Is India's Digital Health System Foolproof?

This contribution builds on "Data Infrastructures and Inequities: Why Does Reproductive Health Surveillance in India Need Our Urgent Attention?" and seeks to understand the role that state-run reproductive health portals such as the Mother and Child Tracking System and the Reproductive and Child Health will play going forward. To do so, the article critically outlines the overall digitised health information ecosystem being envisioned by the Indian state. 

Introduced in 2013 and subsequently updated in 2016, the Ministry of Health and Family Welfare (MHFW) published a document laying out the standards for electronic health records (EHRs). While there exist varying interpretations of what constitutes as EHRs, some of its characteristics include electronic medical records (EMRs) of individual patients, arrangement of these records in a time series, and inter-operable linkages of the EMRs across various healthcare settings (Häyrinen et al 2008; OECD 2013). 

To work effectively, EHRs are required to be highly interoperable so that they can facilitate exchange among health information systems (HIS) across participating hospitals. For this, the Integrated Health Information Platform (IHIP) is being developed so as to assimilate data from various registries across India and provide real-time information on health surveillance (Krishnamurthy 2018).

EHR Implementation: Unpacking the (Dis)incentive Structure

As the implementation of EHR standards is voluntary, anecdotal evidence indicates that their uptake in the Indian healthcare sector has been very slow. Here, the opposition of the Indian Medical Association to the Clinical Establishments (Registration and Regulation) Act, 2010, resulting in nationwide protests and subsequent legal challenges to the act, is instructive. To start with, the act prescribes the minimum standards that have to be maintained by clinical establishments which are registered or seeking registration (itself mandatory to run a clinic under the act).1 Further, Rule 9(ii) of the Clinical Establishments (Registration and Regulation) Rules, 2012, drafted under the act, requires clinical establishments to maintain EMRs or EHRs for every patient. However, with health being a state subject in India, the act has only been enforced in 11 states and all union territories except the National Capital Territory of Delhi (Jyoti 2018). The resistance to the act is largely due to protests by stakeholders from within the medical fraternity regarding its adverse impact on small- and medium-sized hospitals (Jyoti 2018).

Contextualising Clinicians' Inertia

Another major impediment to the adoption of EHRs by health service providers is reluctance on the part of individual physicians to transition to an EHR system. This is because compliance with EHR standards requires physicians to input clinical notes themselves.

Comparing the greater patient load faced by doctors in India vis-à-vis the United States (US), the chief medical officer of an EHR vendor in India estimates that the average Indian doctor sees about 40–60 patients a day, whereas in the US it may be around 18–20 patients (Kandhari 2017). This is suggestive of the wide disparity in the number of physicians per 1,000 citizens in both countries (World Bank nd). Given this, doctors in India tend to be more problem-oriented, time-strapped, and pay less attention to clinical notes (Kandhari 2017). Thus, clinicians will consider a system to be efficient only if the system reduces their documentation time, even if the time savings do not translate into better patient care (Allan and Englebright 2000). The inability of EHRs to help reduce documentation time deters clinicians from supporting their implementation (Poon et al 2004). Additionally, research done in the United States indicates that there is no evidence to suggest that an information system helps save time expended by clinicians on documentation (Daly et al 2002). Moreover, the use of an information system is stated to have had no impact on patient care, but doctors have acknowledged its use for research purposes (Holzemer and Henry 1992).

Prohibitive Costs of Implementation

While national-level EHRs have been adopted globally, their distribution across countries is telling. In a survey published in 2016 by the World Health Organization, wealthier countries were over-represented, with two-thirds from the upper-middle-income group and roughly half from the high-income countries having introduced EHR systems. On the other hand, only a third of lower-middle-income countries and 15% of low-income countries reported having implemented EHRs (World Health Organization 2016). A major reason for the slow uptake of EHRs in poorer countries is likely to be funding as EHR implementation requires considerable investment, with most projects averaging several million dollars (US) (Kuperman and Gibson 2003). Although various funding models for EHR implementation are being utilised globally, it is unclear what model will be adopted in India to bring in private healthcare service providers within its ambit  (Healthcare Information and Management Systems Society 2007). This absence of funding direction for private actors poses to be a significant impediment in the integration of private databases with other public ones.

In general, poorer countries are also more likely to have less developed infrastructure and health Information and Communication Technology (ICT) to support EHR systems. Besides this, they not only lack the capacity and human resources required to develop and maintain such complex systems (Tierney et al 2010; McGinn et al 2011), but training periods have also been found to be long and more costly than expected (Kovener et al 1997). 

