In a considerably short time, Artificial intelligence (AI) has emerged as a game-changer with immense potential to shape the future course of nations. In the Indian context, making the most of a nascent AI atmosphere depends on the establishment of robust foundations, particularly in the realm of government data collection. Especially after the COVID-19 pandemic, global focus has shifted to the humongous volume of health data being generated across various public and private platforms. India’s G20 health priorities also reflect the need to rigorously track animal-origin (zoonotic) diseases and climate shocks with human and plant overlaps, thus conforming to the One Health approach. This commentary explores the critical need for standardizing government data collection mechanisms and enhancing data quality to pave the way for India's sustainable and impactful AI future.

India’s health data intricacies

The current health data landscape in India is a mosaic of disparate data sources. Digital interventions of varying dimensions stem from more than 40 vertical national health programs. There is the primary healthcare ecosystem, with built-in siloes, including several maternal and child health programs. Similarly, the recently digitized Integrated Health Information Platform is a stand-alone government database of outbreak-prone diseases. There is the Ni-kshay platform for tracking and managing tuberculosis cases, and the SOCH platform for tracking HIV care. India now has a dedicated Centre for One Health, responsible for coordinating the national response to animal, human, and climate risks. These are just a few of the many databases that the government manages, and is trying to bridge together through a common interface. 

Focus on AI-powered solutions

Capitalizing on recent AI advancements, the Government is simultaneously fortifying its future readiness by investing in AI-powered solutions. These solutions assist specialty physicians in diagnosing lung diseases, skin conditions, and eye problems. Another relevant example is eSanjeevani, India’s flagship teleconsultation platform, widely propagated during the COVID-19 pandemic for ensuring health service accessibility. A dedicated AI unit of the Health Ministry is now developing an AI-powered chatbot to assist healthcare providers while providing teleconsultations. However, their task is made slightly cumbersome by the irregular entry of patient data, such as ‘fever’ being indicated as both the diagnosis and the symptom in some records. This is precisely why uninterrupted, HIGH-QUALITY data availability is a prerequisite to formalizing AI solutions.

Need for standardizing large-scale data collection

It is imperative to streamline and standardize the data-collection processes to fully harness the power of AI for informed decision-making. A unified approach to public health data collection can help facilitate interoperability, enabling seamless integration of diverse datasets.

Voluntary and periodic data quality audits by government database owners and managers can build system-level safeguards, that in turn, can enable policymakers to overcome the inherent deployment hesitancy for AI solutions. Moreover, the overall gains of a robust data architecture for each of the core One Health domains are already widely recognized.

Identifying best practices for scale-up

A single-window entry platform for all government-administered national health programs may be a workable solution to aid uniform data collection and reporting. The UT of Dadra and Nagar Haveli has initiated a similar exercise by deploying a platform called ‘e-Aarogya’. This platform enables seamless data flow between multiple government health databases and reduces manual entry of data. The Health Department of Maharashtra is also actively trying to integrate such a solution to enable its front-line health workers. Such initiatives have the potential to generate valuable insights for improving and scaling up workable solutions for the entire country. 

Conclusion

Having standardized data across different sectors, like healthcare and education, can lead to better public services and efficient resource allocation. This will require collaboration between policymakers, technologists, and data experts to create a trustworthy and ethical data ecosystem. By establishing a solid foundation for AI through standardized and large-scale health data collection, India can position itself as a global leader in sustainable AI for health. 

List of MoHFW programmes on the IHIP website