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Bank Pernyataan Masalah

Bank Masalah Inovasi Nasional merupakan pusat utama yang menghimpunkan cabaran nasional yang nyata dan disahkan daripada kementerian, agensi, dan syarikat berkaitan kerajaan. Setiap pernyataan masalah menonjolkan bidang utama transformasi nasional dan terbuka untuk kerjasama bersama syarikat permulaan, penyelidik, dan rakan teknologi melalui Inisiatif Inovasi Kerajaan (GII).

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Active problems: 50

Open calls: 52

Total applicants: 1,247

Cara Ia Berfungsi

1. Pilih Masalah

Semak pernyataan masalah yang telah disahkan daripada kementerian dan GLC. Tapis mengikut sektor, status, atau agensi untuk mencari peluang yang sesuai dengan kepakaran anda.

2. Mohon

Hantar cadangan anda melalui borang permohonan kami. Sertakan ringkasan penyelesaian, pemboleh teknologi, dan slaid pembentangan (PDF sahaja).

3. Semakan

Permohonan anda akan disemak oleh pasukan GII dan pihak berkepentingan yang berkaitan. Inovator yang terpilih akan dihubungi untuk pembangunan perintis dan pelancaran nasional.

Nota: Semua permohonan tertakluk kepada pengesahan oleh pasukan pentadbir GII. Kredit dan peluang perintis diberikan berdasarkan kebolehlaksanaan penyelesaian dan keselarasan dengan matlamat inovasi nasional.

Tutup

Tapis Mengikut

Subkategori

Real Time Flood Prediction and Warning System

Ditutup

Kerajaan

Perumahan dan Kerajaan Tempatan

31 startups applied

Tarikh Tutup :31/03/2026

Lihat Butiran

Mohon Sekarang

Real Time Flood Prediction and Warning System

Perumahan dan Kerajaan Tempatan

Konteks & Kepentingan

Floods are one of Malaysia’s most frequent and destructive natural disasters, causing heavy human, social, and economic losses. In 2024, flood related damages reached RM 933.4 million, up from RM 755.4 million in 2023 (dosm.gov.my, The Star). KPKT has allocated RM 219.6 million for drainage upgrades, retention pond maintenance, and local mitigation projects to reduce flash floods in urban areas (Selangor Journal, The Malaysian Reserve). Despite this, national flood forecasting and warning infrastructure has underperformed: Audit reports on the National Flood Forecasting and Warning Programme Phase 1 (2015–2022) found forecast accuracy of only 5.6%. Out of 83 warnings reviewed, most were issued less than two days before flood events. Many rainfall and river level telemetry stations lacked proper maintenance after warranty expiry, with months of missing data. Urbanisation, aging drainage systems, non integrated development, and shifting rainfall patterns further worsen flood risks (Selangor Journal). The result is that Malaysia still records nearly 1,000 flood incidents a year, affecting thousands of citizens (Astro Awani, Malay Mail).

Pernyataan Masalah

How might we build a nationwide, real-time, high-accuracy flood prediction & warning system that integrates meteorological, hydrological, urban infrastructure & drainage data, IoT sensing, and citizen reporting — so that KPKT, local authorities, and citizens receive early, reliable alerts & action-able information to reduce flood damage and improve response time, especially in urban and riverine flood-prone areas?

Garis Asas (Keadaan Semasa)

The National Flood Forecasting & Warning Programme (PRAB/DID) Phase 1 had forecast accuracy ≈ 5.6%.
Out of 83 flood warnings in the audit, only 32 were issued two or more days ahead; the rest were late (less than 2 days warning).
Many flood warning stations (204 in number) lacked maintenance after warranty expiry; some telemetry data (from rainfall / river‐level sensors) was missing for months.
Urban flash flood mitigation works are underway (drainage upgrades, retention ponds, PISMA master-plan adoption) but predicted weather changes and extreme rainfall episodes still overwhelm existing systems.
The number of flood incidents remains high (982–1,000+ per year), with large numbers of people affected.

Hasil Yang Diingini (Keadaan Masa Depan)

Out of 83 flood warnings in the audit, only 32 were issued two or more days ahead; the rest were late (less than 2 days warning).
Many flood warning stations (204 in number) lacked maintenance after warranty expiry; some telemetry data (from rainfall / river‐level sensors) was missing for months.
Reduced number of people needing evacuation in urban floods due to earlier warning & mitigation.
Local authorities’ response time (preparing drainage, evacuation, power cuts etc.) improved by ≥40% compared to past flood events. Increase public trust in warning system (measured via surveys) and improved usage/compliance of warnings.

