Top 10 AI Prompts and Use Cases and in the Healthcare Industry in Iceland

Iceland's COVID response shows why AI matters for healthcare: fast, nationwide RT‑PCR testing, daily telemedicine follow‑ups at Landspítali and a national Contact Tracing Team paired with the Rakning C‑19 app created rich, timely data streams that AI can amplify for surveillance, automated triage and policy modeling; deCODE's population sequencing traced transmission chains in real time, giving AI models genomic resolution to spot clusters and variants.

See the analysis of the “rising role of AI in public health” for Iceland's pandemic response and the deCODE sequencing study for concrete examples of data-driven control efforts (Analysis: rising role of AI in public health (OAEPublish), deCODE sequencing study: early spread of COVID‑19 in Iceland).

Practical workplace AI skills - like prompt writing and using AI tools - help health teams turn those data into safe, explainable decision support; programs such as Nucamp AI Essentials for Work bootcamp registration focus on exactly those applied skills.

Program	Details
AI Essentials for Work	15 Weeks; Courses: AI at Work: Foundations, Writing AI Prompts, Job Based Practical AI Skills; Early bird cost: $3,582; AI Essentials for Work bootcamp syllabus; Register for the AI Essentials for Work bootcamp

“In attempting to carefully map the molecular epidemiology of COVID-19 in Iceland we hope to provide the entire world with data to use in the collective global effort to curb the spread of the disease,” said Kari Stefansson.

Methodology: selections were driven by Iceland's real-world priorities and by measurable signals from local pilots and infrastructure: prompts and use cases were scored for clinical relevance, deployability within Landspítali's workflows, and patient engagement evidence such as that reported in a preliminary program evaluation of a new digital health program for inflammatory bowel disease - where engagement and changes in participants' energy levels were key outcomes (JMIR Formative Research 2023 digital IBD program evaluation).

Practical feasibility also weighed compute and scaling: options that could leverage Nordic resources like LUMI to accelerate model training and lower infrastructure costs were favored (LUMI supercomputer and Icelandic AI compute resources).

Finally, selection emphasized programs that align with funding pathways and clinical priorities - so prompts that map to pilot-to-scale routes such as the Landspítali Science Fund were prioritized, and use cases that support the telemedicine and symptom‑checker evolution in Icelandic care received extra weight (Landspítali Science Fund telemedicine and symptom‑checker funding guide).

The result: a shortlist that balances measurable patient impact, national-scale feasibility, and clear pathways to clinical adoption - think pilot prompts that can move from bedside trial to island‑wide deployment without losing explainability or safety.

Design a telemedicine follow‑up prompt that reflects Landspítali's proven outpatient workflow: notify every confirmed case by phone at diagnosis and then daily or every few days depending on symptoms, record a traffic‑light status (green/yellow/red) in the EMR, and escalate to in‑person evaluation or COVID ambulances only when the red flag appears - this keeps most patients safely at home while preserving hospital capacity, a core Icelandic aim noted in the national review (COVID‑19 in Iceland: the rising role of AI).

Embed outputs from local prognostic tools to triage follow‑up cadence and discharge timing (discharge criteria used in Iceland: 14 days from qPCR diagnosis and at least 7 days symptom‑free) so calls are targeted and predictable (Development of a prognostic model of COVID‑19 severity).

To scale and retrain models on national data without breaking budgets, include options to leverage Nordic compute like LUMI for rapid iteration and secure deployment (LUMI and Icelandic AI resources) - imagine a single EMR flag that saves a hospital bed by routing one well‑timed phone call.

Protocol item	Detail
Call frequency	At diagnosis, then daily or every few days (status‑dependent)
EMR triage	Green / Yellow / Red traffic‑light categorization
Discharge criteria	≥14 days since qPCR diagnosis and ≥7 days symptom‑free
Staffing / remote work	Outpatient staff can work from home with phone and computer

Automated triage prioritization can help Icelandic EDs turn noisy intake moments into consistent, evidence-driven decisions - think a clinician's dashboard that flags a patient whose SpO2 and systolic blood pressure match patterns tied to high acuity while NLP parses the free‑text chief complaint for subtle red flags; a systematic review in BMC Emergency Medicine shows that models combining structured data with triage notes consistently outperform models using only vitals (BMC Emergency Medicine systematic review on ML and NLP for ED triage), and a recent JMIR review cautions that algorithmic scores often err on the side of safety (over‑triage) so clinician oversight and clear escalation rules remain essential (JMIR review on digital check‑in and triage kiosk safety and over‑triage risks); implementation pathways already tested in multi‑site trials and AHRQ‑funded work offer blueprints for embedding clinician‑facing decision support into EHRs with FHIR interoperability and bias assessment, enabling Iceland's compact system to pilot, validate, and scale a triage assistant that reduces variability without replacing the nurse at the bedside - imagine one red alert on a busy shift saving a bed by catching a single silent hypoxic patient early (AHRQ project on ML-based emergency triage clinical decision support).