Socio-economic Exclusions and Cross-cultural Barriers

There exists scant research investigating the existing use of EHRs in India, though preliminary work is being undertaken to assess EHR implementation in other developing countries (Tierney et al 2010; Fraser et al 2005). Even in the context of developed countries, where widespread adoption of EHRs has been gaining traction for some time now, very little data exists around implementation and efficacy in underserved regions and communities. This is further problematised as clinical information systems and user populations also vary in their characteristics and, for this reason, individual studies are unable to identify common trends that would predict EHR implementation success.

Underserved settings may lack the infrastructure needed to support EHRs. The risk of exclusion already exists in parts such as difficulties inherent in delivering care to remote locations, barriers related to cross-cultural communication, and the pervasive problem of providing care in the setting of severe resource constraints. Equally important is the fact that health workers who already report significant existing impediments in their delivery of routine care in these settings do not necessarily see EHRs as being useful in catering to the specific needs of their patient population (Bach et al 2004). Moreover, experience with EHRs also reveals that there are cultural barriers to capturing accurate data (Miklin et al 2019). What this could mean is that stigma associated with the diagnosis of conditions such as HIV/AIDS or induced abortions will result in their under-reporting even within EHR systems.      

Stick or Twist?

Other modalities have been devised to nudge healthcare providers into adopting EHR standards voluntarily. The National Accreditation Board for Hospitals and Healthcare Providers (NABH), India, a constituent board of the Quality Council of India (a public–private initiative), has been reported to have incorporated the EHR standards within its accreditation matrix. NABH accreditation, considered an indicator of high quality patient care, is highly sought–after by hospitals in India in order to attract medical tourists as well as insurance companies: two prominent sources of income for hospitals (Kandhari 2017). Additionally, NABH accreditation is valid for a term of three years, thus requiring hospitals seeking to renew their accreditation to adopt EHR standards as well. 

Another commercial use of EHR has been in health insurance. The Federation of Indian Chambers of Commerce and Industry (FICCI) and the Insurance Regulatory and Development Authority (IRDAI) have both voiced their support for expediting the implementation of the EHR standards (EMR Standards Committee 2013). Both, the FICCI and IRDAI have placed emphasis on adopting EHRs, seeing it as a necessary move for formalising the health insurance industry (FICCI 2015). They have also had representation on the committee that sent recommendations to the MHFW on the first version of the EHR standards in 2013 (FICCI 2015). FICCI had additionally played a coordination role in having the recommendations framed for the 2013 EHR standards. 

Fluid Data Objectives

The push for EHR implementation is emblematic of a larger shift in the healthcare approach of the Indian state, that of an indirect targeting of demand-side financing by plugging data inefficiencies in health insurance.

The draft National Health Policy (NHP), published in 2015, reflected the mandate of the Ministry of Health and Family Welfare to strengthen the public health system by creating a right to healthcare legislation and reaching a public spend of 2.5% of the gross domestic product by 2018. The final version of the NHP, published in 2017, however, codified a shift in healthcare policy by focusing on strategic purchasing of secondary and tertiary care services from the private sector and a publicly funded health insurance model.      

In line with the vision of the NHP 2017, in February 2018, the Union Minister for Finance and Corporate Affairs, Arun Jaitley, announced two major initiatives as a part of the government’s Ayushman Bharat programme (Ministry of Finance 2018). Administered under the aegis of the Ministry of Health and Family Welfare, these initiatives are intended to improve access to primary healthcare through the creation of 150,000 health and wellness centres as envisioned under the NHP 2017, and improve access to secondary and tertiary healthcare for over 100 million vulnerable families by providing insurance cover of up to ₹ 500,000 per family per year under the Pradhan Mantri–Rashtriya Swasthya Suraksha Mission/National Health Protection Scheme (PM–RSSM/NHPS) (Ministry of Health and Family Welfare 2018). The NHPS, modelled along the lines of the Affordable Care Act in the US, was later rebranded as the Pradhan Mantri–Jan Arogya Yojana (PM-JAY) at the time of its launch in September 2018. It is claimed to be the world’s largest government-funded healthcare programme and is intentioned to provide health insurance coverage for vulnerable sections in lieu of the Sustainable Development Goal-3 (National Health Authority nd).

