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THE CARIBBEAN & LATIN AMERICA ADRIAN DUNKLEY ↗
Sector

Healthcare

Administrative and educational drafts for qualified clinical and privacy review.

Chancery Lane extension, San Fernando General Hospital, Trinidad and Tobago
Chancery Lane extension, San Fernando General Hospital, Trinidad and Tobago — Kalamazadkhan, CC BY-SA 3.0, via Wikimedia Commons. Date: 2012-07-19. Licence terms. Original and source record. Displayed proportionally without cropping or alteration. Historical documentary context, not evidence of AI use or endorsement.
Practical guide

Clinics, public health, and chronic disease. AI helps where access is the bottleneck.

Start with administrative drafts and clinician-reviewed patient information in an approved system. Do not delegate clinical decisions to a general chatbot.

Where AI fits

Workflows to test.

  • Clinical note summarization

    Draft notes only in an approved clinical system with lawful data handling and clinician verification

  • Patient triage

    Summarise clinician-approved service information; do not let a general chatbot decide urgency or care pathways

  • Medication adherence

    WhatsApp reminders, refill nudges, and adherence coaching in local language

  • Public health surveillance

    Dengue, Zika, and chikungunya pattern detection from clinic intake data

Where AI fails

Failure modes to plan for.

  • Diagnostic overreach

    General chatbots should not diagnose; clinical systems require appropriate validation and accountable licensed professionals

  • Language register

    Patient explanations must be in local language at appropriate health literacy levels

  • PHI residency

    Patient data must respect local rules, especially for cross-border telehealth

Start here

Plan a measured pilot.

  1. 1 Pilot AI summarization with one specialty clinic for 60 days, measure clinician time saved
  2. 2 Use clinician-approved patient information with direct access to staff; do not introduce autonomous triage
  3. 3 Run a monthly governance review with the medical director
Reference

In depth: AI use cases for Healthcare.

AI in Healthcare: Caribbean & Latin America

Before using these prompts

These are proposed workflows, not reports of measured results or guaranteed savings. Country examples are suggestions to investigate, not official rankings or evidence of local deployment. Start with one task, record the existing time, cost and error rate, then compare a reviewed pilot against that baseline.

Use synthetic or anonymised examples. Do not paste personal, customer, patient, pupil or employee records into a public chatbot. Use only an organisation-approved service with an appropriate lawful basis and data safeguards. Ask for dated primary-source links for laws, prices, statistics and local services; independently open and check them. If a source or input is missing, leave it unknown. A generated answer does not establish legal compliance. A responsible person must approve consequential decisions and external communications.

Tool names are examples, not endorsements. Check current vendor terms, pricing, country availability and suitability before use.

These examples are for administrative drafting and clinician-reviewed education. General-purpose chatbots are not clinical authorities: do not use them to diagnose, prescribe, change treatment or decide whether urgent care is needed. Use approved systems only, with appropriate patient-data safeguards. A qualified clinician must check all medical content against current local protocols. In an emergency, contact local emergency services without waiting for AI. WHO guidance on health AI warns about inaccurate outputs and automation bias.

Improving Health Outcomes Across the Region

Created by Adrian Dunkley | maestroailabs.com | ceo@maestrosai.com


Regional Healthcare Context

The Caribbean and Latin America face unique healthcare challenges:

  • High burden of Non-Communicable Diseases (NCDs): diabetes, hypertension, obesity
  • HIV, dengue, zika, chikungunya - tropical disease burden
  • Limited specialist availability in small island states
  • Brain drain of medical professionals
  • Underfunded public health systems
  • Strong private healthcare growing in major cities
  • Compare local service needs and capacity using current health-ministry evidence

Healthcare AI Use Cases

Clinical Decision Support

Prompt: "I am a [doctor/nurse practitioner] in [country].
Patient: [age, sex, weight, height].
Chief complaint: [describe].
Vital signs: BP [X/X], HR [X], Temp [X], SpO2 [X]%.
Relevant history: [chronic conditions, medications].
Current medications: [list].
Lab results (if available): [results].

In the context of common conditions in [country/Caribbean region],
Using only the current clinician-supplied protocol, create a checklist of
missing documentation and questions for the treating clinician. Do not
generate diagnoses, treatment, triage or referral decisions. Use synthetic
data for this example. The clinician must verify every item against the protocol."

