Centre for Internet and Society

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

Civil Society’s second opinion on a UHI prescription

Pallavi Bedi and Shweta Mohandas — 2023-02-15T08:20:15Z

On January 13, Pallavi Bedi and Shweta Mohandas from CIS participated in an online collaboration organised by Internet Freedom Foundation for a joint submission to the Consultation Paper on Operationalising Unified Health Interface (UHI) in India released by the National Health Authority.

The article originally published by Internet Freedom Foundation can be accessed here.

The National Health Authority (NHA) released the Consultation Paper on Operationalising Unified Health Interface (UHI) in India on December 14, 2022. The deadline for submission of comments was January 13, 2023. We collaborated with the Centre for Health Equity, Law & Policy, the Centre for Internet & Society, & the Forum for Medical Ethics Society to submit comments on the paper.

Background

The UHI is proposed to be a “foundational layer of the Ayushman Bharat Digital Health Mission (ABDM)” and is “envisioned to enable interoperability of health services in India through open protocols”. The ABDM, previously known as the National Digital Health Mission, was announced by the Prime Minister on the 74th Independence Day, and it envisages the creation of a National Digital Health Ecosystem with six key features: Health ID, Digi Doctor, Health Facility Registry, Personal Health Records, Telemedicine, and e-Pharmacy. After launching the programme in six Union Territories, the National Health Authority issued a press release on August 26, 2020 announcing the public consultation for the Draft Health Data Management Policy for NDHM. While the government has repeatedly claimed that creation of a health ID is purely voluntary, contrary reports have emerged. In our comments as part of the public consultation, our primary recommendation was that deployment of any digital health ID programme must be preceded by the enactment of general and sectoral data protection laws by the Parliament of India; and meaningful public consultation which reaches out to vulnerable groups which face the greatest privacy risks.

As per the synopsis document which accompanies the consultation paper, it aims to “seek feedback on how different elements of UHI should function. Inviting public feedback will allow for early course correction, which will in-turn engender trust in the network and enhance market adoption. The feedback received through this consultation will be used to refine the functionalities of UHI so as to limit any operational issues going forward.” The consultation paper contains a set of close-ended questions at the end of each section through which specific feedback has been invited from interested stakeholders. We have collaborated with the Centre for Health Equity, Law & Policy, the Centre for Internet & Society, & the Forum for Medical Ethics Society to draft the comments on this consultation paper.

Our main concern relates to the approach the Government of India and concerned Ministries adopt to draft a consultation paper without explicitly outlining how the proposed UHI fits into the broader healthcare ecosystem and quantifying how it improves it rendering the consultation paper and public engagement efforts inadequate. Additionally, it doesn’t allow the public at large, and other stakeholders to understand how it may contribute to people’s access to quality care towards ensuring realisation of their constitutional right to health and health care. The close-ended nature of the consultation process, wherein specific questions have been posed, restricts stakeholders from questioning the structure of the ABDM itself and forces us to engage with its parts, thereby incorrectly assuming that there is support for the direction in which the ABDM is being developed.

Our submissions

A. General comments

a. Absence of underlying legal framework

Ensuring health data privacy requires legislation at three levels- comprehensive laws, sectoral laws and informal rules. Here, the existing proposal for the data protection legislation, i.e., the draft Digital Personal Data Protection Bill, 2022 (DPDPB, 2022) which could act as the comprehensive legal framework, is inadequate to sufficiently protect health data. This inadequacy arises from the failure of the DPDPB, 2022 to give higher degree of protection to sensitive personal data and allowing for non-consensual processing of health data in certain situations under Clause 8 which relates to “deemed consent”. Here, it may also be noted that the DPDPB, 2022 fails to specifically define either health or health data. Further, the proposed Digital Information Security in Healthcare Act, 2017, which may have acted as a sectoral law, is presently before the Parliament and has not been enacted. Here, the absence of safeguards allows for data capture by health insurance firms and subsequent exclusion/higher costs for vulnerable groups of people. Similarly, such data capture by other third parties potentially leads to commercial interests creeping in at the cost of users of health care services and breach of their privacy and dignity.

