Centre for Internet and Society

Data Infrastructures and Inequities: Why Does Reproductive Health Surveillance in India Need Our Urgent Attention?

Aayush Rathi and Ambika Tandon — 2019-12-30T16:44:32Z

In order to bring out certain conceptual and procedural problems with health monitoring in the Indian context, this article by Aayush Rathi and Ambika Tandon posits health monitoring as surveillance and not merely as a “data problem.” Casting a critical feminist lens, the historicity of surveillance practices unveils the gendered power differentials wedded into taken-for-granted “benign” monitoring processes. The unpacking of the Mother and Child Tracking System and the National Health Stack reveals the neo-liberal aspirations of the Indian state.

The article was first published by EPW Engage, Vol. 54, Issue No. 6, on 9 February 2019.

Framing Reproductive Health as a Surveillance Question

The approach of the postcolonial Indian state to healthcare has been Malthusian, with the prioritisation of family planning and birth control (Hodges 2004). Supported by the notion of socio-economic development arising out of a “modernisation” paradigm, the target-based approach to achieving reduced fertility rates has shaped India’s reproductive and child health (RCH) programme (Simon-Kumar 2006).

This is also the context in which India’s abortion law, the Medical Termination of Pregnancy (MTP) Act, was framed in 1971, placing the decisional privacy of women seeking abortions in the hands of registered medical practitioners. The framing of the MTP act invisibilises females seeking abortions for non-medical reasons within the legal framework. The exclusionary provisions only exacerbated existing gaps in health provisioning, as access to safe and legal abortions had already been curtailed by severe geographic inequalities in funding, infrastructure, and human resources. The state has concomitantly been unable to meet contraceptive needs of married couples or reduce maternal and infant mortality rates in large parts of the country, mediating access along the lines of class, social status, education, and age (Sanneving et al 2013).

While the official narrative around the RCH programme transitioned to focus on universal access to healthcare in the 1990s, the target-based approach continues to shape the reality on the ground. The provision of reproductive healthcare has been deeply unequal and, in some cases, in hospitals. These targets have been known to be met through the practice of forced, and often unsafe, sterilisation, in conditions of absence of adequate provisions or trained professionals, pre-sterilisation counselling, or alternative forms of contraception (Sama and PLD 2018). Further, patients have regularly been provided cash incentives, foreclosing the notion of free consent, especially given that the target population of these camps has been women from marginalised economic classes in rural India.

Placing surveillance studies within a feminist praxis allows us to frame the reproductive health landscape as more than just an ill-conceived, benign monitoring structure. The critical lens becomes useful for highlighting that taken-for-granted structures of monitoring are wedded with power differentials: genetic screening in fertility clinics, identification documents such as birth certificates, and full-body screeners are just some of the manifestations of this (Adrejevic 2015). Emerging conversations around feminist surveillance studies highlight that these data systems are neither benign nor free of gendered implications (Andrejevic 2015). In continual remaking of the social, corporeal body as a data actor in society, such practices render some bodies normative and obfuscate others, based on categorisations put in place by the surveiller.

In fact, the history of surveillance can be traced back to the colonial state where it took the form of systematic sexual and gendered violence enacted upon indigenous populations in order to render them compliant (Rifkin 2011; Morgensen 2011). Surveillance, then, manifests as a “scientific” rationalisation of complex social hieroglyphs (such as reproductive health) into formats enabling administrative interventions by the modern state. Lyon (2001) has also emphasised how the body emerged as the site of surveillance in order for the disciplining of the “irrational, sensual body”—essential to the functioning of the modern nation-state—to effectively happen.

Questioning the Information and Communications Technology for Development (ICT4D) and Big Data for Development (BD4D) Rhetoric

Information and Communications Technology (ICT) and data-driven approaches to the development of a robust health information system, and by extension, welfare, have been offered as solutions to these inequities and exclusions in access to maternal and reproductive healthcare in the country.

The move towards data-driven development in the country commenced with the introduction of the Health Management Information System in Andhra Pradesh in 2008, and the Mother and Child Tracking System (MCTS) nationally in 2011. These are reproductive health information systems (HIS) that collect granular data about each pregnancy from the antenatal to the post-natal period, at the level of each sub-centre as well as primary and community health centre. The introduction of HIS comprised cross-sectoral digitisation measures that were a part of the larger national push towards e-governance; along with health, thirty other distinct areas of governance, from land records to banking to employment, were identified for this move towards the digitalised provisioning of services (MeitY 2015).

