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Improving Healthcare Access for the Poorest Through AI

Ai improving Healthcare for the poorest

Access to basic quality healthcare remains out of reach for millions of the world's poorest people. But could emerging technologies like artificial intelligence (AI) hold some solutions? In this post I will explore the barriers to healthcare facing disadvantaged populations and look at some of the ways AI could help leapfrog obstacles and expand access.

The Challenges

In both rural regions and urban slums across Africa, Asia and Latin America, impoverished communities face numerous obstacles to getting even rudimentary healthcare. Financial barriers, long distances to clinics, lack of medical infrastructure and shortages of trained doctors all contribute to keeping basic primary care inaccessible for many of the poorest.

The costs of seeing a doctor or getting tests done even for common illnesses can be prohibitively expensive for people living in extreme poverty. In many developing countries, over 90% of the population subsists on less than $1.90 a day. Out-of-pocket medical expenses force many families to make an impossible choice between healthcare and other necessities like food, rent, or education.

Geography also severely restricts access to care. Nearly three quarters of the world's extreme poor live in remote rural areas, some in villages so remote they are effectively cut off from healthcare systems entirely. The nearest clinic with a doctor may be days away for some communities, an impossible journey without transportation. Dangerous terrain, poor roads, and lack of vehicles further hamper access. Monsoon rains can make dirt roads impassable for months on end.

Existing healthcare facilities in poorer regions are also frequently underequipped and understaffed. Basic diagnostic tools like x-rays, ultrasound machines, and lab equipment to run blood tests may not be available, making accurate diagnoses difficult. Many rural clinics have no doctors at all, just nurses or community health workers with limited training. This significantly restricts their ability to definitively diagnose and treat all but the most straightforward illnesses and injuries.

Preventative care is also scarce. Health education on topics like nutrition, vaccines, and maternal health is limited. Opportunities for routine check-ups to catch diseases early are rare. Combined with lack of access to diagnosis and treatment, this means conditions that could be prevented or managed early often spiral into critical emergencies.

These barriers converge to make basic healthcare functionally out of reach for a significant portion of the planet's poorest people. Of the 5.6 million children under five who died in 2016, the WHO estimates that roughly 3 million could have been saved with access to simple, affordable primary care interventions. Millions more adults die prematurely each year from preventable or treatable conditions.

The Potential of AI

Recent major advances in artificial intelligence and machine learning, however, present new opportunities to help overcome some of these healthcare access obstacles. Applied thoughtfully, AI may help extend the reach of medicine to places where poverty has blocked its path. Though not a panacea, AI could provide tools to help leapfrog over some of the roadblocks keeping basic care from the world's most vulnerable groups.

Expanding Access to Doctors Through Telemedicine

One of the biggest limitations in rural regions is proximity to physicians. AI-powered telemedicine apps and chatbots can help mitigate this by enabling remote diagnosis and consultations. With minimal training, rural nurses and health workers could collect basic patient information and send it via mobile phones to doctors in cities hundreds of miles away. AI algorithms would analyse the data, providing initial diagnostic suggestions to focus the doctor's review. The urban doctor could then conduct an audio or video consult and recommend treatment, saving time and expense of an in-person visit.

Companies like Babylon Health are already demonstrating the potential of AI-assisted telemedicine in places like Rwanda. Their smartphone app lets rural users consult doctors and receive treatment plans 24/7. Early results are promising, with 60% of initial diagnoses by the AI matching doctors' assessments. Refining and scaling tools like this could greatly alleviate doctor shortages in remote areas.

Assisting Lesser-Trained Workers with Diagnostics

Since many rural clinics lack doctors entirely, empowering the nurses and community health workers on site to make accurate preliminary diagnoses could significantly expand access. AI diagnostic tools could assist lesser-trained workers in the field. Machine learning image recognition models like those being developed by Google Health can analyse photos of skin lesions, eye issues, and other medical images to provide diagnostic suggestions. Though not definitive, these could direct workers' attention to potential serious conditions needing escalation.

AI neural networks can also find patterns and extract insights from other types of patient data like symptoms, vitals, and demographics. Though no substitute for doctors, these models could serve as valuable diagnostic decision support tools on the front lines where doctors are unavailable. Startups like are rolling out AI-assisted diagnostics tailored specifically for clinics in low-resource regions.