Key finding	Implication for Icelandic EDs
Top predictors (SpO2, chief complaint, SBP, age)	Prioritize continuous pulse‑ox and structured capture of chief complaints at triage
NLP + structured data	Improves ROC‑AUC (avg ~0.91 vs 0.88) - include free‑text notes in models
Bias & validation concerns	Require prospective validation, class‑imbalance handling, and XAI for clinician trust

Contact‑tracing communication for the Civil Protection Contact Tracing Team should lean into Iceland's proven strength in manual interviewing while using app data and AI to make outreach faster and clearer: scoping work on contact‑tracing app design and implementation highlights the need for transparent consent language, privacy‑focused prompts, and operational integration with human tracers (JMIR mHealth contact‑tracing scoping review (2021)); in practice, Rakning C‑19 reached roughly 38% of the population but officials say its value came when app logs were used to augment - not replace - phone interviews (MIT Technology Review article on Rakning C‑19 contact tracing in Iceland).

Actionable AI prompts should therefore produce concise fusion summaries (app‑derived location clusters + tracer notes), suggested outreach cadence, and short, culturally tuned SMS/call scripts that preserve trust and consent; pairing these prompts with scalable compute strategies like Nordic LUMI resources can keep national‑scale processing practical (LUMI Nordic supercomputer support for Iceland COVID data processing).

The bottom line: surface likely contacts and context for the human tracer - one well‑timed, clearly worded call informed by an app log can be the difference between a stopped chain and a missed link.

“But it's the integration of the two that gives you results. I would say it [Rakning] has proven useful in a few cases, but it wasn't a game changer for us.” - Gestur Pálmason

Genomic surveillance in a compact national system like Iceland needs protocols that are fast, sensitive, and deliberate about sample prep: Oxford Nanopore's rapid metagenomic workflow offers a dual‑arm approach (DNA‑only for bacteria/fungi and a DNA/RNA arm for viruses) that supports multiplexing up to 24 barcodes (practically ~11 samples/run) and flexible 24–72 hour run times to balance throughput and coverage - useful context for analysts planning batch sizes and turn‑around priorities (Oxford Nanopore rapid metagenomic sequencing protocol and wf‑metagenomics workflow).

Key operational takeaways: host depletion is critical (human reads can jump from ~1% to ~96% without depletion - a ~100× loss of pathogen signal), sample normalisation preserves per‑sample coverage, and segmented viruses (e.g., influenza) showed far better recovery (>500 mapped reads and ~50% genome at >20× with the optimized method versus only ~3 reads with the earlier Quick method).

For surveillance reporting and specimen selection, mirror public health WGS criteria - sequence specimens with PCR Ct ≤30 and adequate volume - and integrate secure pipelines that can scale analysis on Nordic compute like LUMI to keep national‑scale processing practical (Public Health Ontario whole genome sequencing guidance for surveillance, Nordic compute resources such as LUMI supporting Icelandic genomic surveillance); these steps turn raw reads into timely, epidemiologically actionable reports rather than noisy data piles.

Parameter	Practical guidance for deCODE
Multiplexing	Up to 24 barcodes (≈11 samples/run when using two barcodes/sample); balance sample count vs coverage
Sensitivity	Viral detection ~10^3–10^4 cp/ml; bacterial ~10^3 cfu/ml; fungal ~10^2 cfu/ml
Host depletion	Essential - reduces human reads from ~96% to ~1%, freeing sequencing capacity
Specimen criteria	Prefer PCR Ct ≤30 and ≥1–1.5 mL residual volume for routine WGS surveillance

Civil Protection communications on vaccination certificates and border policy should be crisp, localised and action‑ready: Iceland accepts EEA/EFTA or WHO‑style vaccination certificates only if they meet strict formatting and content rules (languages accepted include Icelandic, Danish, Norwegian, Swedish, English or French), list full name, date of birth, passport number, vaccine name/manufacturer and batch, dates of vaccination, and issuer details - many certificates now include a QR code for rapid verification so one quick scan can determine whether a traveller avoids extra screening (UNECE Iceland entry rules summary, Iceland digital vaccine certificates official guidance on heilsuvera.is).

Border officers review validity on arrival and may require double screening and quarantine if a certificate is missing or invalid; exemptions also exist for documented prior infection under specified criteria.

