacking the necessary infrastructure or expertise to support such tools. Despite the existing barriers, AI has demonstrated tremendous promise, exhibiting the potential to enhance the efficiency and quality of medical treatments and ultimately improve patient outcomes.