The Economics of AI-Driven Healthcare: Can Algorithms Make Public Health More Affordable?

theconvergencys
Nov 20, 2025
4 min read

By Leo Wu Nov. 8, 2024

I – Introduction

Public health systems worldwide face a paradox: rising costs and aging populations, yet shrinking workforces and fiscal capacity. The World Health Organization (2025) estimates global healthcare expenditures will surpass $13.3 trillion by 2030, with administrative inefficiency alone consuming nearly 25% of total spending. Against this backdrop, artificial intelligence (AI) is emerging not only as a diagnostic tool but as an economic reform mechanism.

From algorithmic triage systems in hospitals to predictive analytics in population health, AI promises to deliver what decades of bureaucratic reform could not — a measurable reduction in cost without compromising quality. Yet this transformation carries trade-offs: ethical dilemmas, data inequities, and automation risks. This paper examines how AI can restructure public health economics, focusing on efficiency, equity, and long-term fiscal sustainability.

II – Efficiency: Automating the Costs of Care

Healthcare is one of the least digitized sectors globally, but automation is catching up fast. The OECD Health Expenditure Review (2024) found that AI-assisted workflows in diagnostic imaging reduced radiologist workload by 37% while maintaining accuracy above 94%. In the UK’s National Health Service (NHS), automated patient-scheduling algorithms saved £480 million annually by cutting appointment no-shows through predictive modeling (NHS Digital Transformation Report, 2025).

The financial potential is enormous. McKinsey’s Health Productivity Index (2025) projects that widespread AI integration in clinical operations could save $400 billion annually across OECD economies by 2035. These savings come not from replacing doctors but from optimizing administrative bottlenecks: claims processing, lab reporting, and patient logistics — processes that account for up to 30% of hospital budgets.

Still, cost reduction is uneven. Wealthier health systems see faster gains because they already possess digitized datasets and interoperable infrastructure. In contrast, low-income countries risk being left behind, locked in analog systems unable to generate the data AI requires.

III – Equity: The Data Divide and Algorithmic Bias

Economic efficiency cannot justify inequity. AI systems learn from datasets that mirror the inequalities of the societies they serve. A 2024 Journal of Health Economics meta-analysis found that predictive diagnostic models trained primarily on Western populations underperform by 21% in accuracy when applied to African or South Asian cohorts.

This “data divide” translates into fiscal inefficiency — not just moral hazard. Misdiagnosis increases downstream costs such as unnecessary testing or delayed treatment, creating what economists call compounded inefficiency. In the U.S., Centers for Medicare & Medicaid Services (2025) estimate that diagnostic error costs exceed $100 billion per year, a figure likely to rise if biased algorithms scale unchecked.

To correct this, several nations are investing in public health data equity programs. India’s National Digital Health Mission mandates that all AI health tools undergo demographic bias audits. Meanwhile, Kenya’s Open Health Data Project shares anonymized patient data from public hospitals to diversify global training datasets. Such interventions not only democratize innovation but reduce the risk of systemic inefficiency caused by algorithmic exclusion.

IV – Fiscal Sustainability: From Cost Centers to Predictive Governance

AI’s most transformative potential lies in preventive economics — shifting public health from reactive spending to predictive governance. Chronic diseases like diabetes, cardiovascular illness, and cancer account for 74% of global healthcare costs (World Economic Forum, 2025). Predictive analytics can reduce this burden by identifying at-risk populations before disease onset.

For example, Finland’s AI4Health Prevention Network uses machine learning models to analyze lifestyle, environmental, and genomic data. Since implementation, hospital admissions for preventable conditions have dropped 18%, and national healthcare expenditures have stabilized at 9.2% of GDP — compared to the OECD average of 10.7%. Similarly, Singapore’s SmartHealth Initiative (2024) integrates AI-based early screening in primary care, reducing per capita spending by $310 per year.

The fiscal implication is profound: governments can move from budget triage to proactive investment, redirecting resources toward education, climate resilience, or aging support systems. However, these savings require substantial upfront investment. The World Bank projects that AI health infrastructure demands $100–150 billion in initial capital, which low- and middle-income nations cannot finance without concessional loans or technology partnerships.

V – Conclusion

AI is reshaping the economics of public health, but not all revolutions are equitable. The evidence shows that automation reduces waste and boosts productivity, yet entrenched inequalities in data, access, and infrastructure could create a two-tiered health economy — one predictive and efficient, the other reactive and underfunded.

Public policy must therefore evolve alongside technology. Governments should establish AI equity compacts that fund open datasets, enforce algorithmic transparency, and share pre-trained health models across borders. With careful regulation, AI can transform healthcare from a fiscal liability into a sustainable public investment — one that saves not only money, but also lives.

Works Cited (MLA)

World Health Organization Global Health Expenditure Database. WHO, 2025.
OECD Health Expenditure Review 2024. Organisation for Economic Co-operation and Development, 2024.
NHS Digital Transformation Report. National Health Service, UK, 2025.
“AI and Efficiency in Clinical Practice.” Journal of Health Economics, vol. 63, 2024, pp. 211–239.
Centers for Medicare & Medicaid Services Annual Report 2025. U.S. Department of Health and Human Services, 2025.
World Economic Forum Global Health Outlook 2025. World Economic Forum, 2025.
AI4Health Prevention Network Case Report. Ministry of Health, Finland, 2025.
SmartHealth Initiative Report. Ministry of Health, Singapore, 2024.