India’s healthcare challenges are well known. The problems plaguing the system drew even more attention as India faced the brunt of the Covid-19 pandemic. He exposed gaps in our healthcare infrastructure, sparking a conversation about how India can be better equipped to deal with such eventualities in the future.
This article also intends to start a similar conversation. It explains how a specific branch of technology can be implemented to benefit the most underserved people in this country – the rural population.
For a country of our size and diversity, developing a system capable of providing adequate health care to everyone is no easy task. Different parts of the country have different health needs; sickle cell disease, for example, is a major health problem in central India (), while non-communicable diseases are a concern in Kerala.
States like Andhra Pradesh and Uttar Pradesh have to deal with the highest number of deaths from heatstroke (). As a result, the type of health care citizens need and the priority of the state in health care also differ.
Some states are well served while others are not. In NITI Aayog’s SDG India Index 2020-21 report, against a target of 45 health workers per 10,000 population, several states have very low figures. Jharkhand has four and Telangana has 10. On the other hand, states like Karnataka, Andhra Pradesh and Kerala are ahead with 70, 95 and 115 health workers per 10,000 population respectively.
On the supply side, India lacks adequate healthcare providers. According to the Fifteenth Finance Commission, we have one allopathic doctor for every 1,511 people and one nurse for every 670 people, while the World Health Organization (WHO) recommends a ratio of 1:1,000 and 1:300 respectively.
Training physicians is resource-intensive, expensive, and requires hands-on training. Although the government is stepping up efforts to reach the market, it is not certain when we will be able to meet our growing demands.
The natural question that arises is: what should we do in this situation? Improving mortality rates, health care coverage and the workforce takes time. Perhaps there is something that can be deployed at relative speed.
Of course, buzzwords like artificial intelligence (AI), machine learning, and big data are thrown around often. Most people have heard that AI is the next big thing, but how can it help us find practical solutions to the problems we face today?
First, the basics.
Artificial intelligence is, as we can intuitively understand from the term, the replication of human intelligence. In a sense, we use computers and machines to perform tasks that were once only possible with the help of a human being.
The advantage of artificial intelligence is that while we humans have limited memory and capacity for abstraction, machines have near-permanent and infinite memory. This makes them adept at recognizing complex patterns and making decisions when a large number of variables are involved. Therefore, it is particularly suited to the healthcare paradigm.
How? We will now talk about a few use cases that illustrate how AI can transform a villager’s experience with healthcare.
The heart of the health problem lies in the lack of quality and quantity of health care provided that works in rural communities. A recent study () showed that according to the National Sample Survey Office (NSSO), rural India accounts for about 66% of the total population, but only 33% of all health workers practice in rural facilities. This is not only because of the general lack of health personnel in the country, but also because most health care providers move to areas where their professional and financial incentives are higher – big cities and the subways.
Healthcare professionals who are left to serve in rural areas often lack networks and exposure to the latest treatment options, which negatively impacts their careers and the service they can provide. How to attract health professionals to work in rural areas is a multidimensional question, which is another conversation in itself. However, AI can improve the quality of healthcare delivered to patients.
A system that could be helpful in this scenario is called CDSS – Clinical Decision Support System. These are AI algorithms that have been trained using datasets of confirmed cases and work based on pattern recognition. For example, an algorithm that was trained to review x-rays of human lungs can help inexperienced doctors notice peculiarities in a scan they might have otherwise missed, guiding them to the correct diagnosis. This reduces the need for constant mentorship while increasing the quality of healthcare provided.
However, either due to a lack of accessibility or due to financial problems, people in rural areas avoid going to the doctor until the problem becomes unmanageable. This is where the ASHAs, or Accredited Social Health Activists, come in.
ASHAs are not healthcare professionals, but they are the first link between a family and the nation’s healthcare system. They have limited knowledge and keeping every worker up to date with the latest emerging health issues is another concern. An application based on an AI system that would take into account statistics of the local incidence of specific health problems can help ASHAs provide preliminary care to the patient while more tailored care is underway.