Healthcare affordability is often treated as a pricing problem. Let us reexamine affordable healthcare as a system design problem - with measurement methods/metrics, shared infrastructure and practical adoption pathways.
I am borrowing a "grounded futurism" mindset similar to Dario Amodei's Machines of Loving Grace to make the vision concrete, identify leverage points, acknowledge adoption frictions and build pathways that can learn and adapt to societal needs.
In healthcare, the leverage points are clear and practical: a) protect households from financial shocks, b) control system costs through purchasing and delivery design, and c) build shared digital and data infrastructure so improvements can scale beyond pilots and be extensible.
What is affordable healthcare?
"Affordable" doesn't mean cheap. It means access to needed care without financial hardship. The most useful global yardstick is SDG indicator 3.8.2, revised in 2025 to better capture hardship among poorer households. It tracks the proportion of population with positive out-of-pocket (OOP) health spending exceeding 40% of household discretionary budget (relative to societal poverty line).
Why does affordability look different across countries?
The challenges vary by fiscal capacity, health system maturity, and implementation capability — i.e., ability to coordinate providers, payers, and supply chains. This is why WHO's global digital health strategy emphasizes institutionalizing digital health through an integrated approach of financial, organizational, human and technological resources. This is where affordability can be operationalized via shared infrastructure (identity, registries, exchange standards, claims rails, supply chain visibility, etc.)
What works (transferable design patterns), and why is data the key denominator?
Countries that sustain affordability tend to combine financial protection, cost discipline and organized delivery. Thailand's Universal Coverage Scheme (UCS) pairs coverage with explicit cost controls, including capitation for outpatient care and diagnosis-related groups (DRGs) under the country's budget for inpatient care, and positions its purchaser (NHSO) as an "active" manager of budgets and payments. NHSO's responsibilities include registration of beneficiaries and providers, establishing a claims and reimbursement process and using a standard dataset and APIs for claims flows — i.e., affordability reinforced through systems and not only policy.
India's ABDM (National Health Stack) reflects the same principle via a modern digital public infrastructure (DPI). It is built from Health IDs (ABHA), provider and facility registries (HPR/HFR), and a consent manager enabling consented exchange in a federated architecture, designed to support continuity of care and interoperability across a diverse ecosystem.
These examples imply that you cannot scale affordability without building country/state/region-specific datasets as public utilities, as targeting, purchasing, and delivery of health services (including AI) all depend on them.
The Affordable Healthcare Replication Stack: Systems View (three pillars)
The learnings from those transferable design patterns lend themselves to the systems view below for affordability.
1. Financial protection (prepayment + pooling + subsidies + safety nets) Goal: Reduce household hardship, measured using revised SDG 3.8.2 (2025) and complementary impoverishment measures. Required datasets: Household financial protection dataset (OOP spending and consumption/income) captured via household surveys, Beneficiary & entitlement dataset: Eligibility, enrollment and benefit rules captured as part of beneficiary registration and entitlement management by Thailand's NHSO. AI acceleration: AI can improve eligibility verification, detect anomalous enrollment patterns, and optimize outreach (renewals, maternal/NCD reminders), but only once entitlement datasets are reliable and governance is in place.
2. Cost Discipline + Access (strategic purchasing + primary care-first delivery) Goal: Keep care affordable for the system and accessible for patients by shaping incentives and shifting care upstream. Thailand illustrates how provider payment design (capitation + DRG/budget) can contain costs while scaling coverage. Required datasets: Provider and facility registry - who is licensed, where they operate and what services they offer. ABDM's HPR/HFR are direct analogs of this "registry layer", Utilization and case-mix dataset - outpatient visits, inpatient episodes, DRG groupers, Referral pathway and primary care dataset - catchment areas, referral rules, appointment and follow-up flows. AI acceleration: AI copilots can reduce clinical burden and expand capacity - especially documentation and decision support.
3. Digital Rails for Scale (Health DPI + Claims rails) Goal: Make affordability scalable and auditable by reducing fragmentation, duplication and payment friction. ABDM is a working reference to provide a federated, consent-based exchange with registries and gateway model for interoperable services. Required datasets: Longitudinal health record pointers and metadata that are discoverable and consented references to clinical history, Claims and payment status dataset: Standardized, machine-readable claims for adjudication and auditing enabled by National Health Claims Exchange (NHCX). AI acceleration: AI reduces leakage and delay when claims and registries are machine-readable.
An example/'living lab' archetype in creating datasets - A powerful way to build datasets from the ground up is to start in a region with real operational constraints and build end-to-end connectivity. This is demonstrated in Kuppam, Andhra Pradesh (India) via Tata's Digital Nerve Centre (DiNC) - by digitizing personal medical records, connecting an area hospital with 13 primary health centers (PHC) and 92 village health centers, enabling continuous monitoring, timely diagnosis and virtual consultations. DiNC integrates public health facilities through digital tools and protocols to improve coordination and patient convenience.
The supply chain resiliency on affordability - Affordability is not only financing and care delivery, but also the reliability and cost of diagnostics and supply chains, especially during shocks. C-CAMP's Indigenisation of Diagnostics (InDx) program that was launched to build molecular diagnostics capacity and supply chain networks during COVID, connects indigenous manufacturers, suppliers, service providers and health agencies to improve supply chain visibility and accountability. This can be leveraged as a "Diagnostics & Supply Chain Data rail" when connected to public procurements and primary care diagnostic needs.
A pragmatic roadmap of affordable healthcare for developing economies
Here's a practical sequence that acknowledges adoption frictions and delivers services:
- Adopt revised SDG 3.8.2 (2025) metric and publish baselines/targets for financial protection.
- Establish or strengthen an active purchaser function and implement payment discipline
- Build health DPI early - India's ABDM provides a working reference architecture
- Digitize claims via claims rails (similar to National Health Claims Exchange) to reduce friction
- Use district "living labs" for social datasets, connected PHCs to harden workflows and enable scaling and outreach
- Strengthen diagnostics and supply resiliency with InDx-like marketplaces
- Deploy AI where it delivers value in the safest and most responsible way - tele-triage, imaging, clinician co-pilots, claims, etc.
Affordable healthcare is not achieved by one reform or one model, but a continuous journey when financial protection, cost discipline and digital rails evolve together - and when AI is used to reduce burden and extend scarce expertise, reinforcing responsible policies, controls and effective governance for social good.
Time for action is NOW
If you had to start tomorrow, what would you build first in your state/country and why?
- Entitlement + benefit registry
- Provider/facility registry + service directory
- Digital public infrastructure
- Claims rails
- Diagnostics supply chain visibility