For easy public FAQs and sample certificate checklists, link communications to official guidance so travellers and frontline staff know whether an emailed or paper certificate will be accepted (Iceland vaccination certificate requirements and accepted vaccines (Camper.is guide)).

FAQ item	Key detail
Accepted languages	Icelandic, Danish, Norwegian, Swedish, English, French
Required fields	Name, DOB, nationality, passport no., disease (COVID‑19), vaccination dates, issuer, vaccine name/manufacturer/batch
Accepted vaccines	Vaccines with EMA approval (e.g., Pfizer‑BioNTech, Moderna, AstraZeneca) or WHO/Ireland guidance per certificate rules
Invalid certificate	May trigger double testing + 5–6 day quarantine and fines; border guards consult Chief Epidemiologist

An AI‑modeled policy scenario generator lets Icelandic public‑health modelers move from intuition to evidence by comparing concrete what‑if choices - timing of booster drives, age‑targeting, or short‑term NPIs - against long‑range ensembles that already project two periods of increased COVID‑19 activity (a first peak in late August 2025 and a second in January 2026) and weekly hospitalizations close to last winter's ~20,000, per the COVID‑19 Scenario Modeling Hub (COVID‑19 Scenario Modeling Hub projections).

Multi‑model ensembles are also more reliable than single forecasts - median ensembles tend to beat most component models - so a generator built on ensemble outputs provides a robust baseline for decisions (Ensemble forecast performance across Europe (eLife)).

The Hub quantifies policy tradeoffs: vaccinating only high‑risk groups is projected to avert ~90,000 hospitalizations and ~7,000 deaths over the projection window, while vaccinating all ages raises that to ~116,000 hospitalizations and ~9,000 deaths, and even moving a campaign 1.5 months earlier trims deaths by ~3% and hospitalizations by ~2% - numbers that translate abstract curves into staffing and bed‑allocation choices.

Paired with scenario‑planning practices and scalable compute like Nordic LUMI, such a generator can run rapid, transparent policy comparisons for Icelandic planners (Nordic LUMI support for Icelandic healthcare AI).

“what‑if” choices

Metric	Projection / finding (Hub Round 19)
Projected activity peaks	Late August 2025 and January 2026
Weekly hospitalizations (50% PI)	Close to last winter (~20,000 weekly)
Hospitalizations averted (high‑risk vaccination)	~90,000 fewer hospitalizations
Hospitalizations averted (universal vaccination)	~116,000 fewer hospitalizations
Timing benefit (earlier campaign by 1.5 months)	Deaths ≈ −3%; hospitalizations ≈ −2%

An EMR‑to‑summary prompt for hospitalists should turn disparate chart entries into a short, action‑ready ISBAR / I‑PASS style briefing so the recipient can act faster and safer - precisely the handover function researchers identify as critical to improving downstream care (ISBAR clinical handover framework (BMC Medical Education)).

Practical evidence shows that a simple, mandatory EMR transfer summary with core fields (source/reason for admission, problem list, course in unit, investigations, active issues and clear management plan) lifts documentation compliance from ~29.5% to 95.5%, boosts receiving clinicians' satisfaction to 84%, and was associated with a drop in 48‑hour PICU readmissions to zero during the QI window - proof that structure matters (Standardised EMR handover form improved compliance and outcomes (BMJ Open Quality)).

Design the prompt to extract key vitals, meds and outstanding tasks, flag

watch‑for

deterioration signs, require a named owner and contact number, and ask the receiver to synthesize back the plan - mirroring I‑PASS best practices for illness severity, action list and contingency planning (I‑PASS handoff components and protocol (Stanford Scalpel)).

A single clear EMR note that can be auto‑populated and then reviewed before transfer - remember the vivid image of a nurse empowered to hold a bed transfer until the summary is signed - turns busy shift handoffs from risky guesswork into reliable, repeatable care.

Patient‑facing chatbots should be built as a warm, reliable digital front door for Icelandic care: script flows that book and reschedule appointments, push tailored prep instructions, confirm insurance or passport details for border‑related visits, and hand off complex cases to a human with full context - mirroring the 24/7, appointment‑first benefits shown in chatbot pilots and scheduling reviews.

Practical musts for Health System Digital Services Managers include deep EHR/PM integration and write‑back so bookings become single‑step reality (not just a reminder), multilingual prompts for Iceland's accepted languages, and clear escalation rules so empathy and clinician judgment kick in when needed; vendors such as Emitrr AI chatbots for healthcare, highlight HIPAA‑compliant, two‑way booking plus smart reminders that cut no‑shows and free front‑desk time, while conversational‑AI reviews show real gains in after‑hours access and conversion to booked visits - think a midnight SMS that converts a potential no‑show into a same‑day slot rather than a missed opportunity (conversational AI in healthcare reviews and benefits).

