Personalized medical assistant table of Contents
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Big data+AI – PERSONALIZED MEDICAL ASSISTANT Table of ContentsExecutive Summary The underdeveloped world, commonly referred to as developing countries or less developed, is defined as “a nation with a lower living standard, underdeveloped industrial base, and low Human Development Index (HDI) relative to other countries.”1 This includes countries like India, Africa, and Eastern Europe. The rapid increase in smart phone and smart device usage now provides a viable medium to deliver services to the population in these countries. Research firm IDC has stated that: “Smartphone sales in India are expected to reach 80.57 million units by the end of this year. Also, the sales would continue to grow at a CAGR of about 40 per cent over the next five years.”2. Plus, people in these countries have better access to cell phones than access to clean water and electricity3 . In this paper, we will focus on underdeveloped countries that will soon have readily available smart devices and internet connections. Companies such as Google are rapidly expanding their reach in these areas, and we think that Android will be the platform that most of the services will be provided on. Medical care or the lack thereof, has a devastating effect on the people in these countries. Not only does it hinder progress, but also it takes lives.4 The goal of this case-study is to investigate and propose a comprehensive solution to bring medical care of developed countries to the underdeveloped world, combining technologies such as Big Data analytics, artificial intelligence, cloud platform, crowd-sourcing, and data exchange services. We will start with describing our business objectives, target customers, our services and obstacles that we might encounter along the way. Then, we will look at the current Big Data landscape in healthcare sector with the hope that we can identify current trends, and predict where we might be heading towards. We can identify current players in healthcare sector under four main segments:
Each of these companies within each segment has a different approach when it comes to Big Data, healthcare analytics, and personal care. So, there is not a single winning approach. We will explain and analyze the services provided by some of these players in the health-care sector as well as looking at two newly announced fitness tracking platforms, Google Fit and Apple Health. As for our strategy, we will align ourselves with the Google’s mobile initiatives such as Micromax smart phone and Google Loon5 project, which aim to bring connectivity to underdeveloped regions of the world. And, based on the lessons learned from the case studies, we will propose our own solution, Big Data+AI, for bringing a unified healthcare platform to serve the underdeveloped world. Essentially, our solution would be “Uber for medical care”. The Business “People in poor countries tend to have less access to health services than those in better-off countries, and within countries, the poor have less access to health services” [http://onlinelibrary.wiley.com/doi/10.1196/annals.1425.011/pdf]. Deprivation leads to poor health and poor health leads to poor earning potential. Earning potential is directly related to health conditions and social development as a whole. It is an endless-loop. In addition, this situation leads to poor education hindering the underdeveloped countries’ ability to improve their overall community [http://www.nber.org/reporter/spring03/health.html].
We see the future of Big Data in centralized data, especially in healthcare sector. What we envision is a platform to centralize health data for the purpose of storage and analytics. We call this platform ‘The Health Exchange’. Through Health Exchange, people, companies, institutions will be able to get useful medical insights. In the case of an individual, insights given will be personalized. In the case of a company, or institution, given insights will be out of anonymized data and they will be more general medical insights such as response of a particular population to a particular treatment, disease map within particular region, outbreak heat map within a certain country etc.
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