Patient Education

Prompt: "Create patient education materials about [condition: Type 2 Diabetes/
Hypertension/HIV/Dengue/Maternal health] for patients in [country].
Patient literacy level: [basic/standard].
Language: [English/Spanish/French/Haitian Creole/Papiamento].
Format: [single page fact sheet/WhatsApp message series/poster].

Include: what the condition is (simple terms),
local risk factors and statistics for [country],
symptoms to watch for, when to seek care,
diet advice using LOCALLY AVAILABLE foods in [country],
lifestyle changes practical for [country] context,
available medications and where to get them in [country],
and local support resources."

Hospital Administration

Prompt: "Create [document type: discharge summary/referral letter/
case notes template; not a triage protocol] for a [type of facility] in [country].
Patient/clinical context: [describe].
Required format: [Ministry of Health standard/regional standard].
Language: [language].
List questions for qualified review against [country's health records regulations].
Keep clinical language at [appropriate level for recipient]."

Telemedicine for Small Island States

Prompt: "Design a telemedicine program for [country/territory]
to connect patients with specialists in [specialty].
Current situation: [patients travel to Trinidad/Barbados/US for care].
Technology available: [smartphone penetration X%, internet quality].
Partner institutions: [list potential specialist partners].
Target conditions: [most common referral diagnoses].
Draft questions for the clinical lead about eligibility and consultation workflow;
do not determine patient eligibility. Outline
technology requirements and alternatives for low-bandwidth,
data privacy compliance framework, billing model,
and a method to measure travel costs without assuming savings."

Public Health Campaigns

Prompt: "Create a public health campaign for [country/region]
targeting [disease/issue: NCDs/HIV/maternal mortality/vaccine hesitancy].
Target population: [demographics].
Key message: [message].
Channels available: [TV/radio/social media/community health workers].
Language: [language, include Creole/Patois variants if needed].
Cultural considerations: [specific to country].
Create: campaign concept, key messages for each channel,
radio script (60 seconds), social media content (5 posts),
community health worker talking points,
and success metrics."

Country-Specific Healthcare AI Priorities

CountryTop AI Healthcare NeedAI Application
JamaicaNCD management, mental healthPatient education, care protocols
T&TNCD burden, dengue surveillanceEpidemic monitoring, clinical support
BarbadosMedical tourism enhancementQuality assurance, patient experience
HaitiEmergency response, maternal healthResource allocation, community health
CubaClinical decision support, researchMedical AI (unique access constraints)
Dominican RepublicRural health accessTelemedicine, community health
Puerto RicoPost-disaster health systemResilience planning, supply chain
MexicoIMSS efficiency, rural accessAdministrative AI, teleconsultation
ColombiaEPS system efficiency, mental health post-conflictClaims processing, counseling support
BrazilSUS optimization, chronic diseaseNational health system AI, preventive care
ArgentinaMental health, infectious diseasePsychiatric support AI, disease surveillance
ChileFONASA/ISAPRE efficiencyInsurance claims, wait time reduction
PeruRural indigenous healthQuechua-language health education, outreach
EcuadorGalápagos remote careTelemedicine for island populations

Healthcare AI Tools

ToolUse CaseNotes
ClaudeDocumentation, patient education, analysisGeneral drafting only; clinician review required
ChatGPTClinical writing, education materialsWidely available
Google Health AIMedical imaging (where available)Research stage
Doximity (US territories)TelemedicinePuerto Rico
Epic/Cerner AIEHR optimizationLarge hospitals
InfermedicaSymptom checkerAPI integration

AI Healthcare Ethics in Our Region

Key considerations:

  1. Always position AI as clinical support, not replacement
  2. Ensure AI models are tested on diverse (including Afro-Caribbean, indigenous) populations
  3. Protect patient data per national laws (Jamaica Data Protection Act, LGPD Brazil, etc.)
  4. Address algorithmic bias in diagnostic AI trained on non-representative data
  5. Ensure AI-generated health content is reviewed by qualified professionals
  6. Consider low-literacy populations and design inclusive interfaces

Created by Adrian Dunkley | maestroailabs.com | ceo@maestrosai.com

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Cite this page

Dunkley, Adrian. “Healthcare.” LAC AI Playbook. https://aiplaybooklac.netlify.app/sectors/healthcare/. Include your access date when citing this changing resource.

Geographic coverage: Latin America and the Caribbean. This describes the subject of the guidance, not the author's location or a service address.

Credit Adrian Dunkley when quoting or adapting the playbook. Cite the original research for its findings. Third-party text, photographs and other materials retain their own rights; citation alone does not grant permission to reproduce them.