Start scripts with simple intent flows (book/reschedule/triage), require explicit consent language, log every handoff to clinicians, and track KPIs (no‑show rate, call‑deflection, booking conversion) so Reykjavíkur clinics can scale a safe, patient‑centered digital receptionist without losing the human touch.

Prescription‑audit alert generators can turn noisy drug‑interaction warnings into pharmacist‑trusted, patient‑specific prompts that cut alert fatigue while catching real risks in Icelandic practice: build prompts that pull EHR data (age, renal function, dose, route, concomitant therapies and recent labs) to suppress irrelevant flags and surface high‑value alerts - an approach shown to reduce irrelevant warnings in the AHRQ Individualized Drug Interaction Alerts project (AHRQ individualized drug interaction alerts project) and echoed by pharmacy leaders who recommend pharmacist‑led customization to hardwire safer workflows (Wolters Kluwer precision drug alerting for pharmacy leaders).

Pair the prompt with an evidence‑backed drug knowledgebase and override‑tracking so pharmacists can iteratively tune thresholds (studies of DDI systems document high override rates and opportunities for contextualized algorithms to lower noise).

Imagine one crisp alert for a true warfarin–NSAID bleeding risk interrupting a dispensing line and preventing an ER trip - practical, scalable, and compatible with Iceland's national compute options for larger audits (Nordic LUMI compute support for Icelandic healthcare AI audits).

Challenge	AI‑prompt / prescription‑audit solution
Alert fatigue / high override rates	Contextualize alerts by dose, route, duration and patient factors to reduce irrelevant warnings
Low clinical relevance	Use evidence‑based KBs and pharmacist review to prioritize actionable DDIs
System tuning	Track top overrides and iteratively adjust thresholds by setting/provider

For PIs at deCODE, the University of Iceland and partner labs drafting an AI genomic/therapeutics proposal, center the plan on an auditable, modular stack that fuses multiomic, clinical and social‑determinant data to speed target discovery, biomarker validation and companion‑diagnostic development; propose using a Decode Health modular AI PaaS to run reproducible biomarker discovery and precision‑cohort selection workflows that are designed to deliver decision‑support outputs up to 18× faster than legacy pipelines (Decode Health modular AI PaaS).

Lay out clear aims (novel target ID, proteomic/RNA biomarker panels, and clinical‑grade risk stratification), required inputs (WGS, proteomics, EMR extracts, SDoH), governance (SOC‑2 / HIPAA controls and partner‑specific data use agreements), and validation milestones (internal cross‑validation, external replication in deCODE population datasets and CLIA/clinical assay bridging).

Budget in scalable compute and storage - cite Nordic options such as LUMI supercomputer for large‑scale model training - and include deliverables tied to adoption: prioritized cohorts for trials, biomarker candidate lists with assay readiness, and an operational decision‑support prototype for clinicians.

Anchor the narrative with deCODE's population‑genomics track record so reviewers see a realistic path from high‑resolution discovery to actionable therapeutics and diagnostics (deCODE genetics population genomics and news).

"Our work [with Decode Health] will create opportunities to fuel critical advances that unlock cutting-edge diagnostics and therapeutics." - Jay G. Wohlgemuth, MD

Iceland's next steps are clear: prioritize pilot-to-scale pathways that blend the telemedicine, contact‑tracing and sequencing strengths documented in the national review with rigorous safeguards for consent, explainability and clinical oversight; anchor pilots to funding routes like the Landspítali Science Fund so promising prompts move from trial to practice, and use Nordic compute to keep models fast and affordable - see how the

OAEPublish article on the rising role of AI in public health

frames telemedicine and tracing as core ingredients and why scaling needs infrastructure such as Nordic compute resource LUMI for healthcare AI infrastructure.

Safeguards should require prospective validation, human-in-the-loop escalation rules and strict data governance (SOC‑2 / HIPAA style controls where relevant), so an automated triage score is a trusted cue rather than a lone decision-maker.

Workforce readiness matters: practical prompt-writing and AI‑at‑work skills help clinicians and admin staff translate models into safer workflows - consider targeted training such as the Nucamp AI Essentials for Work bootcamp (AI at Work: Foundations) to build those operational skills.

Done right, Iceland can turn compact data streams into transparent, equitable AI tools that support faster care without sacrificing privacy or clinician judgment.