Cancer has been declared by the World Health Organization as the leading cause of global morbidity. Low- to middle-income countries are bearing the brunt of that burden, with more than 70 per cent of cancer deaths happen in developing countries. The most glaring area of inequality is seen in the field of childhood cancer, where the survival rates over 80 per cent in high-income countries while as low as 20 per cent in low-income countries. The disparity in care is one of the growing concerns in the field of cancer care. This disparity arises from four main factors, viz. geographic factors, access to healthcare, socioeconomic status, and biological/genetic factors.
47% of literature studies in the research have shown that cancer (in this particular study it was breast cancer) diagnosis was delayed when the patients were residing in geographically remote or rural areas. 28% of these studies observed that higher proportions of women were diagnosed with cancer in an urban setting than in a rural setting. The diagnosis was delayed by six months in patients located in rural locations and for women living far away from specialised care centres.
In cancer, survival is very closely linked to the stage at diagnosis. In other words, early diagnosis leads to longer survival and improve the quality of life. Studies have observed continuously that as much as two-thirds of the delay in diagnosis was due to patient factors such as symptoms not being considered serious enough(56%), traditional therapy not being good enough(12%), and fear of cancer diagnosis and or treatment(12%).
How can we bridge the divide between the rural and the urban population as we witness an ever-increasing shortage in trained healthcare professionals in the face of a growing demand for healthcare services, especially cancer services? Clinical artificial intelligence holds the key to this problem and has the potential to change the cancer diagnosis process drastically.
We carried out a survey in Kenya to unveil more on the ground realities of access to healthcare. We were startled to find that almost two-thirds of the population in studied area in Kenya have access to mobile phone; however, only less than ten per cent have got access to quality healthcare.
Cost is a major consideration, and therefore we have to keep the cost factor and affordability in mind while carrying out any field trial. Out of all the options that are available to us to bridge this gap, the most readily available and easily implementable is mobile health. Given the widespread access and reducing price of a mobile phone, mobile healthcare can be a powerful, modern and yet affordable way to bridge the disparity in the healthcare standards.
We created a mobile application and carried out a field trial amongst the inhabitants of Kenya. This trial was aimed to find out community’s adoption of new technology and to calculate its potential impact in the underserved part of the world.
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