It is not surprising that India has a booming healthcare sector, considering that a population of 1.35 billion in 2018 is likely to exhibit myriad morbidities (World Bank, 2018). With a three-fold increase in the healthcare market and governmental projections pushing a 372 billion USD mark in 2022, the sector is ready for significant technological interventions. (IBEF, 2019). On the downside however, the sector is beleaguered by concerns that range from access barriers and poor doctor-patient ratio to affordability and poor healthcare infrastructure. Artificial intelligence (AI) comes with a promise of not only overcoming a majority of these barriers, but also eliminating predispositions, such as the recency bias, in medical sciences. Riding the investment wave AI—robotics and Internet of Things (IoTs) can revolutionise healthcare.
Healthcare concerns can be broadly classified into those which are predictive in nature—pre-empting a problem and providing a solution to abrogate the issue; and, prescriptive, where a treatment is offered based on an informed decision. When AI is deployed to prepare algorithms that help map patterns by collecting and analysing gathered data—both spatial and temporal, it is found that it can provide astounding results in preparing a response ahead of time and influencing outcomes. From early detection of diseases based on analysis of past data, to decentralised diagnostic testing, AI can singularly alleviate healthcare problems in rural and remote areas. AI algorithms are able, with a certain degree of precision, to screen diseases, which can help triaging high priority cases, enhancing the productivity of healthcare professionals.
In India, companies such as Artelus, a Bangalore based AI enabled healthcare unit, works to provide an image-based early detection facility for diabetic retinopathy . The Deep Learning and AI based setup in Artelus can help identify at a primary screening, lesions or abnormalities present in fundus images and report it to the doctor, making it impactful for areas that lack this facility. These healthcare capacities extend beyond the boundaries of hospitals and specialised clinics, reduce cost and improve health outcomes. There are many such applications for various anatomical disorders world over, such as IBM Watson for oncology and many private hospitals in India, such as the Manipal group of hospitals, make use of such interfaces.
Interestingly, AI can also screen mental disorders in its early stages, like depression. Wysa, a bot developed by Bangalore based start-up Touchkin, is delving into the domain of emotional wellness. Supported by human coaches, the bot helps cure depressive thoughts (wysa.io).The app records and analyses various physiological factors like sleep patterns, blood sugar levels and other behavioural insights and predicts the user’s mental health. In the event that the bot identifies an individual who needs intensive care, it refers the case to professionals, who can then intervene.
Niramai, a Bengaluru based healthcare start-up has developed a non-invasive, radiation free breast cancer detection software that uses a high resolution thermal sensing device and a cloud hosted analytics solution for screening thermal images that accurately leads to the early detection of tumours. Orbuculum, another start-up in Bengaluru analyses genomic data to predict a gamut of diseases.
Telangana in fact, has adopted a cloud-based analytics tool developed by Microsoft, for the state’s Rashtriya Bal Swasthya Karyakram, to reduce avoidable blindness among children by screening them for the ailment. Thus predictive AI usage in healthcare can provide actionable insights based on available data and thus improve healthcare penetration.
Prescriptive analytics on the other hand, make use of machine learning to determine the best solution or outcome among various courses of action. For instance, an AI algorithm in IBM Watson for oncology will use information from relevant literature to assess the information from a patient’s medical record and throw up potential treatment options ranked by level of confidence. The oncologist can then use the results along with the supporting evidence to arrive at the appropriate treatment option. Such AI interfaces aid human decision making for doctors and health administrators to use critical data to support clinical, financial and operational decisions. It can lower the cost of healthcare, improve patient efficiency and mitigate operational risks.
This technology has found its way into hospitals in India as well. Manipal uses IBM Watson for Oncology. Max Healthcare,India has deployed the GE Healthcare’s web-based radiology information system—the Centricity RIS-IC. Integrated with the GE -picture archive and communication system (PACS), the programme addresses a healthcare unit’s evolving radiology workflow to enable seamless access to images like X-Ray, MRI and more for patients across locations. It can therefore be used to create an integrated customer record of patients. Fortis Hospital, Bengaluru has partnered with Phable, a healthcare start-up in India, to provide an App to the patients that allows for constant monitoring by the doctor in the event any new symptoms emerge and can also help patients manage medication, tests, diet, exercise etc.
