Top 10 AI Prompts and Use Cases and in the Healthcare Industry in Brunei Darussalam

Brunei's BruHealth story makes a clear case for why AI matters in a small, connected health system: what began as a COVID tracker has grown into an AI-enhanced platform that 63% of residents use weekly, delivers personalized preventive recommendations in BruHealth 5.0 and ties lifestyle programmes - like the BN on the Move challenge that drew ~49,000 participants - to measurable behaviour change (World Economic Forum).

Local deployments pair clinical sources such as BruHIMS with the MOH Intelligence Hub for predictive analytics and surge planning, while industry reviews show AI can boost diagnostic accuracy, telemedicine and operational efficiency across the nation (BytePlus).

Policymakers must balance these gains with equity, multilingual access and strong governance; building practical AI skills for healthcare teams is urgent - programmes like the Nucamp AI Essentials for Work bootcamp teach prompt-writing and workplace AI use to help bridge that capacity gap.

Bootcamp	Length	Early bird cost	Registration
AI Essentials for Work	15 Weeks	$3,582	Register for Nucamp AI Essentials for Work bootcamp

“In the highly regulated healthcare sector, characterized by long gestation periods and low returns on investment for innovations, cross-learning and collaboration with various stakeholders are critical to eliminating duplicative efforts, avoiding mistakes made by others, and shortening the research and translation process to optimize financial returns and bring necessary innovations to market faster. We look forward to engaging more ecosystem partners to advance AI innovations in healthcare for greater impact,” said Mr Chua Ming Jie, CEO of EVYD Technology and Co-Director of the A*STAR-EVYD Joint Lab.

The methodology behind selecting and structuring the top 10 AI prompts and use cases was deliberately pragmatic: start with what evidence shows works in clinical settings, then test for local fit and risk - anchoring choices to a comprehensive clinical review (see Revolutionizing healthcare for the evidence base) and to country-specific use cases that describe how AI is already being applied in Brunei (AI use cases in Brunei - BytePlus); next, screen for feasibility in low‑resource settings and rehabilitation services informed by practice guidance, and finally, apply a cost‑risk filter that flags GenAI risks like hallucinations and expense (GenAI tradeoffs in customs and logistics - GovInsider).

Prompts were then grouped by function - prevention, triage, imaging support, capacity planning, surveillance, clinician literature synthesis, multilingual patient education, digital therapeutics, risk assessment, and deployment/costing - to create compact, testable pilot recipes that prioritise measurable ROI and workforce reskilling; imagine swapping a paper intake form for a calm, colour-coded dashboard that tells clinicians who needs attention first.

This pipeline balances scientific rigor with on-the-ground practicality for Brunei's health system.

BruHealth's move from a COVID tracker to an AI-enhanced preventive platform shows how personalised routines can turn data into better habits: BruHealth 5.0 analyses activity, sleep, diet and stress to deliver evolving, individualised recommendations and ties them to tangible rewards and challenges that boost long‑term engagement - witness the BN on the Move steps challenge that drew ~49,000 participants who collectively logged about 1 billion steps in eight days.

The platform also bundles appointment booking, real‑time lab and imaging results and gamified incentives, helping users act on prevention rather than wait for disease; early reporting from the World Economic Forum profile of BruHealth app notes higher engagement from AI-driven, tailored messaging while the BruHealth app listing on Google Play Store details features like personalised routines, interactive challenges and enhanced rewards on Google Play.

These gains come with caveats - equitable access, digital literacy and algorithmic oversight remain essential to ensure AI recommendations are transparent, clinically supervised and inclusive for older or less digitally connected populations.

Metric	Figure
Weekly active users	63%
Unique lab-results users	566,403
Imaging results viewers	335,320
BN on the Move participants	~49,000 (≈1 billion steps in 8 days)

AI-powered triage and booking can turn BruHealth's digital front door into a calm, 24/7 navigator that asks symptom questions, recommends urgency and books the right appointment without tying up reception lines - think of transforming the traditional “8am phone crush” into an always‑on self‑service lane.

Evidence from AI chatbots shows they automate scheduling, symptom assessment, reminders and multilingual support while syncing with EHRs for accurate patient records (AI chatbots for medical appointment booking and scheduling), and autonomous triage platforms demonstrate major drops in wait times and high rates of automated bookings when clinical pathways are validated (Rapid Health smart triage platform for primary care).