The HIS have been seen as playing a critical role in the ecosystem of health service provision globally. HIS-based interventions in reproductive health programming have been envisioned as a means of: (i) improving access to services in the context of a healthcare system ridden with inequalities; (ii) improving the quality of services provided, and (iii) producing better quality data to facilitate the objectives of India’s RCH programme, including family planning and population control. Accordingly, starting 2018, the MCTS is being replaced by the RCH portal in a phased manner. The RCH portal, in areas where the ANMOL (ANM Online) application has been introduced, captures data real-time through tablets provided to health workers (MoHFW 2015).

A proposal to mandatorily link the Aadhaar with data on pregnancies and abortions through the MCTS/RCH has been made by the union minister for Women and Child Development as a deterrent to gender-biased sex selection (Tembhekar 2016). The proposal stems from the prohibition of gender-biased sex selection provided under the Pre-Conception and Pre-Natal Diagnostics Techniques (PCPNDT) Act, 1994. The approach taken so far under the PCPNDT Act, 2014 has been to regulate the use of technologies involved in sex determination. However, the steady decline in the national sex ratio since the passage of the PCPNDT Act provides a clear indication that the regulation of such technology has been largely ineffective. A national policy linking Aadhaar with abortions would be aimed at discouraging gender-biased sex selection through state surveillance, in direct violation of a female’s right to decisional privacy with regards to their own body.

Linking Aadhaar would also be used as a mechanism to enable direct benefit transfer (DBT) to the beneficiaries of the national maternal benefits scheme. Linking reproductive health services to the Aadhaar ecosystem has been critiqued because it is exclusionary towards women with legitimate claims towards abortions and other reproductive services and benefits, and it heightens the risk of data breaches in a cultural fabric that already stigmatises abortions. The bodies on which this stigma is disproportionately placed, unmarried or disabled females, for instance, experience the harms of visibility through centralised surveillance mechanisms more acutely than others by being penalised for their deviance from cultural expectations. This is in accordance with the theory of "data extremes,” wherein marginalised communities are seen as living on the extremes of data capture, leading to a data regime that either refuses to recognise them as legitimate entities or subjects them to overpolicing in order to discipline deviance (Arora 2016). In both developed and developing contexts, the broader purpose of identity management has largely been to demarcate legitimate and illegitimate actors within a population, either within the framework of security or welfare.

Potential Harms of the Data Model of Reproductive Health Provisioning

Informational privacy and decisional privacy are critically shaped by data flows and security within the MCTS/RCH. No standards for data sharing and storage, or anonymisation and encryption of data have been implemented despite role-based authentication (NHSRC and Taurus Glocal 2011). The risks of this architectural design are further amplified in the context of the RCH/ANMOL where data is captured real-time. In the absence of adequate safeguards against data leaks, real-time data capture risks the publicising of reproductive health choices in an already stigmatised environment. This opens up avenues for further dilution of autonomy in making future reproductive health choices.

Several core principles of informational privacy, such as limitations regarding data collection and usage, or informed consent, also need to be reworked within this context.^[1] For instance, the centrality of the requirement of “free, informed consent” by an individual would need to be replaced by other models, especially in the context of reproductive health of rape survivors who are vulnerable and therefore unable to exercise full agency. The ability to make a free and informed choice, already dismantled in the context of contemporary data regimes, gets further precluded in such contexts. The constraints on privacy in decisions regarding the body are then replicated in the domain of reproductive data collection.

What is uniform across these digitisation initiatives is their treatment of maternal and reproductive health as solely a medical event, framed as a data scarcity problem. In doing so, they tend to amplify the understanding of reproductive health through measurable indicators that ignore social determinants of health. For instance, several studies conducted in the rural Indian context have shown that the degree of women’s autonomy influences the degree of usage of pregnancy care, and that the uptake of pregnancy care was associated with village-level indicators such as economic development, provisioning of basic infrastructure and social cohesion. These contextual factors get overridden in pervasive surveillance systems that treat reproductive healthcare as comprising only of measurable indicators and behaviours, that are dependent on individual behaviour of practitioners and women themselves, rather than structural gaps within the system.

