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Bridging digital health divides


On 23 March 2020, India announced a national lockdown to contain the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic ([ 1 ][1]). Routine health care virtually came to a standstill, with only emergency care being provided. In view of the high infectivity of SARS-CoV-2 and scarcity of quality personal protective equipment, patients and health care workers (HCWs) both looked for alternatives to face-to-face care ([ 2 ][2]). Telemedicine and digital health, oversold and underdelivered for the past two decades, found a new impetus driven by coalescence of often antagonistic viewpoints on issues such as rights of medical practitioners versus digital health companies, regulatory standards to ensure quality and data security, and financial models of public versus private goods. However, whether the current urgency will succeed in bringing about digital health transformation, with quality care and seamless connections across spheres of life, will depend on many factors. The old adage of crisis triggering reform came true in India, with pending matters being rapidly cleared. On 25 March, telemedicine practice guidelines were released, removing uncertainty about the legitimacy of telemedicine since 2018 due to an adverse court decision ([ 3 ][3]). In May, financial approval was granted to the 2019 national digital health blueprint that advocates a separate national digital health authority to develop and administer high-value digital public health goods in the form of a national health stack containing master health data of the nation and necessary tools for authorized access ([ 4 ][4]). These far-reaching decisions are the culmination of a long process, and the rapid timeline of their clearance is testimony to the catalyst effect of the SARS-CoV-2 pandemic on digital health transformation. However, during times of rapid acceleration, it is important to steer precisely and with anticipation to avoid crashes. The organic growth plan contained in the “blueprint” should not become the “wild west,” where concern for data integrity, privacy, and ethics gets lost. India has a well-recognized need and capacity for digital health services such as telemedicine, owing to large underserved rural areas, as well as domestic strength in information technology. There has been continuing investment in general digital infrastructure and governance, and India is one of the fastest-growing digital consumer markets, with more than half a billion internet subscribers using over 8 GB data monthly, at some of the lowest data costs in the world ([ 5 ][5]). Data speed is sufficient for video communication well beyond the megacities and cities, into the rural hinterlands. Yet, telemedicine is one of the least used digital services despite almost two decades of planning and pilot studies. Satellite-based telemedicine was launched in 2000 by Apollo Hospitals (a large corporate) and the Indian Space Research Organization through a public-private partnership ([ 6 ][6]). A national telemedicine task force was established as early as 2005. Unfortunately, scalable and sustainable models never emerged, partly because of technological constraints such as limited internet speed and partly owing to lack of economic incentives. The few notable successes, such as teleradiology, were due to a naturally digital workflow that required little additional infrastructure, coupled with sustainable economic models. Imaging services became possible in regions that had been unable to recruit radiologists, opening new markets and attracting private investment. Teleradiology was also used to optimally distribute cases to experts across the globe, allowing use of time-zone differences and price differentials to create value-added consultation services from India ([ 7 ][7]). In other medical fields, where workflow was not natively digital, it was difficult to even get to digitization of analog data, let alone digitalization, where core health care processes are unified digitally. Digitalization can improve quality, efficiency, and accessibility but requires reimagination of existing systems (see the figure). An important lesson from India's experience is that citizen-centric digital health policies, such as increasing accessibility for underserved areas with low sociodemographic development, require simultaneous investments in physical health infrastructure. A major challenge is that more than 80% of outpatient care is currently delivered by the private sector, with wide variations in quality and cost ([ 8 ][8]). However, the government-provided care sector dominates in terms of inpatient beds and perinatal care. Reimagining digital health in India must thus be a multipronged strategy. Individual practitioners, working on a fee-for-service basis, are the backbone of Indian health care, especially in smaller cities