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AI-Driven National “Sick Projects” Tracking and Intervention Platform

Ditutup

Kerajaan

Digital

31 startups applied

Tarikh Tutup :31/03/2026

Lihat Butiran

Mohon Sekarang

AI-Driven National “Sick Projects” Tracking and Intervention Platform

Digital

Konteks & Kepentingan

Malaysia faces recurring issues of delayed and over-budget (“sick”) public projects across sectors. Official federal definitions classify projects as “sick/ailing” when they are more than 2 months or 20% behind schedule, a standard applied across federal development projects. The Auditor-General’s Report (LKAN) 3/2024 shows that under the 12th Malaysia Plan snapshot, 202 projects (≈5.93% of 3,404) were ailing and 107 (3.14%) were behind schedule. This demonstrates that early risk detection at national scale is both measurable and impactful. While ICU-JPM’s MyProjek serves as the federal monitoring backbone, today’s ecosystem is fragmented: ministries run their own project systems, MOF manages disbursement reporting separately, and most analytics remain descriptive rather than predictive. Momentum exists for change. Kementerian Digital and the National AI Office (NAIO), aligned with MyDIGITAL, provide both the mandate and policy environment to institutionalise AI-driven oversight for public project delivery.

Pernyataan Masalah

How might we build a real time, AI-driven national platform that unifies project, finance, and ground-truth data to predict which projects will turn “sick”, trigger timely interventions, and transparently track recovery — improving delivery, saving public funds, and building citizen trust?

Garis Asas (Keadaan Semasa)

Definition in use: “Sick/ailing” when physical progress is >2 months or >20% behind the contract schedule (variations appear by sector, but this is the federal baseline).
Scale of the issue: 202 ailing projects (5.93% of 3,404) in the 12MP snapshot reported with LKAN 3/2024; 107 behind schedule.
Fragmented monitoring: MyProjek exists as the federal monitoring backbone, yet ministries and MOF operate separate reporting/disbursement views, slowing early detection and cross-agency action.
Public impact visible in sectors: e.g., housing task force tracked 347 “sick” private housing projects (Mar 2025) showing the scale and the need for proactive escalation and recovery playbooks (illustrative of monitoring methods that can inspire federal practice).

Hasil Yang Diingini (Keadaan Masa Depan)

Scale of the issue: 202 ailing projects (5.93% of 3,404) in the 12MP snapshot reported with LKAN 3/2024; 107 behind schedule.
Fragmented monitoring: MyProjek exists as the federal monitoring backbone, yet ministries and MOF operate separate reporting/disbursement views, slowing early detection and cross-agency action.
Transparency: publish quarterly project-health stats (non-sensitive), aligned to MyDIGITAL’s public-sector openness goals.

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AI-Enhanced Automated Traffic Violation Enforcement

Ditutup

Kerajaan

Pengangkutan

31 startups applied

Tarikh Tutup :31/03/2026

Lihat Butiran

Mohon Sekarang

AI-Enhanced Automated Traffic Violation Enforcement

Pengangkutan

Konteks & Kepentingan

Automated Enforcement Systems (AES/AWAS) have long served as Malaysia’s primary mechanism for improving road safety by detecting speeding and red-light violations. Recent technological upgrades are transitioning legacy systems to AI-enabled, Automatic Number Plate Recognition (ANPR) and video analytics, improving detection accuracy and coverage. At the same time, pilots in AI traffic intelligence using 5G connectivity and neural-network analysis in urban areas demonstrate Malaysia’s readiness for real-time, intelligent enforcement and dynamic traffic management. However, legacy fragmentation, inconsistent detection performance, and data-handling concerns continue to undermine efficiency and public trust. With rising traffic volumes and road-safety priorities under the Ministry of Transport (MoT), an integrated, AI-driven enforcement system can improve accuracy, speed, and transparency while ensuring compliance with Malaysia’s Personal Data Protection Act (PDPA) and ethical AI standards.

Pernyataan Masalah

How might the Ministry of Transport implement an AI-enhanced automated traffic-violation enforcement system that accurately detects and issues citations for speeding, red-light running, illegal turns, unsafe lane changes, and HOV/bus-lane violations in near real-time — while ensuring legal defensibility, data privacy (PDPA compliance), transparency, and minimal false positives for citizens and enforcement agencies?