To enable the implementation of the Ayushman Bharat programme, the NITI Aayog then proposed the creation of a supply-side digital infrastructure called National Health Stack (NHS) (NITI Aayog 2018). As outlined in the consultation and strategy paper, the NHS is “built for NHPS, but beyond NHPS.” The NHS seeks to leverage the digitisation push through IndiaStack, which seeks to digitalise “any large-scale health insurance program, in particular, any government-funded health care programs.” The synergy is clear, with the NHPS scheme also aiming to be “cashless and paperless at public hospitals and empanelled private hospitals" (National Health Authority nd).2 

The NHS is also closely aligned with the NHP 2017, which draws attention to leveraging technologies such as big data analytics on data stored in universal registries. The Vision document for the NHS emphasises the fragmented nature of health data as an impediment to reducing inequities in healthcare provision. The NHS, then, also seeks to be the master repository of health data akin to the IHIP. By creating a base layer of registries containing information about various actors involved in the healthcare supply chain (providers such as hospitals, beneficiaries, doctors, insurers and Accredited Social Health Activists), it potentially allows for recording of data from both public and private sector entities, plugging a significant gap in the coverage of the HIS currently implemented in India. With the provision of open, pullable APIs, the NHS also shares the motivations of the IndiaStack to monetise health data. 

A key component of the proposed NHS is the Coverage and Claims platform, which the vision document describes as “provid[ing] the building blocks required to implement any large-scale health insurance program, in particular, any government-funded healthcare programs. This platform has the transformative vision of enabling both public and private actors to implement insurance schemes in an automated, data-driven manner through open APIs " (NITI Aayog2018). A post on the iSPIRT website further explains the centrality of this Coverage and Claims platform in enabling a highly personalised medical insurance market in India: “This component will not only bring down the cost of processing a claim but ... increased access to information about an individual’s health and claims history ... will also enable the creation of personalised, sachet-sized insurance policies." These data-driven customised insurance policies are expected to generate “care policies that are not only personalized in nature but that also incentivize good healthcare practices amongst consumers and providers … [and] use of techniques from microeconomics to manage incentives for care providers, and those from behavioural economics to incentivise consumers" (Productnation Network 2019). The Coverage and Claims platform, and especially the Policy (generation) Engine that it will contain, is aimed at intensive financialisation of personal healthcare expenses, and extensive experiments with designing personalised nudges to shape the demand behaviour of consumers.

The imagination of healthcare the NHS demonstrates is one where broadening health insurance coverage is equated to providing equitable healthcare and as a panacea for the public healthcare sector. The first phase of this push towards better healthcare provision is to focus on contextualising the historical socio-economic divide. The next phase is characterised by digitalisation: the introduction of ICT to bridge the socio-economic divide in healthcare provision. In this process, the resulting data divide has been invisibilised in reframing better healthcare as an insurance problem for which data needs to be generated. Each policy innovation is then characterised by further marginalisation of those that were originally identified as underserved. This is a result of increasing repercussions of the data-divide, with access to benefits increasingly being mediated by technology. 

Concluding Remarks 

The idea that any person in India can go to any health service provider/ practitioner, any diagnostic center or any pharmacy and yet be able to access and have fully integrated and always available health records in an electronic format is not only empowering but also the vision for efficient 21st century healthcare delivery.

— Ministry of Health and Family Welfare, Electronic Health Record Standards For India (2013)

The objective of health data collection has evolved over the course of the institution of the HIS in 2011, to the development of the NHPS and National Health Policy in 2017. What began as a solution to measure and address gaps in access and quality in healthcare provisioning through data analysis has morphed into data centralisation and insurance coverage. Shifting goalposts can also be found in the objectives behind introducing digital systems to collect data. 

In recent iterations of the healthcare imaginary, such as the IHIP and the NHS, data ownership by the beneficiaries is stressed upon. In the absence of a rights-based framework dictating the use of data, the role of ownership should be interrogated, especially in the context of a prevalent data divide (Tisne 2019). The legitimisation of data capture can be seen in the emergence of opt-in models of consent, data fiduciaries managing consent on the data subject’s behalf, etc. (Zuboff 2019).

This framing forecloses a discussion about the quality and kind of data being used. The push towards datafication needs to be questioned for its re-indexing of categorical meaning away from the complexities of narrative, context and history (Cheney-Lippold 2018). Instead, the proposed solution is one that stores datafied elements within a closed set (reproductive health= [abortion, aids, contraceptive,...vaccination, womb]). While this set may be editable, so new interpretations can be codified, it inherently remains stable, assuming a static relationship between words and meaning. Health is then treated as having an empirically definable meaning, thus losing the dynamism of what the health and wellness discourse could entail. 

It has been historically demonstrated in the Indian context that multiple tools and databases for health data management are a barrier to an efficient HIS. However, generating centralised or federated databases without addressing concerns in data flows, quality, uses in existing data structures, and the digital divide across health workers and beneficiaries alike will lead to the amplification of existing exclusions in data and, consequently, service provisioning. 

 
The author would like to express his gratitude to Sumandro Chattapadhyay and Ambika Tandon for their inputs and editorial work on this contribution. This work was supported by the Big Data for Development Network established by International Development Research Centre (Canada).

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