While traditionally associated with state governance, the contemporary surveillance regime is experienced as distinct from its earlier forms due to its reliance on a nexus between surveillance by the state and private institutions and actors, with both legal frameworks and material apparatuses for data collection and sharing (Shepherd 2017). As with historical forms of surveillance, the harms of contemporary data regimes accrue disproportionately among already marginalised and dissenting communities and individuals. Data-driven surveillance has been critiqued for its excesses in multiple contexts globally, including in the domains of predictive policing, health management, and targeted advertising (Mason 2015). In the attempts to achieve these objectives, surveillance systems have been criticised for their reliance on replicating past patterns, reifying proximity to a hetero-patriarchal norm (Haggerty and Ericson 2000). Under data-driven surveillance systems, this proximity informs the preexisting boxes of identity for which algorithmic representations of the individual are formed. The boxes are defined contingent on the distinct objectives of the particular surveillance project, collating disparate pieces of data flows and resulting in the recasting of the singular offline self into various 'data doubles' (Haggerty and Ericson 2000). Refractive, rather than reflective, the data doubles have implications for the physical, embodied life of individual with an increasing number of service provisioning relying on the data doubles (Lyon 2001). Consider, for instance, apps on menstruation, fertility, and health, and wearables such as fitness trackers and pacers, that support corporate agendas around what a woman’s healthy body should look, be or behave like (Lupton 2014). Once viewed through the lens of power relations, the fetishised, apolitical notion of the data “revolution” gives way to what we may better understand as “dataveillance.”

Towards a Networked State and a Neo-liberal Citizen

Following in this tradition of ICT being treated as the solution to problems plaguing India’s public health information system, a larger, all-pervasive healthcare ecosystem is now being proposed by the Indian state (NITI Aayog 2018). Termed the National Health Stack, it seeks to create a centralised electronic repository of health records of Indian citizens with the aim of capturing every instance of healthcare service usage. Among other functions, it also envisions a platform for the provisioning of health and wellness-based services that may be dispensed by public or private actors in an attempt to achieve universal health coverage. By allowing private parties to utilise the data collected through pullable open application program interfaces (APIs), it also fits within the larger framework of the National Health Policy 2017 that envisions the private sector playing a significant role in the provision of healthcare in India. It also then fits within the state–private sector nexus that characterises dataveillance. This, in turn, follows broader trends towards market-driven solutions and private financing of health sector reform measures that have already had profound consequences on the political economy of healthcare worldwide (Joe et al 2018).

These initiatives are, in many ways, emblematic of the growing adoption of network governance reform by the Indian state (Newman 2001). This is a stark shift from its traditional posturing as the hegemonic sovereign nation state. This shift entails the delayering from large, hierarchical and unitary government systems to horizontally arranged, more flexible, relatively dispersed systems.^[2] The former govern through the power of rules and law, while the latter take the shape of self-regulating networks such as public–private contractual arrangements (Snellen 2005). ICTs have been posited as an effective tool in enabling the transition to network governance by enhancing local governance and interactive policymaking enabling the co-production of knowledge (Ferlie et al 2011). The development of these capabilities is also critical to addressing “wicked problems” such as healthcare (Rittel and Webber 1973).^[3] The application of the techno-deterministic, data-driven model to reproductive healthcare provision, then, resembles a fetishised approach to technological change. The NHSRC describes this as the collection of data without an objective, leading to a disproportional burden on data collection over use (NHSRC and Taurus Glocal 2011).

The blurring of the functions of state and private actors is reflective of the neo-liberal ethic, which produces new practices of governmentality. Within the neo-liberal framework of reproductive healthcare, the citizen is constructed as an individual actor, with agency over and responsibility for their own health and well-being (Maturo et al 2016).

“Quantified Self” of the Neo-liberal Citizen

Nowhere can the manifestation of this neo-liberal citizen can be seen as clearly as in the “quantified self” movement. The quantified self movement refers to the emergence of a whole range of apps that enable the user to track bodily functions and record data to achieve wellness and health goals, including menstruation, fertility, pregnancies, and health indicators in the mother and baby. Lupton (2015) labels this as the emergence of the “digitised reproductive citizen,” who is expected to be attentive to her fertility and sexual behaviour to achieve better reproductive health goals. The practice of collecting data around reproductive health is not new to the individual or the state, as has been demonstrated by the discussion above. What is new in this regime of datafication under the self-tracking movement is the monetisation of reproductive health data by private actors, the labour for which is performed by the user. Focusing on embodiment draws attention to different kinds of exploitation engendered by reproductive health apps. Not only is data about the body collected and sold, the unpaid labour for collection is extracted from the user. The reproductive body can then be understood as a cyborg, or a woman-machine hybrid, systematically digitising its bodily functions for profit-making within the capitalist (re)production machine (Fotoloulou 2016). Accordingly, all major reproductive health tracking apps have a business model that relies on selling information about users for direct marketing of products around reproductive health and well-being (Felizi and Varon nd).