For Brunei, combining symptom‑aware chatbots with a clinically‑validated triage engine in BruHealth booking and telemedicine could prioritise urgent cases, reduce no‑shows, and free clinicians for complex care - while safeguarding privacy, keeping clinicians in the loop, and monitoring for over‑ or under‑triage through ongoing audits and human oversight.

“What we've done is help the nurses confidently identify a larger group of those low risk patients,” said Scott Levin, describing how AI-supported triage improves patient flow.

Radiology AI can be a practical force‑multiplier for Brunei's imaging services: clinical work at scale shows generative tools drafting reports, flagging life‑threatening findings in milliseconds and boosting report productivity - Northwestern's multicentre study found average gains of 15.5% with some readers reaching 40% (and follow‑on work showing even larger efficiency rises) - benefits that translate directly to faster ED decisions and shorter inpatient stays when every minute matters.

AI now automates triage, segmentation, measurements and structured‑report pre‑fills so radiologists focus on complex cases while routine reads are reliably handled, a pattern explored in RamSoft's overview of radiology automation; integrated solutions that plug into PACS/RIS and respect local protocols help avoid workflow friction and reduce burnout.

For Brunei, starting with a small, validated pilot - linking an AI triage engine into existing hospital PACS, auditing outputs and upskilling clinical data stewards - offers measurable ROI and safer, faster care without handing over final decision‑making to a black box.

Think of it as giving radiologists back time to explain results to families rather than ticking boxes on a report.

Metric	Source / Figure
Average report efficiency boost	Northwestern - 15.5% (some up to 40%)
Follow‑on CT efficiency potential	Northwestern - up to 80% (unpublished follow‑on)
Turnaround example (chest X‑ray)	RamSoft - from 11.2 days to 2.7 days
Radiologist burnout	RamSoft - affects >45% of radiologists

“It doubled our efficiency” - Dr. Samir Abboud, co‑author on the Northwestern Medicine study.

Predictive analytics in Brunei's MOH Intelligence Hub can turn fragmented signals - admissions, ICU use, PPE burn rates - into clearer, actionable plans, but the promise comes with clear caveats from recent evaluations: hospital planners have tools that forecast 30‑day patient inflow, length‑of‑stay and bottlenecks (see the Neurealm Hospital Capacity Planner for an example of these features), yet independent validation studies warn that models vary widely in reliability.

A multicentre validation found some hospital forecasting approaches achieving high point accuracy (IEEE cited ~90% forecast accuracy for daily ED presentations, admissions and discharges in a studied site), while a broader review of COVID‑era demand models reported typical peak‑magnitude errors around 50% and up to five‑fold differences across gown‑demand calculators - so standardisation, transparency and rigorous local validation are essential before operational use (see the BMC analysis).

For Brunei, a practical path is to ensemble validated time‑series and clinical models inside the MOH Intelligence Hub, run small pilots that collect ground‑truth usage data, and prioritise reskilling staff to audit outputs - because even a single mis‑timed peak can mean beds and supplies are either dangerously short or unnecessarily tied up.

Metric / Finding	Source / Figure
Hospital forecasting example accuracy	IEEE - ≈90% for ED/admissions/discharges
Typical peak‑magnitude error	BMC - ~50% relative error
Demand calculator variability (gowns)	BMC - up to 5× difference between models

Brunei's Epidemic Intelligence and Response Unit can gain a real time advantage by adopting syndromic surveillance: systems that monitor pre‑diagnostic signals - emergency‑department and outpatient symptoms, absenteeism, even OTC sales - to spot unusual illness patterns before lab confirmations accumulate, essentially “seeing the coughs in a school” days earlier than traditional reporting.

The CDC's overview explains how syndromic approaches use automated, near‑real‑time data and aberration‑detection methods to flag early clusters while emphasising that these systems augment, not replace, physician reporting (CDC overview of syndromic surveillance methods).

Practical implementation guidance - from onboarding and technical connections to validation and production data quality - has been documented by public health programmes such as Minnesota's MDH, which highlights the benefits of integrated feeds and stepwise onboarding for sustainable surveillance (Minnesota Department of Health syndromic reporting implementation guidance).