Optimizing Allocation of Limited Medical Resources

In countries with widespread poverty, medical resources like doctors, hospital beds, ICU capacity, and medicines are frequently scarce, with demand far outstripping supply. AI predictive models and optimization algorithms could help distribute these limited resources more efficiently.

For example, AI could identify which rural villages have the highest incidence of treatable conditions and assign doctors accordingly for periodic visits. AI scheduling algorithms used by companies like Optimize could determine optimal allocation of available doctor hours across different facilities. At the national level, AI modelling may also assist health agencies in strategically prioritizing medical infrastructure investments to maximize impact.

Providing Health Education via Chatbots

Limited health literacy is another major obstacle to access. Many impoverished communities lack awareness of how to prevent and manage diseases. AI chatbots could provide a low-cost tool to effectively share preventative health knowledge in areas with low literacy.

Conversational agents like Sensely's Molly can understand natural language, remember context, and have ongoing dialogs to educate users about health topics at their level of understanding. They can help people learn to identify concerning symptoms early, self-treat minor ailments, and know when to seek in-person care for more serious conditions. Voice-based chatbots accessible on basic mobile phones could prove more effective than traditional health education efforts at boosting understanding.

Enabling Real-Time Epidemic Tracking and Response

In poor countries, limited data hampers the ability of health agencies to effectively track disease outbreaks and deploy resources quickly enough to contain them. But AI models fuelled by streams of on-the-ground data can enable more real-time epidemic monitoring and faster response.

Researchers from multiple institutions have demonstrated how AI can analyse disparate data sources like news reports, social media posts, diagnostic records, and internet search trends to identify disease outbreaks early with high accuracy. Other AI models can predict the trajectory of outbreaks, letting agencies strategically pre-position personnel and supplies. Startups like BlueDot and Metabiota already provide these AI epidemic tracking capabilities to governments worldwide. Though not a complete solution, better real-time visibility could help health agencies in poorer countries save lives by responding faster to contain outbreaks when every day counts.

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Improving Efficiency of Public Health Interventions

Public health interventions like vaccine drives, mosquito net distribution, and water purification projects have limited budgets. AI predictive analytics could enable more strategic and precise targeting of these interventions to higher risk populations, improving outcomes. By mining data on demographics, living conditions, health records, and prior intervention results, AI can zero in on the highest priority locations and recipients most likely to benefit.

IBM researchers have demonstrated this ability to optimize distribution of limited vaccines and nets during pilot projects in Kenya, Ghana, and Thailand. Though scaling the data inputs remains challenging, capabilities like this could help cash-strapped public health agencies in poorer nations maximize impact. AI is no magic bullet, but refining data-driven targeting could stretch limited budgets further.

Identifying Healthcare Access Gaps

Finally, advanced AI data mining techniques have potential to help governments and aid agencies identify the communities with the poorest healthcare access. By analysing disparate datasets on health infrastructure, transportation routes, disease burdens, living conditions, and demographics, AI algorithms can surface patterns that reveal areas facing the highest access barriers. These insights can inform where new facilities, personnel, equipment, and transportation resources need to be deployed first to expand coverage.

For example, a Harvard team demonstrated an AI technique that analyses satellite imagery of regions with limited surveys to estimate poverty levels and likely healthcare access gaps. Though still early stage, capabilities like this could provide valuable intelligence to guide investments towards areas in greatest need.

The Path Forward

AI is clearly no panacea - there are no magic bullets that will fix healthcare access deficits overnight in the developing world. Serious challenges remain around data availability, interpretation, infrastructure limitations, and sustainable implementation. Any AI solution will need to be thoughtfully designed to address the unique complexities of delivering care in low-resource environments.

But by creatively applying these emerging technologies, we may begin to chip away at the healthcare inequalities and access gaps for vulnerable populations worldwide. AI has potential to remove some of the barriers standing between the poorest people and the care they need. The moral and economic cases for expanding access using every ethical tool at our disposal are clear. AI can be part of the solution, complementing other efforts to help save and improve lives among those whom poverty has placed furthest from healthcare.

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