How AI Is Helping Healthcare Companies in Chile Cut Costs and Improve Efficiency

Chile's healthcare system is already experimenting with AI to cut costs and speed up care - but the payoff depends on smart choices, not just shiny tools. World Privacy Forum's deep dive shows agencies like SUSESO are using machine learning to triage a deluge of claims (roughly 200,000 last year and about 20,000 waiting now), while wrestling with procurement rules that traditionally prize price over fairness and transparency (World Privacy Forum report on AI governance in Chile).

At the same time, real operational wins are already happening: AI assistants such as Patricia Eniax cut no-shows and slashed outbound calls at Chilean centers, freeing staff to see more patients (Patricia Eniax AI assistant reduces no-shows in Chile).

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“success” might be defined another way; in situations affecting people's wellbeing and livelihoods, use of a well-designed and assessed model in support of speedier-yet-thoughtful human decisions can constitute success.

Chile's cost-and-efficiency squeeze is visible at every level: the Ministry of Health's Department of Digital Health pushed telemedicine and electronic records during the pandemic to tame rising costs and replace paper workflows, yet providers still face an

administrative burden

when processing telemedicine reimbursements and many locations lack telemedicine-ready infrastructure (Chile digital health market overview - telemedicine & electronic records); at the same time private insurers (ISAPREs) are under acute financial pressure after a Supreme Court ruling that drives a roughly USD 1.4 billion restitution, with total liabilities to facilities nearing USD 629.2 million and aggregate losses reported in recent years (e.g., ~USD 158.1 million), a fiscal shock that threatens payment flows and bargaining power with hospitals (Financial crisis in Chile private healthcare insurers - USD 1.4 billion restitution).

Operational fixes - cloud records, teleconsultation platforms that move clinics off WhatsApp, and AI tools that triage imaging to prioritize urgent X‑rays - promise real savings and faster care, but meaningful gains hinge on clinician training, better coverage outside business hours, addressing older patients' tech barriers, and urgent upgrades to privacy and cybersecurity practices (AI imaging diagnostic support use cases in Chile healthcare to prioritize urgent cases), a combination that can turn cost pressure into operational momentum.

Quick, pragmatic AI wins for Chilean healthcare firms are already within reach: automated scheduling and reminder systems can cut no-shows by roughly 30% and deflect high call volumes, while AI scribes and EHR automation reclaim clinician hours and reduce post-visit charting (see how mobile EHRs and AI-powered documentation speed workflows in practice NextGen review of mobile EHR and AI-powered documentation); Document AI and claims‑automation tools can accelerate the work of agencies like SUSESO that handled ~200,000 claims last year and still face ~20,000 pending decisions, easing a costly administrative bottleneck (World Privacy Forum analysis of SUSESO's AI governance); imaging‑triage models that highlight critical regions on X‑rays and CTs help radiologists prioritize emergency reads (AI imaging diagnostic support for X‑rays and CTs).

These quick wins - automation of scheduling, documentation, claims routing and image prioritization - often require integrations, defined review gates and modest pilot timelines, but they can shave clinical hours and chip away at backlogs that directly affect patient care.

“success” might be defined another way; in situations affecting people's wellbeing and livelihoods, use of a well-designed and assessed model in support of speedier-yet-thoughtful human decisions can constitute success.

Clinical and advanced AI is already reshaping diagnostics and personalization in ways that Chilean health systems can pragmatically harness: AI-enhanced image reconstruction and real‑time segmentation speed up CT/MRI reads and can even segment 104 anatomical structures in a whole‑body CT, turning bulky raw scans into actionable insights for faster diagnoses (NVIDIA AI-powered medical imaging solutions); when those image models are paired with platforms that surface clinical context from EHRs and FHIR interfaces, radiologists get the background they need to make safer, quicker decisions and reduce backlogs - a practical interoperability route described by InterSystems for next‑gen enterprise imaging (InterSystems next‑gen enterprise imaging and clinical context platform).

Beyond scans, AI is pushing toward true personalization - combining imaging, genomics and clinical data - and even accelerating early drug discovery workflows by narrowing candidate compounds and trial cohorts, a trend captured in recent industry analyses of AI use cases in healthcare (AI-driven drug discovery and personalized medicine use cases).

The payoff for Chile: clearer triage, shorter turnaround times, and more targeted therapies that translate technological promise into tangible patient and budgetary relief.