As has been pointed out in the case of big data more broadly, reproductive health applications (apps) facilitate the visibility of the female reproductive body in the public domain. Supplying anonymised data sets to medical researchers and universities fills some of the historical gaps in research around the female body and reproductive health. Reproductive and sexual health tracking apps globally provide their users a platform to engage with biomedical information around sexual and reproductive health. Through group chats on the platform, they are also able to engage with experiential knowledge of sexual and reproductive health. This could also help form transnational networks of solidarity around the body and health (Fotopoulou 2016).

This radical potential of network-building around reproductive and sexual health is, however, tempered to a large extent by the reconfiguration of gendered stereotypes through these apps. In a study on reproductive health apps on Google Play Store, Lupton (2014) finds that products targeted towards female users are marketed through the discourse of risk and vulnerability, while those targeted towards male users are framed within that of virility. Apart from reiterating gendered stereotypes around the male and female body, such a discourse assumes that the entire labour of family planning is performed by females. This same is the case with the MCTS/RCH.

Technological interventions such as reproductive health apps as well as HIS are based on the assumption that females have perfect control over decisions regarding their own bodies and reproductive health, despite this being disproved in India. The Guttmacher Institute (2014) has found that 60% of women in India report not having control over decisions regarding their own healthcare. The failure to account for the husband or the family as stakeholder in decision-making around reproductive health has been a historical failure of the family planning programme in India, and is now being replicated in other modalities. This notion of an autonomous citizen who is able to take responsibility of their own reproductive health and well-being does not hold true in the Indian context. It can even be seen as marginalising females who have already been excluded from the reproductive health system, as they are held responsible for their own inability to access healthcare.

Concluding Remarks

The interplay that emerges between reproductive health surveillance and data infrastructures is a complex one. It requires the careful positioning of the political nature of data collection and processing as well as its hetero-patriarchal and colonial legacies, within the need for effective utilisation of data for achieving developmental goals. Assessing this discourse through a feminist lens identifies the web of power relations in data regimes. This problematises narratives of technological solutions for welfare provision.

The reproductive healthcare framework in India then offers up a useful case study to assess these concerns. The growing adoption of ICT-based surveillance tools to equalise access to healthcare needs to be understood in the socio-economic, legal, and cultural context where these tools are being implemented. Increased surveillance has historically been associated with causing the structural gendered violence that it is now being offered as a solution to. This is a function of normative standards being constructed for reproductive behaviour that necessarily leave out broader definitions of reproductive health and welfare when viewed through a feminist lens. Within the larger context of health policymaking in India, moves towards privatisation then demonstrate the peculiarity of dataveillance as it functions through an unaccountable and pervasive overlapping of state and private surveillance practises. It remains to be seen how these trends in ICT-driven health policies affect access to reproductive rights and decisional privacy for millions of females in India and other parts of the global South.

For more details visit https://cis-india.org/internet-governance/blog/data-infrastructures-inequities-reproductive-health-surveillance-india

National Health Stack: Data For Data’s Sake, A Manmade Health Hazard

Murali Neelakantan, Swaraj Barooah, Swagam Dasgupta and Torsha Sarkar — 2018-09-16T05:01:18Z

On Oct. 5, 2017, an HIV positive woman was denied admission in Hyderabad’s Osmania General Hospital even though she was entitled to free treatment under India’s National AIDS Control Organisation programme. Another incident around the same time witnessed a 24-year-old pregnant woman at Tikamgarh district hospital in Madhya Pradesh being denied treatment by hospital doctors once she tested positive for HIV. The patient reportedly delivered the twins outside the maternity ward after she was turned away by the hospital, but her newborn twin girls died soon after.

The op-ed was published in Bloomberg Quint on August 14, 2018.