and rural areas. These are typically low-cost, high-volume practices with an average consultation time of less than 2 min, much of which goes into prescriptions and refills ([ 9 ][9]). There is a sense of patient ownership and a justifiable fear that transition to digital health may lead to weakening of relationships with patients, divert referrals, create additional burden on time and infrastructure, and reduce financial returns. In the time of coronavirus disease 2019 (COVID-19), fear has driven consensus regarding the viability and desirability of digital interactions, given that HCWs figured prominently among the global infected and the dead, accounting for a large proportion of young and healthy COVID-19 patients with severe illness ([ 2 ][2]). This has accelerated initial digital adoption by individual practitioners, but there is a danger that these will be stop-gap solutions of insufficient quality and without measures for data privacy and protection. ![Figure][10] Stages in digital health evolution The first stage of digitization has been crossed in many places in India. The next stage of digitalization has started in megacities but is yet to percolate nationally. The final stage of digital transformation is envisioned in a national digital health blueprint advocating a fully connected open Health Stack securely aggregating patient, provider, and payer data. Necessary elements such as the world's largest biometrically enabled and cloud-based national unique identification authority and a linked universal payment interface raise hope for successful convergence. GRAPHIC: MELISSA THOMAS BAUM/ SCIENCE Conversely, digitalization has already happened in corporate health care organizations that employ many physicians, cater to a higher socioeconomic stratum with digital access, and receive payment from insurance companies for documented services. These organizations exercise high control over customized digital platforms that are professionally managed. In contrast to the corporate sector, government health care institutions are largely still analog, but there is a strong digital commitment in the “blueprint.” For example, India aims to create 150,000 wellness centers and provide health insurance to 100 million families under the “Ayushman Bharat” scheme. The blueprint envisions a central digital repository and secure access for every citizen to their medical records. This seems possible because necessary foundations have been laid. The world's largest biometric unique identity platform (Aadhaar), covering 1.3 billion Indians, is fully cross-functional with IndiaStack, a set of cross-domain generic building blocks that allows government, businesses, start-ups, and developers to formulate presence-less, paperless, and cashless service delivery solutions. Building a Health Stack of registries, data, and freely available tools on top of the IndiaStack is on the horizon. To ensure affordability and ease of participation by individual practitioners, a digital public goods philosophy is essential. The current greenfield state, with strong foundations, is an excellent opportunity to leapfrog the complex, restrictive, digital health systems that have evolved elsewhere. In the beginning of the lockdown, India saw an acceleration of messaging-based consultations, largely on the WhatsApp platform. This is easy for patients and individual HCWs, but unsuited to quality, scale, or data security. eSanjeevani is a government-built public telemedicine platform that is expected to nationally support Ayushman Bharat wellness centers in providing free universal health coverage ([ 10 ][11]). COVID-19 has accelerated its uptake and use by multiple states. The central point of accessing COVID-19–related information and care is a multilingual personal risk assessment and contact tracing app called Aarogya Setu (AS), which became the world's fastest-growing mobile app after its launch on 2 April ([ 11 ][12]). AS links out to a web portal, AS Mitr (ASM), that permits users to obtain telemedicine consultations, necessary testing, and home delivery of medicines ([ 12 ][13]). All consultations are free, whether through eSanjeevani or one of many private providers. ASM has become a fast-tracked entry point into digital health for well-known tech companies, as well as new coalitions that have sprung up almost overnight between venture capitalists, hospitals, and online marketplaces. Mobile phone penetration in India is extremely high, but not everyone has a smartphone or internet access suitable for ASM. Under project StepOne, Indian start-ups specialized in cloud telephony, language processing, call handling, and software development came together to cater to people without smartphones or internet, through an automated interactive voice response system that guides callers and enables consultations with volunteer doctors, if needed ([ 13 ][14]). Within a few months, this