“The InterSystems platform simplified much of the work involved in integrating disparate systems, connecting to EMRs, using DICOM and HL7, managing the data, supporting workflows, and enabling event detection and alerting. It enabled us to focus our development effort on incorporating our radiology process and workflow expertise into the solution.” - John Mazur, President, Charlotte Radiology

Chile's clearest on‑the‑ground case study is SUSESO, where roughly 200,000 medical claims crossed desks last year and about 20,000 still await decisions - cases that can determine whether a worker receives wages during medical leave - so the agency's pilot use of machine‑learning to triage and speed claims is as much about social protection as it is about efficiency (see the detailed World Privacy Forum report on SUSESO AI governance in Chile).

SUSESO's paired projects - a gradient boosting model to optimize claims flow and an independent audit of a mental‑health claims classifier - expose a classic tradeoff: procurement templates pushed price and competition higher in the scorecard, while GobLab's involvement injected bias, transparency and explainability checks into vendor evaluations; the result is a pragmatic lesson for any Chilean payer or lab thinking about automation.

Predictive analytics can deliver measurable wins - faster routing, fewer denials, earlier fraud flags - that improve cash flow and patient outcomes when paired with human review and solid procurement rules (predictive analytics for insurance claims research), and Chile's broader policy work on efficiency provides a promising policy backdrop for scaling these pilots into sustained savings (lessons from Chilean health policy (PubMed)).

“success” might be defined another way; in situations affecting people's wellbeing and livelihoods, use of a well‑designed and assessed model in support of speedier‑yet‑thoughtful human decisions can constitute success.

Procurement reform in Chile has reshaped the governance landscape that health purchasers and vendors must navigate: Law No. 21634 made Supplier Registry registration and disclosure of ultimate beneficial owners mandatory (effective December 2024), paired BO checks with pre‑filled SII data to reduce friction, and fed ownership records into ChileCompra's Observatory so automated alerts flag possible conflicts for human review - changes that helped drive a 69% drop in monthly detected conflict‑of‑interest cases and generated 208 protected whistleblower reports that bolster oversight (OpenOwnership report on Chile's beneficial ownership procurement reforms).

For healthcare procurement this matters on the ground: tighter vendor transparency, mandatory registration when suppliers transact on Mercado Público, and stronger escalation rules mean AI or automation pilots (claims triage, image‑reading contracts, cloud services) must sit behind clear review gates, vendor audits and human decision nodes - especially in far‑flung places like Rapa Nui, 3,500 km from the mainland, where family ties complicate sourcing and require documented exemptions (ChileCompra Observatory and procurement resources).

The result is a practical governance stack: machine flags plus mandated human oversight and capacity‑building, not “black‑box” procurement.

Metric (2024/Mar 2025)	Value
Value on Mercado Público (2024)	USD 16,542,305,230
Suppliers on Mercado Público	110,000
Companies reporting BO (Mar 2025)	39,039 (≈59% of 66,000)
Beneficial owners registered	>100,000

Scaling AI from pilots to national impact in Chile now has concrete momentum: a Corfo‑backed supercomputing call offers co‑funding of up to US$7 million per project (covering up to 80% of costs) to build the advanced compute needed to train and serve medical AI models and other sectoral solutions (Corfo supercomputing co‑funding (US$7M)); that seed funding underpins two state‑backed centers announced in 2025 and a National Data Center Plan aiming to host AI campuses in regions with renewable power.

One flagship effort, SCAI‑Lab, brings 65 universities and institutions into a shared lab model so hospitals, startups and researchers can access scale without each buying their own cluster (SCAI‑Lab consortium of 65 institutions), and it explicitly allocates more than half of initial funds to high‑performance machines.

Those investments build on Chile's existing NLHPC capacity - whose Leftraru cluster was described as equivalent to roughly 25,000 notebooks working together - and recent hardware upgrades that sharply improved performance per watt, a practical advantage when running energy‑hungry imaging and genomics workloads for healthcare (NLHPC - national HPC capacity).

Together, co‑funding, shared labs and a growing data‑center strategy create the infrastructure and fiscal pathways for hospitals, labs and med‑tech firms to scale AI pilots into cost‑saving, patient‑facing services without outsourcing core data sovereignty.