The medical equipment industry are also using AI and machine learning to develop smart wearables and insertables that gather individual data and detect anomalies. The US drug major Abott has launched an Insertable Cardiac Monitor (ICM) that can alert users about irregular heartbeats (arrhythmias) on their smartphone screen (cardiovascular.abbott). Ten3T, another Indian healthcare start-up has launched a wearable device named ‘Smart Patch’ that inter alia measures the patients’ temperature, pulse and blood pressure and provides real time monitoring facility (ten3thealth.com). AI also delves into solving problems related to the pharma supply chain with tools streamlining the entire process from drug generation to delivery. Pharmarack, a Pune based start-up has developed a tool to automate the sales and operational processes of pharma companies (pharmarack.com). It offers management solutions from the origin of order to its completion, in a seamless platform to process, track, and settle all orders, creating complete visibility of business operations in real time.
The AI and machine learning has begun to partner with the medical insurance sector as well. Embedded into existing insurance frameworks, the platform helps insurers to automate and expedite the process, minimising delays and frauds. ICICI Lombard and HDFC bank with their AI and Natural Language Processing (NLP) based chat bots named MyRA and SPOK, respectively, are using AI to categorise, prioritise and respond to customer emails and mine appropriate information for an improved operational efficiency (myralabs.com;
Dhawan 2018) .
Ethical Concerns in AI Healthcare
In 2008, Google Flu Trends (GFT), began aggregating and analysing big data from a range of countries based on Google search queries. GFT then went on to predict or ‘nowcast’ the onset on flu outbreaks days before they were reported by the global Centres for Disease Control and Prevention (Lazer and Kennedy, 2015). However, GFT failed to accurately predict the 2009 global swine flu pandemic, as its algorithm over relied on the Google search patterns rather than the traditional reporting of the disease. In 2013, the GFT failed again, missing predictions by 140 per cent at the peak of the flu season. The project was thereafter closed. It is true that big data is competent to model disease spread and identify emergencies, way faster than traditional methods, but the method and the data used becomes critical in identifying a trend—which is why the GFT lost out. AI and machine learning therefore, needs to be understood in the perspective of its own set of challenges. Apart from the data accuracy concerns, a faulty algorithm can distort the results. In a scenario where humans begin to depend on AI for its decisions, such errors can lead to critical drawbacks in healthcare.
AI, if handled improperly can result in data leaks, which would lead to privacy violations. In India the consent forms for data sharing are not mandatorily filled by the healthcare units—and patients too are barely aware of its need. In practice, doctor-patient confidentiality is in the realm of ethics. Therefore, there is always a chance that the profile of patients can be exploited by companies and consequent data breach can lead to an erosion of trust among the general public. For instance, in 2016, a Mumbai based diagnostic laboratory Health Solutions had to remove over 35,000 medical records of patients which included HIV reports, when its data leaked (Indian Express, 2019). Such breaches are a constant threat that AI needs to combat in order to maintain patient confidentiality.
Then of course there is the single language predisposition that AI holds, where English predominates, making it difficult for the technology to penetrate rural areas. With a huge internet connectivity of over 566 million people in 2018 (The Economics Time, 2019), AI can make greater headway if vernacular usage is encouraged.
Importantly, an AI enabled system thrives on data—generation of poor quality data coupled with poor digital infrastructure and storage can skew results, rendering programmes ineffective. In India lack of trained professionals for data handling also impedes the penetration of AI in this sector. Also, the focus of AI in healthcare in India is nascent and fairly narrow, with disease specific solutions.
Way Forward
AI has significant scope in developing solutions in bettering the lives of humans, and healthcare is a priority area of research. Despite the many challenges AI and machine learning exhibit at present, ground is being made for larger and more accurate predictive and prescriptive programmes. An ambient policy framework by the Indian government that makes for favourable investments in the AI and machine learning sector can help augment the paucity of quality healthcare professionals in many locations of the nation
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Steve Ovett, the famous British middle-distance athlete, won the 800-metres gold medal at the Moscow Olympics of 1980. Just a few days later, he was about to win a 5,000-metres race at London’s Crystal Palace. Known for his burst of acceleration on the home stretch, he had supreme confidence in his ability to out-sprint rivals. With the final 100 metres remaining,
[wptelegram-join-channel link=”https://t.me/s/upsctree” text=”Join @upsctree on Telegram”]Ovett waved to the crowd and raised a hand in triumph. But he had celebrated a bit too early. At the finishing line, Ireland’s John Treacy edged past Ovett. For those few moments, Ovett had lost his sense of reality and ignored the possibility of a negative event.
