Waiting lists — the national problem
Chile’s system is designed to treat the sick, not to produce longevity. The waiting list is not a bug: it is a systemic gap. These are the numbers teams will build on.
Waiting hits hardest on lower incomes, older adults and the regions. It is not just a management problem — it is a structural inequity.
Even those who get treated don’t always understand that the goal is not just to be cured: it is to live more years with autonomy. The longevity paradigm is missing.
The institutional map of Chilean health
For your solution to be adoptable you need to know who manages what. The system mixes public and private actors, and there is a concrete value chain where every avoided waiting day translates into measurable savings.
The return vein: COMPIN validates medical leave → compensation funds (like La Araucana) pay the subsidy → SUSESO oversees. Fewer waiting lists → fewer leaves → less spending → more country productivity.
Every avoided waiting day is direct, measurable return — for the patient, the institutions and the country.
Health data: the legal framework
In Chile, health data is sensitive data: the most protected legal category. Your solution must be born compliant with this framework — not adapted later.
Law 21.719 (new data protection) takes full effect on December 1, 2026 — a few months after the Lab. Design for it: your solution must comply before going to production.
| Regulation | Topic | Relevance |
|---|---|---|
| Law 19.628 | Protection of private life | Current personal-data framework until Dec 2026 |
| Law 21.719 | New data protection | In force Dec 1, 2026 — Data Protection Agency + real sanctions |
| Law 20.584 | Patient rights and duties | Confidential clinical record, informed consent |
| Sanitary Code | General health framework | Regulates health professions and actions |
Privacy by design from the prototype: if your demo needs identifiable patient data to work, it is badly designed.
Responsible data at the Lab
The Lab works exclusively with anonymized, aggregated data, curated public sources or synthetic prospecting. Data governance is defined from day one: who owns, who authorizes, who manages.
Starting with anonymized, aggregated data is not a limitation: it is the condition for your solution to scale into the real system.
AI in health, done responsibly
Lab solutions extend the reach of health professionals: they let them serve more people without losing quality. What they do not do — and cannot do — is diagnose or prescribe autonomously.
You are not building an artificial doctor: you are building the bridge so a doctor, a midwife or a physiotherapist can reach 10x more people.
Datasets & sources available
Public sources from the Chilean health system, ready to connect with Claude MCPs, plus the Lab’s curated datasets — anonymized and aggregated — published when applications open.
Claude API credits per participant, Claude Code, Agent SDK and MCP included. Curated datasets and their agreements are announced when applications open.