Garis Asas (Keadaan Semasa)

The Automated Enforcement System (AES) has been in operation since 2012 and covers speed & red-light enforcement on federal roads and expressways, with a mix of fixed, mobile and traffic-light cameras. This establishes precedent and existing camera infrastructure.
JPJ/MoT have been upgrading legacy cameras (e.g., replacing Redflex with ANPR-capable devices such as Ekin Spotter), improving plate capture but exposing vendor lifecycle and maintenance vulnerabilities.
New pilots for AI traffic intelligence and neural-network based insights are active in urban pilots (e.g., Petaling Jaya) using telco 5G connectivity and private partners, showing appetite for AI + connectivity integration beyond traditional AES.
Recent AWAS/AES upgrade rollouts and trials sometimes face delays and operational snags — implying procurement, integration and coordination challenges.
Malaysia’s PDPA and recent data governance discussions (and emerging AI ethics frameworks) require clear data handling, retention, breach reporting and algorithmic transparency for any system processing personal vehicle data.

Hasil Yang Diingini (Keadaan Masa Depan)

JPJ/MoT have been upgrading legacy cameras (e.g., replacing Redflex with ANPR-capable devices such as Ekin Spotter), improving plate capture but exposing vendor lifecycle and maintenance vulnerabilities.
New pilots for AI traffic intelligence and neural-network based insights are active in urban pilots (e.g., Petaling Jaya) using telco 5G connectivity and private partners, showing appetite for AI + connectivity integration beyond traditional AES.
Public trust metrics: 75% on transparency & fairness surveys.
Operational: reduce targeted violation types (e.g., speeding in school zones) by ≥30% in 12 months after deployment.

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Intelligent Traffic Signal Optimisation

Ditutup

Kerajaan

Pengangkutan

31 startups applied

Tarikh Tutup :31/03/2026

Lihat Butiran

Mohon Sekarang

Intelligent Traffic Signal Optimisation

Pengangkutan

Konteks & Kepentingan

Kuala Lumpur and other major Malaysian cities experience chronic traffic congestion, especially during peak hours. The TomTom Traffic Index consistently ranks Kuala Lumpur among highly congested global cities, with slower average travel speeds and long commute times during peak periods. Malaysia already operates the Integrated Transport Information System (ITIS) in the Klang Valley and is expanding its smart city and AI traffic management initiatives, including DBKL’s digital twin project and 5G-enabled AI pilots in Petaling Jaya. Despite this progress, most traffic signals still operate on fixed-time cycles or limited coordination, failing to respond dynamically to changing traffic demand. Deploying an intelligent, AI-driven adaptive signal system that coordinates entire corridors based on real-time data can reduce delays, improve safety, and deliver citizen-visible results without major infrastructure expansion — directly advancing national goals under the National Transport Policy and Intelligent Transport Systems (ITS) Blueprints.

Pernyataan Masalah

How might we deploy a standards-based, AI-driven adaptive traffic-signal system that senses real-time demand and coordinates signal timing across corridors — to reduce congestion, shorten travel times, prioritise buses and pedestrians, and improve safety, while ensuring data privacy, interoperability, and integration with legacy controllers?

Garis Asas (Keadaan Semasa)

Congestion burden: KL’s traffic index shows materially slower peak travel times vs off-peak; TomTom publishes live and historical KPIs for KL (travel time, congestion, speed).
Existing platforms: ITIS has long provided traffic monitoring/management in Klang Valley (ATMS/ATIS), but many signals still run fixed-time/limited coordination.
Momentum for AI: DBKL announced AI-powered traffic management and digital twin work; Petaling Jaya launched a 5G + AI traffic intelligence pilot (neural networks for real-time insights to guide light management).
Policy backdrop: National Transport Policy and ITS blueprints emphasise ITS/smart signals as modernization priorities.

Hasil Yang Diingini (Keadaan Masa Depan)

Existing platforms: ITIS has long provided traffic monitoring/management in Klang Valley (ATMS/ATIS), but many signals still run fixed-time/limited coordination.
Momentum for AI: DBKL announced AI-powered traffic management and digital twin work; Petaling Jaya launched a 5G + AI traffic intelligence pilot (neural networks for real-time insights to guide light management).
Crash surrogates (red-light entries, harsh braking) ↓ 10–15%.
Citizen sentiment: ≥75% positive feedback in targeted areas.