b. Issues pertaining to scope

Clarity is needed on whether UHI will be only providing healthcare services through private entities, or will also include the public health care system and various health care schemes and programs of the government, such as eSanjeevani.

c. Pre-existing concerns

Exclusion: Access to health services through the Unified Health Interface should not be made contingent upon possessing an ABHA ID, as alluded to in the section on ‘UHI protocols in action: An example’ under Chapter 2(b). Such an approach is contrary to the Health Data Management Policy that is based on individual autonomy and voluntary participation. Clause 16.4 of the Policy clearly states that nobody will “be denied access to any health facility or service or any other right in any manner by any government or private entity, merely by reason of not creating a Health ID or disclosing their Health ID…or for not being in possession of a Health ID.” Moreover, the National Medical Commission Guidelines for Telemedicine in India also does not create any obligation for the patient to possess an ABHA ID in order to access any telehealth service. The UHI should explicitly state that a patient can log in on the network using any identification and not just ABHA.
Consent: As per media reports, registration for a UHID under the NDHM, which is an earlier version of the ABHA number under the ABDM, may have been voluntary on paper but it was being made mandatory in practice by hospital administrators and heads of departments. Similarly, reports suggest that people who received vaccination against COVID-19 were assigned a UHID number without their consent or knowledge.
Function creep: In the absence of an underlying legal framework, concerns also arise that the health data under the NDHM scheme may suffer from function creep, i.e., the collected data being used for purposes other than for which consent has been obtained. These concerns arise due to similar function creep taking place in the context of data collected by the Aarogya Setu application, which has now pivoted from being a contact-tracing application to “health app of the nation”. Here, it must be noted that as per a RTI response dated June 8, 2022 from NIC, the Aarogya Setu Data Access And Knowledge Sharing Protocol “has been discontinued".
Issues with the United Payments Interface may be replicated by the UHI: The consultation paper cites the United Payments Interface (UPI) as “strong public digital infrastructure” which the UHI aims to leverage. However, a trend towards market concentration can be witnessed in UPI: the two largest entities, GooglePay and PhonePe, have seen their market share hover around 35% and 47% (by volume) for some time now (their share by value transacted is even higher). Meanwhile, the share of the NPCI’s own app (BHIM) has fallen from 40% in August 2017 to 0.74% in September 2021. Thus, if such a model is to be adopted, it is important to study the UPI model to understand such threats and ensure that a similar trend towards oligopoly or monopoly formation in UHI is addressed. This is all the more important in a country in which the decreasing share of the public health sector has led to skyrocketing healthcare costs for citizens.

B. Our response also addressed specific questions about search and discovery, service booking, grievance redressal, and fake reviews and scores. Our responses on these questions can be found in our comments here.

Our previous submissions on health data

We have consistently engaged with the government since the announcement of the NDHM in 2020. Some of our submissions and other outputs are linked below:

IFF’s comment on the Draft Health Data Management Policy dated May 21, 2022 (link)
IFF’s comments on the consultation Paper on Healthcare Professionals Registry dated July 20, 2021 (link)
IFF and C-HELP Working Paper: ‘Analysing the NDHM Health Data Management Policy’ dated June 11, 2021 (link)
IFF’s Consultation Response to Draft Health Data Retention Policy dated January 6, 2021 (link)
IFF’s comments on the National Digital Health Mission’s Health Data Management Policy dated September 21, 2020 (link)

Important documents

Response on the Consultation Paper on Operationalising Unified Health Interface (UHI) in India by Centre for Health Equity, Law & Policy, the Centre for Internet & Society, the Forum for Medical Ethics Society, & IFF dated January 13, 2023 (link)
NHA’s Consultation Paper on Operationalising Unified Health Interface (UHI) in India dated December 14, 2022 (link)
Synopsis of NHA’s Consultation Paper on Operationalising Unified Health Interface (UHI) in India dated December 14, 2022 (link)

