For centuries, societies have sought to understand health through the data they could collect. From Roman censuses to parish registers in Florence and from the earliest civil registration systems to large-scale digitized population databases. Today, India is generating an unprecedented volume of health information through its rapidly expanding diagnostics sector, conducting nearly a billion laboratory tests each year. Yet this vast reservoir of clinical data remains largely untapped for public health intelligence and policy making. If systematically aggregated and responsibly governed, diagnostic data could transform how we monitor disease trends, anticipate health system pressures, and design evidence-based policy.

Healthcare management has always been shaped by health information. Today, the focus has shifted toward AI-driven, patient-level interventions and population-level program design. Across the world, policymakers and researchers seek reliable health data, such as biomarkers, anthropometric measures, and genetic histories to understand epidemiological transitions, anticipate health system pressures, and design evidence-based interventions. However, generating such data in many low- and middle-income countries (LMICs) remains technically complex, resource-intensive, and slow.

India’s rapidly expanding diagnostic ecosystem

Meanwhile, a quiet but significant shift is underway in several developing economies: a rapidly expanding diagnostics market, consistent of clinical pathology, imaging, and genetic testing. In India alone, there are an estimated 300,000 laboratories, including national diagnostic chains, regional networks, independent imaging and pathology centers, and embedded laboratories within public and private hospitals. Driven by health awareness, preventive screening culture, and expanding insurance coverage, these networks collectively conduct approximately 900 million tests annually. Preventive health check-ups alone account for roughly 10–12% of total diagnostic volumes.

Yet this data is rarely leveraged for public health planning. When used, it often remains confined to private-sector applications in terms of identifying potential markets for drugs or devices, rather than contributing to broader population health intelligence. The opportunity is substantial. Aggregated and de-identified diagnostic data could provide condition-specific trends, geographic clustering, and early signals of emerging disease burdens. Compared to large-scale surveys, laboratory data offers high-frequency, biomarker-based insights at relatively low marginal cost. Insurance-linked testing further extends reach across socioeconomic strata.

Structural interventions to leverage diagnostic intelligence

What needs to be done?

First, India should establish a national laboratory Management Information System (LMIS) that aggregates standardized, de-identified diagnostic results. Laboratories would be registered and required to report through interoperable digital platforms. India’s experience with centralized digital systems in vaccination and insurance demonstrates that large-scale integration is administratively feasible.

Second, data pooling requires incentives and standardization. Health data is increasingly treated as a commercial asset. In global markets, individual health records can cost up to $250 USD in black markets. In India, hospital chains, fertility centers, and pharmacy networks already leverage patient data for targeted marketing, sometimes with limited transparency. Without clear policy incentives and enforceable consent frameworks, voluntary data convergence for public-good purposes will remain unlikely.

Third, emerging technologies, wearables and point-of-care diagnostics offer additional longitudinal data streams. If standardized and anonymized, integration of these data sources could enable panel datasets that track health trajectories over time, moving from static snapshots to dynamic population monitoring.

Potential risks and threats

There are also risks, though, that need to be managed.

First and foremost is the data privacy issue. With large-scale aggregation, we are seeing increasing exposure to cybersecurity threats, including ransomware attacks, which are becoming exceedingly costly. A national diagnostic intelligence system would therefore require strong encryption, strict protocols, and enforceable data governance standards.

Second, population-level results and interpretation are often rooted in statistical prudence. On the other hand, diagnostic data comes from different sources and reflects varying representation, quality standards, and referral biases. Without appropriate adjustment through robust epidemiological and biostatistical frameworks, population-level inferences may be misleading.

Finally, India’s diagnostic landscape remains fragmented, with a considerable share of small laboratories and pathology centers operating on non-digitized infrastructure. These are also the laboratories most frequently used by marginalized populations. Excluding them would result in significant biases in population-level information. Thus, actively reaching out to them through user-friendly mobile modules and a common reporting system can help address this gap.

In sum, India does not lack health data. Rather, it lacks a structured mechanism to convert diagnostic volume into public health intelligence. With appropriate safeguards, standardization, and analytical rigor, the diagnostics ecosystem could evolve from a clinical service industry into a foundational pillar of population health management.

Perhaps it is fortunate that scholars like Ibn Sina  practiced medicine before the age of fitness trackers and algorithmic marketing. Otherwise, he might have spent more time filtering promotional messages than writing the Canon of Medicine.