has become one of the largest such efforts, handling ∼40,000 calls daily and spanning 10 states, with volunteers who can handle the 30 major Indian languages. In India, both the challenges and opportunities are immense. With COVID-19 providing immediate traction and potential to develop very large user bases, many of these entrants are almost certain to extend digital offerings to other health care sectors. There has also been a market shift with digital products created for affordable health care access becoming a preferred option with wider markets. For instance, CogniABle, an Indian digital health company, has been providing early screening and affordable behavioral intervention for autism ([ 14 ][15]). The demand of such niche products has moved from local to global, as the world suffers through lockdowns and looks for digital alternatives. Similar experiences abound in every sector from diabetes to women's health, and innovative digital health products have a great opportunity to scale. Ensuring quality data flow across homes, diagnostic centers, clinics, and hospitals is a major challenge requiring low-cost, high-quality connected health devices and wearables, an area that has now been prioritized by the Council of Scientific and Industrial Research, India. Artificial intelligence (AI) tools are being developed to process the increasing digital data inflows, from chest x-rays to videos. Although the immediate impact of such tools on COVID-19 is unclear, rapid growth of the AI-health interface is likely. It remains to be seen how conventional health systems, medical providers, and consumers react to these changes after COVID-19. Although health corporates and an emerging class of digital natives in Indian megacities may see immediate benefits, the case is not so clear elsewhere. Increased affordability and access to citizens everywhere is the real touchstone of success. An obvious area of concern, given the composition of the new coalitions, is the ownership and use of data. Use of data to establish dominance in a fledgling market or for exploitative practices is not unheard of in other sectors. Health being a human right, regulatory policies for use, storage, access, and transmission of health data must firmly avoid both paralysis and colonialism. Creation of authorized health-data exchanges, with transparent transactions, is one of the possible solutions ([ 15 ][16]). The purpose of digital health care remains better care of people's health; a vibrant data or digital economy is only a desirable side product. The future of digital health in India looks bright, with COVID-19 providing urgency to a sector that has seen much deliberation with limited action. It is important to now focus on building trust and reducing friction, between the existing health system and digital entrants. Patient primacy, ethical data use, respect for conventional health care, and sustainable growth models must be integral parts of the Indian digital health movement. For the foreseeable future, babies will still be delivered by humans, and public health infrastructure, strong social contracts, and thoughtful leadership will still matter most. 1. [↵][17]1. P. Pulla , BMJ 368, m1251 (2020). [OpenUrl][18][FREE Full Text][19] 2. [↵][20]1. P. Lapolla et al ., Infect. Control Hosp. Epidemiol. 10.1017/ice.2020.241 (2020). 3. [↵][21]Ministry of Health and Family Welfare (India), Telemedicine Practice Guidelines (2020); [][22]. 4. [↵][23]Ministry of Health and Family Welfare (India), National Digital Health Blueprint (2019); [\_Digital\_Health\_Blueprint\_Report\_comments\_invited.pdf][24]. 5. [↵][25]McKinsey Global Institute, Digital India: Technology to transform a connected nation (2019); [][26]. 6. [↵][27]1. V. G. Chellaiyan, 2. A. Y. Nirupama, 3. N. Taneja , J. Family Med. Prim. Care 8, 1872 (2019). [OpenUrl][28] 7. [↵][29]1. A. Agrawal, 2. D. B. Koundinya, 3. J. S. Raju, 4. A. Agrawal, 5. A. Kalyanpur , Emerg. Radiol. 24, 157 (2017). [OpenUrl][30] 8. [↵][31]1. T. Jayakrishnan, 2. B. Thomas, 3. B. Rao, 4. B. George , Int. J. Med. Public Health 3, 225 (2013). [OpenUrl][32] 9. [↵][33]1. G. Irving et al ., BMJ Open 7, e017902 (2017). [OpenUrl][34][Abstract/FREE Full Text][35] 10. [↵][36]eSanjeevani; . 11. [↵][37]1. A. Jhunjhunwala , Indian Natl. Acad. Eng. 5, 157 (2020). [OpenUrl][38] 12. [↵][39]Aarogya Setu Mitr; [][40]. 13. [↵][41]Project StepOne; [][42]. 14. [↵][43]CogniAble; . 15. [↵][44]1. S. Balsari et al ., J. Med. Internet Res. 20, e10725 (2018). Acknowledgments: Thanks to Lipsa for help with artwork and The Lancet–Financial Times commission for Governing Health Futures 2030 for discussions. A.A. is supported by the Council of Scientific and Industrial Research (India), Wellcome Trust-DBT India Alliance, and Fondation Botnar. 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Akraino's AI Edge-School/Education Video Security Monitoring Blueprint - LF Edge