For more details visit https://cis-india.org/internet-governance/blog/civil-society-second-opinion-on-uhi-prescription

Big Data and Reproductive Health in India: A Case Study of the Mother and Child Tracking System

ambika — 2019-12-06T04:57:55Z

In this case study undertaken as part of the Big Data for Development (BD4D) network, Ambika Tandon evaluates the Mother and Child Tracking System (MCTS) as data-driven initiative in reproductive health at the national level in India. The study also assesses the potential of MCTS to contribute towards the big data landscape on reproductive health in the country, as the Indian state’s imagination of health informatics moves towards big data.

Case study: Download (PDF)

Introduction

The reproductive health information ecosystem in India comprises of a range of different databases across state and national levels. These collect data through a combination of manual and digital tools. Two national-level databases have been launched by the Ministry of Health and Family Welfare - the Health Management Information System (HMIS) in 2008, and the MCTS in 2009. 4 The MCTS focuses on collecting data on maternal and child health. It was instituted due to reported gaps in the HMIS, which records monthly data across health programmes including reproductive health. There are several other state-level initiatives on reproductive health data that have either been subsumed into, or run in parallel with, the MCTS.

With this case study, we aim to evaluate the MCTS as data-driven initiative in reproductive health at the national level. It will also assess its potential to contribute towards the big data landscape on reproductive health in the country, as the Indian state’s imagination of health informatics moves towards big data. The methodology for the case study involved a desk-based review of existing literature on the use of health information systems globally, as well as analysis of government reports, journal articles, media coverage, policy documents, and other material on the MCTS.

The first section of this report details the theoretical framing of the case study, drawing on the feminist critique of reproductive data systems. The second section maps the current landscape of reproductive health data produced by the state in India, with a focus on data flows, and barriers to data collection and analysis at the local and national level. The case of abortion data is used to further the argument of flawed data collection systems at the national level. Section three briefly discusses the state’s imagination of reproductive health policy and the role of data systems through a discussion on the National Health Policy, 2017 and the National Health Stack, 2018. Finally, we make some policy recommendations and identify directions for future research, taking into account the ongoing shift towards big data globally to democratise reproductive healthcare.

For more details visit https://cis-india.org/raw/big-data-reproductive-health-india-mcts

AI for Healthcare: Understanding Data Supply Chain and Auditability in India

2024-11-30T08:17:48Z

This report aims to understand the prevalence and use of AI auditing practices in the healthcare sector. By mapping the data supply chain underlying AI technologies, the study aims to unpack i) how AI systems are developed and deployed to achieve healthcare outcomes and, ii) how AI audits are perceived and implemented by key stakeholders in the healthcare ecosystem.

Read our full report here.

The use of artificial intelligence (AI) technologies constitutes a significant development in the Indian healthcare sector, with industry and government actors showing keen interest in designing and deploying these technologies. Even as key stakeholders explore ways to incorporate AI systems into their products and workflows, a growing debate on the accessibility, success, and potential harms of these technologies continues, along with several concerns over their large-scale adoption. A recurring question in India and the world over is whether these technologies serve a wider interest in public health. For example, the discourse on ethical and responsible AI in the context of emerging technologies and their impact on marginalised populations, climate change, and labour practices has been especially contentious.

For the purposes of this study, we define AI in healthcare as the use of artificial intelligence and related technologies to support healthcare research and delivery. The use cases include assisted imaging and diagnosis, disease prediction, robotic surgery, automated patient monitoring, medical chatbots, hospital management, drug discovery, and epidemiology. The emergence of AI auditing mechanisms is an essential development in this context, with several stakeholders ranging from big-tech to smaller startups adopting various checks and balances while developing and deploying their products. While auditing as a practice is neither uniform nor widespread within healthcare or other sectors in India, it is one of the few available mechanisms that can act as guardrails in using AI systems.