A systematic review of emergency‑department syndromic systems in BMC reinforces the value of these feeds for early warning and situational awareness while flagging variability in methods and the need for standardised signal‑detection and response protocols (BMC systematic review of emergency-department syndromic surveillance systems).

For Brunei, the pragmatic path is phased pilots that integrate multiple data sources, validate syndrome definitions locally, and train rapid‑response teams so alerts translate into timely investigations rather than noise.

Automated literature synthesis could help Brunei's clinicians and medical publishers cut through the noise by turning a mountain of PDFs into a two‑minute clinical brief: large language models can process and synthesise vast biomedical literature, assist with summarising evidence for guideline updates, reduce clinical note transcription and speed manuscript drafting (see Databricks guide to generative AI in healthcare for practical use cases and pilots Databricks guide to generative AI in healthcare: practical use cases and implementation tips).

Trust and uptake hinge on familiar factors identified in recent evidence - transparency, clinician training, usability, local validation and ethical safeguards - which the JMIR systematic review maps into eight actionable themes for designing trusted AI clinical tools (JMIR 2025 systematic review on trusted AI clinical tools).

For Brunei, the sensible path is a start‑small pilot that pairs an open, auditable LLM with a curated local knowledge base, clear governance and targeted reskilling so editors and clinicians can validate outputs; done well, the result is less time wrestling with literature and more time explaining treatment options to patients.

See local guidance on pilot‑first approaches and workforce retraining to align expectations and measurable ROI (Nucamp AI Essentials for Work bootcamp syllabus: pilot-first AI projects and workforce retraining).

Clear, culturally‑tailored patient education is the linchpin of safe AI adoption in Brunei: multilingual materials and digital‑literacy support ensure a discharge summary or BruHealth notification isn't reduced to a confusing page but becomes an actionable plan the patient or caregiver understands in Brunei Malay or Mandarin.

Local capacity exists - courts and government services already offer certified translation and certification (including Chinese↔Malay work) through the Brunei Judiciary Translation Unit - translation and certification services (Brunei Judiciary Translation Unit - translation and certification services).

Private providers complement that scale with Brunei‑specialist services and on‑demand medical interpreters for appointments and outreach (Applied Lingo Brunei translation and medical interpretation services), while commercial platforms make certified Malay translations accessible at low per‑page rates (Languex certified Malay translation services - from $24.50/page).

A pragmatic pilot pairs these human translators with MTPE workflows, community workshops and simple UX fixes - so a family member who only reads Brunei Malay or Mandarin can follow medication steps without guessing, turning confusing medical text into confidence at home.

Provider	Languages / Services	Cost / Note
Judiciary Translation Unit	Malay ↔ English, Chinese ↔ Malay; certified/court translations	Translation $20/page; Certification $20/page; combined $40/page
Applied Lingo	Brunei Malay, Mandarin, English; medical translation, interpreters	Certified & notarized translations; interpretation services
Languex	Professional & certified Malay translations; portal & localization	Certified Malay translation from $24.50/page

Brunei's approach to gamified digital therapeutics turns public health nudges into a national pastime: the BruPoints mall system rewards preventive actions (screenings, medication adherence, step goals) with real-world perks, while challenges like BN on the Move and the Oyen Challenge use friendly competition to sustain behaviour change - BN on the Move drew ~49,000 participants who logged about 1 billion steps in eight days, a vivid example of scale and momentum (see the World Economic Forum profile of BruHealth digital health program: World Economic Forum BruHealth profile).

Design matters: blending personalised AI-driven routines from BruHealth 5.0 with simple UX, multilingual support and local merchant incentives makes healthy choices easier to maintain, and evidence from a recent BMC scoping review on gamified digital interventions for mental health and health behaviour suggests gamified digital interventions can effectively promote mental‑health and health behaviour goals in adults.

For Brunei, the pragmatic path is pilot-first bundles that couple gamified tasks with clinician oversight, community digital‑literacy support and measurable rewards so points translate into lasting habits - not just one-off spikes in engagement.

Metric	Figure / Note
Weekly active users (BruHealth)	63% (World Economic Forum)
BN on the Move participation	~49,000 participants (~1 billion steps in 8 days)
Gamified features	BruPoints mall, Oyen Challenge, BN on the Move (BruHealth 5.0)

“True health equity begins when everyone can understand their own health code.”