In order to support end-to-end edge solutions from the Akraino community, Akraino uses blueprint concepts to address specific Edge use cases. A Blueprint is a declarative configuration of the entire stack i.e., edge platform that can support edge workloads and edge APIs. In order to address specific use cases, a reference architecture is developed by the community. The School/Education Video Security Monitoring Blueprint belongs to the AI Edge Blueprint family. It focuses on establishing an open source MEC platform that combined with AI capacities at the Edge.

AI as a blueprint for fintech startups – TechCrunch


While most startup founders would prefer not to pore over laws, regulations and interpretive materials to design a perfect product, it's an essential exercise for those developing financial services solutions. For fintechs and the other finserv-related startups (e.g., regtech, suptech, etc.) understanding the regulatory obligations of customers and prospects will be core to your mission. In some cases, the process of interpretation and analysis might be a heavy lift involving expert outside counsel, lobbying efforts, and specialized consulting services. A complicating factor for any fintech looking to solidify its understanding of regulatory paradigms is the gray area where regulators have issued cursory guidance, or no guidance at all. One gray-ish realm where financial services regulators have shown interest, but are largely treading lightly, has been offering guidance about the use of artificial intelligence ("AI").

Python: Procedural or Object-Oriented Programming?


Who does not know Python? Mostly used in Data Science and Machine Learning. Let us discuss more about it! When you first learn a program, you are seemingly using a technique called procedural programming. A procedural program is typically a list of instructions that execute one after the other starting from the top of the line. On the other hand, object-oriented programs are built around well objects.

What is the difference between artificial neural networks and biological brains?


What is the master algorithm that allows humans to be so efficient at learning things? That is a question that has perplexed artificial intelligence scientists and researchers who, for the past decades, have tried to replicate the thinking and problem-solving capabilities of the human brain. The dream of creating thinking machines has spurred many innovations in the field of AI, and has most recently contributed to the rise of deep learning, AI algorithms that roughly mimic the learning functions of the brain. But as some scientists argue, brute-force learning is not what gives humans and animals the ability to interact the world shortly after birth. The key is the structure and innate capabilities of the organic brain, an argument that is mostly dismissed in today's AI community, which is dominated by artificial neural networks. In a paper published in the peer-reviewed journal Nature, Anthony Zador, Professor of Neuroscience Cold Spring Harbor Laboratory, argues that it is a highly structured brain that allows animals to become very efficient learners.

Does AI Scream at Electric Creeps?


When most people think of machine learning in relation to themselves, something like the auto-correct peppered throughout their texts might come to mind. But these technologies are integrated into so many industries that touch us daily. In my previous article linked below, I talk about the broad strokes of machine learning by looking into the technologies of self driving cars, healthcare, and briefly touched on the YouTube algorithm. In this article, I'll be diving farther into that last concept by approaching three different violations of terms and services on a social media platform and the role that machine learning has in mitigating any hardships caused by these violations. To fully understand the decision making behavior, we must go over the basics of these algorithms.

What is the difference between artificial neural networks and biological brains


This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. What is the master algorithm that allows humans to be so efficient at learning things? That is a question that has perplexed artificial intelligence scientists and researchers who, for the past decades, have tried to replicate the thinking and problem-solving capabilities of the human brain. The dream of creating thinking machines has spurred many innovations in the field of AI, and has most recently contributed to the rise of deep learning, AI algorithms that roughly mimic the learning functions of the brain. But as some scientists argue, brute-force learning is not what gives humans and animals the ability to interact the world shortly after birth.

Artificial Intelligence Blueprint : Machine Learning


Don't Hit the Infrastructure Wall. Find the Right Solution for AI with IBM Today. Instructor: Daniel Mandachi Enroll Now - Artificial Intelligence Blueprint: Machine Learning Machine learning is one of the most important areas of Artificial Intelligence. It provides developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. It can be applied across many industries to increase profits, reduce costs, and improve customer experiences.

Can AI Find a Cure for COVID-19?


The novel coronavirus has been circulating among humans for barely three months, but several bio-tech firms have already created drugs that target the COVID-19 disease. One of the secret weapons for the fast response is artificial intelligence. The Chinese government initially was criticized for downplaying the severity of the coronavirus outbreak that originated in Wuhan last December. However, researchers around the world applauded the quick work of Chinese scientists in decoding the genetic sequence of the virus, dubbed SARS-CoV-2, and posting the results in a public database on January 10. Researchers quickly went to work.

Cao Fei: Blueprints review – would you trade love for progress?

The Guardian

Love is evidence that we are recognised as individuals, as significant. Love is what we are asked to set aside in the name of progress under a revolutionary regime. Love, too, is imperilled by automation, given how it minimises human contact. Two great loves sit at the heart of Cao Fei's feature-length film Nova from last year: a romance between two computer scientists – one Russian, one Chinese – and the relationship between the latter and his son. Both loves fall foul of Sino-Soviet progress.