By  Janese Wong, Public Health Executive, NCSM

Precision public health is gaining momentum in an era of big data and AI—promising tailored solutions for populations, not just individuals. But can it deliver equity, or will it deepen existing divides? In the Precision Public Health Asia 2025 plenary “Digging Deep: Picking Precisely at the Root of the Problem,” three experts—Dr. Murallitharan Munisamy, Managing Director of the National Cancer Society Malaysia, Prof. Jose M. Valderas, Director of the Centre for Research in Health Systems Performance at the National University of Singapore, and Prof. Kent Buse, Prof of Health Policy at Monash University Malaysia, — wrestled with this question, exposing both the potential and pitfalls of data-driven approaches to health equity, especially for lower-middle income communities, as it interacts with social and structural determinants of health.

Singapore’s Experiment: Equity in Practice?

Prof. Valderas opened with a case study: Singapore’s Healthier SG initiative, which is Singapore’s attempt at transforming from reactive sick-care to preventative health-care. This model also aims to reduce inequities by attaching citizens to family physicians for personalized care plans. The model integrates social prescribing—connecting patients with non-medical community services to address root causes of poor health, particularly in social factors. 

The Healthier SG initiative features several key aspects including the mobilisation of existing networks of family physicians and streamlining it such that doctor shopping is discouraged and a 1:1 patient to family doctor relationship is established. Personalised care plans are to be implemented, tailored to individual needs and to facilitate social prescription while fostering community partnerships. This initiative builds on Singapore’s robust foundation of primary healthcare providers while integrating digital technology to innovate monitoring and implementation of different health campaigns.

While this works in the context of Singapore, rethinking this in the context of the Southeast Asian region may prove to be difficult. “This assumes robust primary care systems,” noted Valderas, which is a significant challenge In LMICs, where up to 70% in rural villages may not have access to formal healthcare, such models may exclude the most vulnerable. In addition, while social prescribing improves individual wellbeing, its impact on system-level outcomes (e.g., reducing hospitalizations) remains to be supported.

Singapore’s approach, while innovative, underscores a tension: precision public health risks becoming a privilege, only suitable for well-resourced systems.

The Data Paradox: Who Counts in Precision Public Health?

Data’s role in providing us with tailored solutions and optimising resource allocation in many fields is undeniable in recent years, however, a closer inspection exposes its systematic flaws. Dr. Murallitharan shifted the lens to Malaysia and other lower to middle-income countries (LMIC), where big data often erases the marginalized, creating a paradox for its use in tailoring solutions for specific needs and populations. “Maternal mortality data looks successful—until you disaggregate it and find refugees or stateless communities entirely missing,” he explained. “How can precision public health be used to further work at unraveling issues concerning populations as-risk?”. Being excluded from such data renders their needs invisible to policymakers, further compounding their challenges. 

In addition to such exclusion, there is also the issue with political neglect. Politicians often prioritise “the greatest good for the greatest number”—typically middle-class voters—leaving underserved communities without a voice or influence among the decision-makers. “Everyone makes a moral case but no one makes the financial case about equity – equity is expensive,” Dr Murallitharan stressed,

It then brings us to the question of how we can tackle this systematic flaw. For Dr Murralitharan, “we need to go smaller – precision public health is great as a tool if we focus it at grassroots level”. There is a need to identify and resolve the problems where they exist, then scale. But this perspective demands a radical rethink of how data is collected—and who decides what gets measured.

Power, AI, and the Fight for Democratic Data

Aligned with Dr Murralitharan’s perspective on data injustices, Prof. Kent Buse issued a stark warning: “If you think data can save lives without confronting power—think again.” He critiqued the “techno-utopian” belief that AI and big data alone can solve inequities, highlighting three risks:

1. Data monopolies: “Who owns the data? Who benefits?” Without democratic governance, precision tools become tools of surveillance.
2. Algorithmic bias: AI trained on incomplete datasets, highlighted by Dr Murallitharan described will perpetuate exclusion and further injustices
3. Political resistance: Pro-equity policies threaten entrenched power structures as “the benefits of equity are diffuse, but the costs to those who hold power are concentrated,” Buse noted. “Research that has impact will either challenge the inherent structural inequalities in the world or further entrench them”.

It is also imperative to note that such technological advancements are not neutral nor can they replace and dictate existing political and financial resources. There is always a need for human intervention to guide the use of such technologies in our best interest. 

To end his session, Prof Kent offered us his call to action involving five pillars for equitable precision public health:

• Equitable governance of data where community co-creation is integral to reducing health inequalities
• Democratic data stewardship where data is governed transparently, ethically and with consent while avoiding monopolistic control of data
• Systems change such as the Healthier SG initiative
• Partnerships with social movements to facilitate policy uptake by decision makers and to confront power imbalances
• Narrative shifts from individual blame to systemic accountability.

Precision Without Justice Is Just Prediction

The plenary revealed a central paradox: precision public health cannot achieve equity without first dismantling the systems that create inequity. Singapore’s model, while promising, remains out of reach for many LMICs. Murallitharan’s grassroots approach challenges us to rethink data collection, while Buse’s framework demands we confront power head-on.

This plenary underscored that the problem isn’t in technological advancement but rather the power structures that overlook it. As Prof Kent Bute emphasised, without addressing the imbalances in social and structural determinants, precision public health remains much to be desired. As such, we must ask: precision for whom?