Our primary research questions are as follows:

What is the current data supply chain infrastructure for organisations operating in the healthcare ecosystem in India?
What auditing practices, if any, are being followed by technology companies and healthcare institutions?
What best practices can organisations based in India adopt to improve AI auditability?

This was a mixed methods study, comprising a review of available literature in the field, followed by quantitative and qualitative data collection through surveys and in-depth interviews. The findings from the study offer essential insights into the current use of AI in the healthcare sector, the operationalisation of the data supply chain, and policies and practices related to health data sourcing, collection, management, and use. It also discusses ethical and practical challenges related to privacy, data protection and informed consent, and the emerging role of auditing and other related practices in the field. Some of the key learnings related to the data supply chain and auditing include:

Technology companies, medical institutions, and medical practitioners rely on an equal mix of proprietary and open sources of health data and there is significant reliance on datasets from the Global North.
Data quality checks are extant, but they are seen as an additional burden; with the removal of personally identifiable information being a priority during processing.
Collaboration between medical practitioners and AI developers remains limited, and feedback between users and developers of these technologies is limited.
There is a heavy reliance on external vendors to develop AI models, with many models replicated from existing systems in the Global North.
Healthcare professionals are hesitant to integrate AI systems into their workflows, with a significant gap stemming from a lack of training and infrastructure to integrate these systems successfully.
The understanding and application of audits are not uniform across the sector, with many stakeholders prioritising more mainstream and intersectional concepts such as data privacy and security in their scope.

Based on these findings, this report offers a set of recommendations addressed to different stakeholders such as healthcare professionals and institutions, AI developers, technology companies, startups, academia, and civil society groups working in health and social welfare. These include:

Improve data management across the AI data supply chain

Adopt standardised data-sharing policies. This would entail building a standardised policy that adopts an intersectional approach to include all stakeholders and areas where data is collected to ensure their participation in the process. This would also require robust feedback loops and better collaboration between the users, developers, and implementers of the policy (medical professionals and institutions), and technologists working in AI and healthcare.

Emphasise not just data quantity but also data quality. Given that the limited quantity and quality of Indian healthcare datasets present significant challenges, institutions engaged in data collection must consider their interoperability to make them available to diverse stakeholders and ensure their security. This would include recruiting additional support staff for digitisation to ensure accuracy and safety and maintain data quality.

Streamline AI auditing as a form of governance

Standardise the practice of AI auditing. A certain level of standardisation in AI auditing would contribute to the growth and contextualisation of these practices in the Indian healthcare sector. Similarly, it would also aid in decision-making among implementing institutions.

Build organisational knowledge and inter-stakeholder collaboration. It is imperative to build knowledge and capacity among technical experts, healthcare professionals, and auditors on the technical details of the underlying architecture and socioeconomic realities of public health. Hence, collaboration and feedback are essential to enhance model development and AI auditing.

Prioritise transparency and public accountability in auditing standards. Given that most healthcare institutions procure externally developed AI systems, some form of internal or external AI audit would contribute to better public accountability and transparency of these technologies.

Centre public good in India’s AI industrial policy

Adopt focused and transparent approaches to investing in and financing AI projects. An equitable distribution of AI spending and associated benefits is essential to guarantee that these investments and their applications extend beyond private healthcare, and that implementation approaches prioritise the public good. This would involve investing in entire AI life cycles instead of merely focusing on development and promoting transparent public–private partnerships.

Strengthen regulatory checks and balances for AI governance.
While an overarching law to regulate AI technologies may still be under debate, existing regulations may be amended to bring AI within their ambit. Furthermore, all regulations must be informed by stakeholder consultations to guarantee that the process is transparent, addresses the rights and concerns of all the parties involved, and prioritises the public good.

For more details visit https://cis-india.org/internet-governance/blog/ai-for-healthcare-understanding-data-supply-chain-and-auditability-in-india