Metric	Value / Source
Corfo co‑funding per project	Up to US$7 million (up to 80%) - InvestChile
State investment (two AI centers)	14 billion pesos total - Inria / Government announcements
SCAI‑Lab consortium size	65 institutions - CMM / Universidad de Chile
Existing NLHPC scale	Leftraru ≈ 25,000 notebooks equivalent - NLHPC

Preparing Chile's health workforce for practical AI adoption means marrying quick wins with deliberate training: start by familiarizing teams with readily available generative tools and target high‑value, time‑consuming tasks - data entry, document summarization, scheduling and EHR automation - to capture early productivity gains; Stanford's deep dive and related research show roughly 4.7 million Chilean workers (nearly half the workforce) could accelerate more than 30% of their tasks, while public‑sector roles alone have ~31% of tasks ripe for acceleration (Stanford Impact Labs analysis of generative AI impact in Chile, Stanford GSB summary on AI productivity in common jobs).

Practical steps for health teams: run short, instrumented pilots that pair AI suggestions with mandatory human review; re-skill administrative staff and clinical scribes toward QA, EHR normalization and prompt engineering roles; build localized tooling rather than only importing black boxes; and invest in irreplaceable human skills - communication, clinical judgment and empathy - so clinicians can spend more time on patients instead of paperwork.

For Chilean clinics and labs, that mix of pilots, role pivots and targeted training turns the theoretical productivity boost into measurable time savings and better patient-facing care (how transcriptionists and scribes can adapt to AI in Chile's healthcare).

Metric	Value / Source
Workers who could accelerate >30% of tasks	≈4.7 million (nearly 50%) - Stanford Impact Labs
Public‑sector tasks accelerable	≈31% - Stanford Impact Labs
Teachers' administrative tasks suitable for automation	65–75% - Stanford Impact Labs
SME tasks optimizable	≈44% - Stanford Impact Labs
Jobs with ≥30% tasks accelerable (alternate estimate)	≈80% of workers in those job categories - Stanford GSB

AI can shave real costs in Chilean care, but those savings only help patients if payment rules, regulation and business models are redesigned to channel gains outward - not just shrink provider headcount or add new line‑items that get passed back to households.

The Paragon Institute warns that pre‑set payment rates and legacy contracts often block the transmission of provider‑side efficiencies to consumers, and points to autonomous

self‑service AI (low marginal cost at scale)

and targeted IP/regulatory reforms as high‑leverage levers to preserve downstream savings (Paragon Institute report on lowering health care costs through AI).

Practical Chilean policy steps include piloting shared‑savings or value‑based contracts that explicitly split verified productivity gains with patients and payers, requiring empirical clinical and economic validation before new fees are approved, and protecting trust through governance, explainability and workflow integration - barriers strongly flagged by the JMIR scoping review on adoption (JMIR Human Factors scoping review of AI adoption barriers).

On the business side, vendors and clinics can demonstrate tangible patient benefit (for example, some AI platforms report double‑digit annual cost reductions in clinic operations) and tie pricing to measured outcomes, so the memorable test becomes simple: if AI cuts paperwork and wait times but leaves the patient bill unchanged, the model failed; if lower operating costs lead to lower patient costs or better access, policy and market incentives worked (Caliper case study on AI cost savings in clinics).

Policy lever	What it fixes
Shared‑savings/value contracts	Aligns incentives so provider productivity gains lower patient cost burden
Evidence‑based reimbursement gates	Prevents new AI costs from being passed to patients without clinical/economic validation
Governance + explainability rules	Builds trust and supports adoption across workflows

The clear next step for Chilean healthcare companies is to convert promising pilots into governed, patient‑facing services by pairing risk‑aware engineering with real workforce training: follow Chile's new, risk‑based AI rules that require documentation, testing and meaningful human oversight (Chile AI regulation framework and high‑risk requirements), bake review gates into procurement after the SUSESO experience so models support - not replace - life‑changing decisions (World Privacy Forum analysis of SUSESO AI governance pilots), and run short, instrumented pilots that measure clinical and economic outcomes before scaling.

Practical moves include a regulatory gap analysis, mandatory human‑in‑the‑loop checkpoints for diagnostic or claims tools, robust documentation and bias testing, and reskilling administrative staff into QA and prompt‑engineering roles; even a single well‑audited model can turn a mountain of paperwork into a prioritized handful of urgent cases.

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