A practical risk assessment for AI in Brunei's health system starts by mapping harms to real people and rules: run privacy‑impact and bias audits on datasets that feed BruHealth and Bru‑HIMS. BruHealth's own policy notes:

our algorithms run on this information to provide personalized risk assessment and recommendation

check model explainability against Brunei's voluntary AI Guide principles (transparency, fairness, security and data governance), and treat psychiatric and other sensitive records as high‑risk because misuse can cause stigma, discrimination and erosion of patient trust as local work has warned.

Align technical controls and vendor contracts with the forthcoming PDPO expectations - mandatory breach reporting, clearer transfer rules and stronger enforcement - and build human‑in‑the‑loop safeguards so clinicians validate high‑impact outputs.

Practical steps: run automated privacy and bias scans, document model lineage, require Data Protection Officers and DPIAs where needed, limit retention and transfers, and pilot alerting thresholds before production; done well, this turns compliance from a checklist into an operational guardrail that protects patients and preserves public trust.

Item	Key detail / source
Brunei AI Guide principles	Brunei voluntary AI guidelines – transparency, fairness, security, and data governance (US-ASEAN)
PDPO (anticipated)	Brunei PDPO anticipated mandatory breach notification, DPO guidance, and penalties (DLA Piper)
Operational note (BruHealth)	BruHealth privacy policy – algorithms power personalized risk assessment in the app

Deploying LLMs in Brunei's hospitals means turning token math into an operational plan: pick the smallest model that meets clinical safety needs, budget for token-based API costs and the hidden infrastructure and people costs, and start with a tightly scoped pilot that hooks into hospital IT and the MOH Intelligence Hub for validation and auditing.

BytePlus ModelArk supports private or managed cloud deployments and token billing, so planners can model expenses using the simple formula from the BytePlus LLM Pricing Guide (Total Cost = input tokens × input price + output tokens × output price) and run realistic examples - one published scenario estimated roughly $350/month for 50k interactions at 400/600 token averages - before scaling.

For sensitive or high‑throughput workloads, on‑premise options (DeepSeek R1) require a very different budget: a rack of ten H100 GPUs can push hardware caps into the $100k–$250k range while a CPU‑heavy prototype can be built for ~$6k–$7k, so compare cloud token costs, predictable provisioned throughput, and long‑term TCO before committing.

Practical cost controls for Brunei: pilot‑first deployment, prompt and retrieval optimisation, caching of frequent responses, phased MCP-style integration, and explicit funding for MLOps, clinician validation and digital‑literacy training so the system saves clinician hours rather than creating surprise bills; imagine a validated triage bot that shaves minutes off each ED decision rather than an unchecked cost centre.

For detailed deployment models and hardware tradeoffs, review BytePlus' ModelArk and DeepSeek notes.

Item	Representative figure / note
Example token-based pilot cost	$350/month (50k requests; 400 input / 600 output tokens) - BytePlus LLM Pricing Guide
DeepSeek R1 on-prem GPU hardware	~$100,000–$250,000 (≈ten H100 GPUs) - BytePlus DeepSeek R1 Deployment Cost
DeepSeek R1 CPU prototype	~$6,000–$7,000 (large RAM, lower throughput)
MCP / integration (mid-market)	Annual: ~$20k–$100k (hosting, consulting, integrations) - phased implementation advised

Brunei's pragmatic path to safe, useful AI in health is straightforward: start small, govern tightly, and build local skills. Begin with tightly scoped pilots that prioritise measurable ROI and local validation - early pilots reveal technical gaps and operational risks faster than big‑bang rollouts (see the case for Start‑Small AI Pilot Projects for Brunei Healthcare) - then lock those pilots to Brunei's seven guiding principles of transparency, explainability, fairness, security and data governance in the Brunei Voluntary AI Guidelines for Responsible AI.

Parallel investment in skilled roles - clinical data managers, MLOps engineers and clinician validators - and short practical courses will keep systems auditable and useful; a focused training option is the 15‑week Nucamp AI Essentials for Work (15‑Week Bootcamp), which teaches prompt writing, practical pilots and workplace AI skills so teams can safely turn validated pilots into routine improvements rather than speculative cost centres.

Bootcamp	Length	Early bird cost	Registration
AI Essentials for Work	15 Weeks	$3,582	Register for Nucamp AI Essentials for Work Bootcamp (15 Weeks)