This analogy works well for the India story and our policy failures , including during the ongoing covid pandemic. While we have never been as well prepared or had significant successes in terms of growth stability as Ovett did in his illustrious running career, we tend to celebrate too early. Indeed, we have done so many times before.
It is as if we’re convinced that India is destined for greater heights, come what may, and so we never run through the finish line. Do we and our policymakers suffer from a collective optimism bias, which, as the Nobel Prize winner Daniel Kahneman once wrote, “may well be the most significant of the cognitive biases”? The optimism bias arises from mistaken beliefs which form expectations that are better than the reality. It makes us underestimate chances of a negative outcome and ignore warnings repeatedly.
The Indian economy had a dream run for five years from 2003-04 to 2007-08, with an average annual growth rate of around 9%. Many believed that India was on its way to clocking consistent double-digit growth and comparisons with China were rife. It was conveniently overlooked that this output expansion had come mainly came from a few sectors: automobiles, telecom and business services.
Indians were made to believe that we could sprint without high-quality education, healthcare, infrastructure or banking sectors, which form the backbone of any stable economy. The plan was to build them as we went along, but then in the euphoria of short-term success, it got lost.
India’s exports of goods grew from $20 billion in 1990-91 to over $310 billion in 2019-20. Looking at these absolute figures it would seem as if India has arrived on the world stage. However, India’s share of global trade has moved up only marginally. Even now, the country accounts for less than 2% of the world’s goods exports.
More importantly, hidden behind this performance was the role played by one sector that should have never made it to India’s list of exports—refined petroleum. The share of refined petroleum exports in India’s goods exports increased from 1.4% in 1996-97 to over 18% in 2011-12.
An import-intensive sector with low labour intensity, exports of refined petroleum zoomed because of the then policy regime of a retail price ceiling on petroleum products in the domestic market. While we have done well in the export of services, our share is still less than 4% of world exports.
India seemed to emerge from the 2008 global financial crisis relatively unscathed. But, a temporary demand push had played a role in the revival—the incomes of many households, both rural and urban, had shot up. Fiscal stimulus to the rural economy and implementation of the Sixth Pay Commission scales had led to the salaries of around 20% of organized-sector employees jumping up. We celebrated, but once again, neither did we resolve the crisis brewing elsewhere in India’s banking sector, nor did we improve our capacity for healthcare or quality education.
Employment saw little economy-wide growth in our boom years. Manufacturing jobs, if anything, shrank. But we continued to celebrate. Youth flocked to low-productivity service-sector jobs, such as those in hotels and restaurants, security and other services. The dependence on such jobs on one hand and high-skilled services on the other was bound to make Indian society more unequal.
And then, there is agriculture, an elephant in the room. If and when farm-sector reforms get implemented, celebrations would once again be premature. The vast majority of India’s farmers have small plots of land, and though these farms are at least as productive as larger ones, net absolute incomes from small plots can only be meagre.
A further rise in farm productivity and consequent increase in supply, if not matched by a demand rise, especially with access to export markets, would result in downward pressure on market prices for farm produce and a further decline in the net incomes of small farmers.
We should learn from what John Treacy did right. He didn’t give up, and pushed for the finish line like it was his only chance at winning. Treacy had years of long-distance practice. The same goes for our economy. A long grind is required to build up its base before we can win and celebrate. And Ovett did not blame anyone for his loss. We play the blame game. Everyone else, right from China and the US to ‘greedy corporates’, seems to be responsible for our failures.
We have lowered absolute poverty levels and had technology-based successes like Aadhaar and digital access to public services. But there are no short cuts to good quality and adequate healthcare and education services. We must remain optimistic but stay firmly away from the optimism bias.
In the end, it is not about how we start, but how we finish. The disastrous second wave of covid and our inability to manage it is a ghastly reminder of this fact.