Apart from facing the severity of their condition, patients afflicted with diseases such as HIV, tuberculosis, and mental illnesses, are often subject to social stigma, sometimes even leading to the denial of medical treatment. Given this grim reality would patients want their full medical history in a database?

The ‘National Health Stack’ as described by the NITI Aayog in its consultation paper, is an ambitious attempt to build a digital infrastructure with a “deep understanding of the incentive structures prevalent in the Indian healthcare ecosystem”. If the government is to create a database of individuals’ health records, then it should appreciate the differential impact that it could have on the patients.

The collection of health data, without sensitisation and accountability, has the potential to deny healthcare to the vulnerable.

We have innumerable instances of denial of services due to Aadhaar and there is a real risk that another database will lead to more denial of access to the most vulnerable.

Earlier, we had outlined some key aspects of the NHS, the ‘world’s largest’ government-funded national healthcare scheme. Here we discuss some of the core technical issues surrounding the question of data collection, updating, quality, and utilisation.

Resting On A Flimsy Foundation: The Unique Health ID

The National Health Stack envisages the creation of a unique ID for registered beneficiaries in the system — a ‘Digital Health ID’. Upon the submission of a ‘national identifier’ and completion of the Know Your Customer process, the patient would be registered in the system, and a unique health ID generated.

This seemingly straightforward process rests on a very flimsy foundation. The base entry in the beneficiary registry would be linked to a ‘strong foundational ID’. Extreme care needs to be taken to ensure that this is not limited to an Aadhaar number. Currently, the unavailability of Aadhaar would not be a ground for denial of treatment to a patient only for their first visit; the patient must provide Aadhaar or an Aadhaar enrolment slip to avail treatment thereafter. This suggests that the national healthcare infrastructure will be geared towards increasing Aadhaar enrollment, with the unstated implication that healthcare is a benefit or subsidy — a largess of government, and not, as the courts have confirmed, a fundamental right.

Not only is this project using government-funded infrastructure to deny its citizens the fundamental right to healthcare, it is using the desperate need of the vulnerable for healthcare to push the ‘Aadhaar’ agenda.

Any pretence that Aadhaar is voluntary is slowly fading with the government mandating it at every step of our lives.

Aadhaar Seva kendra. (Source: Aadhaar Official Account/Facebook

Is The Health ID An Effective And Unique Identifier?

Even if we choose to look past the fact that the validity of Aadhaar is still pending the test of legality before the apex court, a foundational ID would mean that the data contained within that ID is unique, accurate, incorruptible, and cannot be misused. These principles, unfortunately, have been compromised by the UIDAI in the Aadhaar project with its lack of uniqueness of identity (i.e, fake IDs and duplicity), failure to authenticate identity, numerous alleged data leaks (‘alleged’ because UIDAI maintains that there haven’t been any leaks), lack of connectivity to be able to authenticate identity and numerous instances of inaccurate information which cannot be corrected.

Linking something as crucial and basic as healthcare data with such a database is a potential disaster.

There is a real risk that incorrect linking could cause deaths or inappropriate medical care.

The High Risk Of Poor Quality Data

The NITI Aayog paper envisages several expansive databases that are capable of being updated by different entities. It includes enrollment and updating processes but seems to assume that all these extra steps will be taken by all the relevant stakeholders and does not explain the motivation for stakeholders to do so.

In a country where government doctors, hospitals, wellness centres, etc are overburdened and understaffed, this reliance is simply not credible. For instance, all attributes within the registries are to be digitally signed by an authorised updater, there must be an audit trail for all changes made to the registries, and surveyors will be tasked with visiting providers in person to validate the data. Identifying these precautions as measures to assure accurate data is a great step towards building a national health database, but this seems an impossible task.

Who are these actors and what will incentivise them to ensure the accuracy and integrity of data?

In other words, what incentive and accountability structures will ensure that data entry and updating is accurate, and not approached from a more ‘jugaad’ ‘let’s just get this done for the sake of it’ attitude that permeates much of the country. How will patients have access to the database to be able to check its accuracy? Is it possible for a patient (who will presumably be ill) to gain easy access to an updater to change their data? If so, how? It is worth noting that the patient’s ‘right’ to check her data assumes that they have access to a computer that is connected to the internet as well as a good level of digital literacy, which is not the case in India for a significant section of the population. Even data portability loses its potential benefits if the quality of data on these registries is not reliable. In this case, healthcare providers will need to verify their patients’ health history using physical records instead, rendering the stack redundant.

