The pursuit of global health equity has taken on new urgency as healthcare disparities persist and in some cases expand. Today, more than 4.5 billion people, half of the world’s population, lack access to essential healthcare services. It’s not just a “developing world” challenge; nearly 80% of the rural population in the United States remains medically underserved.
This disparity is shaped by a complex interplay of socioeconomic, geographic and systemic factors. As countries, states, counties, and cities attempt to bridge the healthcare gap, AI-enabled solutions have emerged to transform healthcare globally.
The global population has experienced significant growth alongside a wave of urbanization that has reshaped communities. As cities expand and populations swell, healthcare systems worldwide have faced challenges in keeping up with demand, placing increased burden on an already-overworked system.
In sub-Saharan Africa, for example, more than half of the countries do not meet WHO’s minimum recommended doctor-to-patient ratio of 1 doctor for every 5,000 patients. This shortage places immense pressure on both patients and healthcare providers, resulting in longer wait times and reduced access to quality care. As shown in the illustration below, these capacity concerns span the globe.
In response, the healthcare industry has for several years leveraged AI to improve outcomes and enhance service delivery. Multiple AI-powered solutions have entered the market, and as AI technologies have quickly evolved, new use cases have emerged for AI to help reduce global health inequities. Here are four key examples:
One of the sector’s major challenges is an increasing shortage of healthcare workers. The WHO predicts that by 2030, there will be a shortage of 10 million healthcare workers worldwide, with middle to low-income countries being the most affected. Addressing the imbalance doctor-to-patient ratio has become more critical than ever.
AI has helped to address this gap by enabling more efficient and accessible care. For example, AI-powered telemedicine platforms empower healthcare workers to remotely reach more patients in underserved communities. Leveraging cloud computing and data analytics, these platforms provide healthcare workers with valuable insights from genomic to clinical data, enabling health workers to deliver faster, more personalized and more-accurate diagnoses and treatments. This significantly eases the burden on local healthcare providers and ensures improved care with fewer resources.
Healthcare providers and governments are increasingly harnessing the power of AI and data to optimize healthcare delivery and improve patient outcomes. One notable example is the Rural Healthcare Operating System, which helps healthcare providers identify ideal locations for new facilities. By layering and analyzing data on population density, disease prevalence, road network, accessibility, existing healthcare infrastructure, and referral networks this platform helps optimize investments and ensure that healthcare services are accessible to vulnerable and underserved communities.
AI-powered drones are also emerging as valuable tool for improving healthcare delivery in remote and underserved areas. Zipline, a leading drone delivery service, has successfully used drones to deliver blood and essential medicines to rural hospitals in various countries. This innovative approach has significantly reduced delivery times and improved access to critical medical supplies, ensuring that healthcare workers always have the medicines they need.
Along with enhancing the quality of care, AI is improving operational efficiencies across the healthcare sector. For example, automating repetitive administrative tasks empowers healthcare workers to focus more on patient care, thereby increasing their ability to treat more individuals.
Similarly, clinics that use AI-enabled tools for data processing and analysis are witnessing greater accuracy in billing and claims management as well as reimbursements. Ultimately, this streamlined operational approach not only reduces administrative expenses but also optimizes resource allocation, paving the way for a more sustainable healthcare system.
The risk of global pandemics and disease outbreaks has risen due to factors such as increased travel, urbanization and climate change. Equipping countries with the necessary resources and tools to address public health challenges is essential for advancing global health equity.
AI is helping to address this by enhancing disease surveillance and outbreak detection. Through real-time data analysis and predictive modeling, AI algorithms can rapidly identify emerging health-threats, enabling swift and targeted interventions. For instance, during the height of the COVID-19 pandemic, AI tools were instrumental in analyzing data to track the spread of the virus and predict hotspots. Such tools continue to work in this area, with AI regularly evaluating data from sensors monitoring wastewater for evidence of disease.
By extending such analysis to historical data and simulations, AI can help countries develop robust contingency plans, stockpile essential supplies and train healthcare workers. Such proactive measures can help mitigate the impact of epidemics and strengthen health systems, especially in regions with limited resources.
AI holds great potential to transform the future of healthcare and propel us toward global health equity. By harnessing AI, the healthcare sector can break down barriers that have long hindered underserved populations and pave the way for innovative solutions that address their unique needs. In the process, they will be using technology to bridge significant healthcare gaps and make progress against the global need for improved health equity.
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