Who will be liable to the patient for misdiagnosis based on the database?

A sonographic image is displayed on a monitor as a patient undergoes an ultrasound scan in Bikaner, Rajasthan, India. (Photographer: Prashanth Vishwanathan/Bloomberg)

Leaving the question of accountability vague opens updaters to the possibility of facing dangerous and unnecessarily punitive measures in the future. The NITI Aayog paper fails to address this key issue which arose recently. Despite being a notifiable disease, there are reports that numerous doctors from the private sector failed to notify or update TB cases to the Ministry of Health and Family Welfare ostensibly on the grounds that they did not receive consent from their patients to share their information with the government. This was met with a harsh response from the government which stated that clinical establishment that failed to notify tuberculosis patients would face jail time. According to a few doctors, the government’s new move would coerce patients to go to ‘underground clinics’ to receive treatment discreetly and hence, would not solve the issue of TB.

The document also offers no specific recommended procedures regarding how inaccurate entries will be corrected or deleted.

It is then perhaps not a stretch to imagine that these scenarios would affect the quality of the data stored; defeating NITI Aayog’s objective of researchers using the stack for high-quality medical data.

The reason why the quality and integrity of data is at the head of the table is that all the proposed applications of the NHS (analytics, fraud detection etc.) assume a high quality, accurate dataset. At the same time, the enrolment process, updating process and disclosed measures to ensure data quality will effectively lead to poor quality data. If this is the case, then applications derived from the NHS dataset should assume an imperfect data, rather than an accurate dataset, which should make one wonder if no data is better than data that is certainly inaccurate.

Lack Of Data Utilisation Guidelines

Issues with data quality are exacerbated depending on how and where it is used, and who uses it. The paper has identified some users to be health-sector stakeholders such as healthcare providers (hospitals, clinics, labs etc), beneficiaries, doctors, insurers and accredited social health activists but misses laying down utilisation guidelines. The foresight to create a dataset that can be utilised by multiple actors for numerous applications is commendable, but potentially problematic -- especially if guidelines on how this data is to be used by stakeholders (especially the private sector) are ignored.

In order to bridge this knowledge gap, India has the opportunity to learn from the legal precedent set by foreign institutions. As an example, one could examine the Health Information Technology for Economic and Clinical Health Act (HITECH) and the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. which sets out strict guidelines for how businesses are to handle sensitive health data in order to maintain the individual’s privacy and security. It goes one step further to also lay down incentive and accountability structures in order that business associates necessarily report security breaches to their respective covered entities.

If we do not take necessary precautions now, we not only run the risk of poor security and breach of privacy but of inaccurate data that renders the national health data repository a health risk for the whole patient population.

There’s also the lack of clarity on who is meant to benefit from using such a database or whether the benefits are equal to all stakeholders, but more on that in a subsequent piece.

A medical team uses a glucometer to check the blood glucose level of a patient at a mobile clinic in Pancharala, on the outskirts of Bengaluru, India. (Photographer: Dhiraj Singh/Bloomberg)

It’s Your Recipe, You Try It First!

If the NITI Aayog and the government are sure that there is a need for a national healthcare database, perhaps they can start using the Central Government Health Scheme (which includes all current and retired government employees and their families) as a pilot scheme for this. Once the software, database and the various apps built on it are found to be good value for money and patients benefit from excellent treatment all over the country, it could be expanded to those who use the Employees’ State Insurance system, and then perhaps to the armed forces. After all, these three groups already have a unique identifier and would benefit from the portability of healthcare records since they are likely to be transferred and posted all over the country. If, and only if, it works for these groups and the claimed benefits are observed, then perhaps it can be expanded to the rest of the country’s healthcare systems.

Murali Neelakantan is an expert in healthcare laws. Swaraj Barooah is Policy Director at The Centre for Internet and Society. Swagam Dasgupta and Torsha Sarkar are interns at The Centre for Internet and Society.

For more details visit https://cis-india.org/internet-governance/blog/bloomberg-quint-murali-neelakantan-swaraj-barooah-swagam-dasgupta-torsha-sarkar-august-14-2018-national-health-stack-data-for-datas-sake-a-manmade-health-hazard

Comments on the Draft Digital Information Security in Healthcare Act

Amber Sinha and Shweta Mohandas — 2018-05-01T02:05:58Z

The Centre for Internet & Society submitted comments to the Ministry of Health & Family Welfare, Government of India on the draft Digital Information Security in Healthcare Act on April 21, 2018.

This submission presents comments by the Centre for Internet and Society, India (“CIS”) on the Draft Digital Information Security in Healthcare Act, released by Ministry of Health & Family Welfare, Government of India. CIS has conducted research on the issues of privacy, data protection and data security since 2010 and is thankful for the opportunity to put forth its views. This submission was made on April 21, 2018.

Download the full submission here

For more details visit https://cis-india.org/internet-governance/blog/comments-on-the-draft-digital-information-security-in-healthcare-act

Roundtable on Artificial Intelligence & Healthcare

Admin — 2018-01-02T13:49:14Z

Centre for Internet & Society (CIS) is organizing a roundtable on artificial intelligence (AI) and healthcare at 'The Energy and Resources Institute' (TERI) in Bengaluru on November 30, 2017 from 2 p.m. to 5 p.m. The roundtable seeks to discuss the various issues and challenges surrounding the implementation of AI and related technologies in the Indian healthcare sector.

The Indian healthcare industry, powered by Artificial Intelligence, is moving into a new era of increased innovation and independence. With multiple new healthcare start-ups and large ICT companies such as Microsoft, IBM, and Google offering AI solutions to healthcare challenges in the country, it is evident that AI is attempting to enhance the accessibility, affordability, quality and awareness of healthcare in India. Major target areas sought to be enhanced by use of AI in healthcare include addressing the uneven ratio of skilled doctors to patients and making doctors more efficient at their jobs, delivery of personalized and high-quality healthcare to rural areas, and training doctors and nurses in complex procedures.

Through the application of machine learning, data mining, natural language processing (NLP), and advanced analytics, AI can help doctors in speedy diagnosis of diseases. AI is also mobilised as ‘smart advisors’ or virtual humans who are capable of making informed decisions by better comprehending data and information through sensing interfaces and analytics, in various forms.

Some of these forms include ‘customer service agents’ that can expedite simple tasks like appointment scheduling, or more complex decisions like selecting health plan benefits, ‘clinicians’ that can help with primary screening in understaffed rural areas possibly substituting for human labour, and ‘cognitive agents’ that can efficiently manage existing clinical knowledge alongside physicians, nurses and researchers, thereby reducing the cognitive load on humans. AI based Indian healthcare start-ups such as SigTuple, Aindra, Ten3T, Touchkin and many others are offering a range of solutions including automation of medical diagnosis, automated analysis of medical tests, detection and screening of diseases, wearable sensor based medical devices and monitoring equipment, patient management systems, predictive healthcare diagnosis and disease prevention.

However, AI in healthcare raises many potential concerns, a common one being the lack of comprehensive, representative, interoperable, and clean data - a challenge that is beginning to be addressed through the Electronic Health Records Standards developed by the Ministry of Health and Family Welfare in 2016 by the Ministry of Health and Family Welfare. Other major challenges include patient adoption and the need for personal interaction with doctors, concerns over mass-scale job losses, distrust in technology, and ethical concerns.

It is imperative to note that implementing AI in healthcare, which is bound to disrupt it, does not imply replacing doctors but augmenting their efforts to create a more efficient healthcare landscape in the country. A harmonious collaboration of man and machine is expected to bring about a meaningful and long-lasting impact and stakeholders should be prepared to adapt to this change and the challenges that come with it.

Roundtable Agenda

Thursday, November 30, 2017, 2:00pm - 5:00pm

2:00 - 2:30: Introduction and setting the scene

2:30 - 3:30: Discussion on the AI landscape in health in India:

Manner and extent of integration of AI into products/services of healthcare companies.
Relevant stakeholders and their roles in implementing AI into products/services of healthcare companies.
Future of AI and related technologies in the healthcare sector

3:30 - 4:30: Discussion on challenges and solutions towards regulating AI in India:

Challenges faced in the conception and implementation of the AI product/service, and reasons for such challenges.
Regulatory provisions for implementation of AI in healthcare products/services under the existing laws, and need for reforms.
Challenges posed by AI to existing policy and regulatory frameworks in the Indian as well as the global context, and possible solutions.

Click to download the invite

For more details visit https://cis-india.org/internet-governance/events/roundtable-on-artificial-intelligence-and-healthcare