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National Port Waters Pollution Detection & Alerting via Satellite Imaging

Ditutup

Kerajaan

Pengangkutan

31 startups applied

Tarikh Tutup :31/03/2026

Lihat Butiran

Mohon Sekarang

National Port Waters Pollution Detection & Alerting via Satellite Imaging

Pengangkutan

Konteks & Kepentingan

Malaysia’s busiest port zones — including Port Klang, Pasir Gudang/Tanjung Langsat, Port of Tanjung Pelepas, Penang, Kuantan, and Bintulu — lie along dense regional shipping corridors where spills and illegal discharges can rapidly threaten fisheries, tourism, and coastal livelihoods. Recent incidents near major terminals — such as the Port Klang palm oil spill (July 2024), Johor TLPT oil spill (April 2025), and the Singapore Pasir Panjang spill (June 2024) whose slicks reached Johor’s coastline — highlight the urgent need for faster detection and coordinated response. Modern satellite sensors such as Sentinel-1 SAR (for oil slicks) and Sentinel-2 multispectral imagery (for turbidity and sediment plumes) offer proven, wide-area maritime surveillance capabilities that can complement physical patrols and CCTV systems. Leveraging these technologies through an integrated, near-real-time national service would enhance situational awareness, support rapid response, and strengthen environmental enforcement capacity.

Pernyataan Masalah

How might we deploy a national, near-real-time satellite imaging and alerting system fused with AIS and port data to automatically detect, classify, and alert on oil or chemical slicks and turbidity plumes within Malaysian port waters — so that DOE, port authorities, and MMEA can respond faster, trace sources, and minimise environmental and economic damage?

Garis Asas (Keadaan Semasa)

Incidents happen near ports: Oil/palm-oil slick detected and cleaned at Port Klang (Jul 2024); Johor TLPT investigating an oil spill (Apr 2025); Singapore Jun 2024 spill spread regionally with reports of oil on Johor beaches. Response often relies on on-scene reports and booms after slicks are visible.
Mandate & standards exist: DOE (NRECC) oversees marine water under Environmental Quality Act 1974 and maintains marine monitoring programs, but satellite fusion is not yet institutionalised nationwide for port enforcement.
Space tech is mature: Sentinel-1 SAR is widely used for oil-spill surveillance; Sentinel-2 can quantify turbidity/sediment (useful for dredging runoff/berth operations).
Operational readiness: Port Klang Authority runs oil-spill response training; coordination frameworks exist but detection latency remains a gap.

Hasil Yang Diingini (Keadaan Masa Depan)

Mandate & standards exist: DOE (NRECC) oversees marine water under Environmental Quality Act 1974 and maintains marine monitoring programs, but satellite fusion is not yet institutionalised nationwide for port enforcement.
Space tech is mature: Sentinel-1 SAR is widely used for oil-spill surveillance; Sentinel-2 can quantify turbidity/sediment (useful for dredging runoff/berth operations).
Transparency: quarterly public dashboard of incidents, response times, and outcomes.

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Pest Prediction and Control: Securing Crop Health with Data-Driven Early Intervention

Ditutup

Kerajaan

Pertanian dan Keselamatan Makanan

31 startups applied

Tarikh Tutup :31/03/2026

Lihat Butiran

Mohon Sekarang

Pest Prediction and Control: Securing Crop Health with Data-Driven Early Intervention

Pertanian dan Keselamatan Makanan

Konteks & Kepentingan

Malaysia’s agricultural sector continues to face significant losses due to pest and disease outbreaks affecting key crops such as rice and oil palm. Bacterial Leaf Blight in rice, caused by Xanthomonas oryzae pv. oryzae, is among the most destructive diseases, with a severe outbreak in Sekinchan, Selangor in 2016 affecting over 80 percent of planted areas and resulting in yield losses of up to 70 percent. Bacterial panicle blast caused by Burkholderia glumae has also been reported to reduce rice yields by up to 50 percent in major granary areas, based on findings by MARDI. In the oil palm sector, bagworm infestations, particularly Metisa plana, are recognised as one of the most damaging pest threats in Malaysia. Moderate infestations involving 10 to 50 percent leaf damage have been associated with yield reductions of 43 to 47 percent. Over 10,000 hectares in Johor and Perak have been affected, with national annual economic losses exceeding RM100 million. Historical surveillance records show infestations spanning 63,955 hectares across 69 estates in Peninsular Malaysia, highlighting the widespread and recurring nature of the problem.

Pernyataan Masalah

Current pest and disease management practices in Malaysia are largely reactive, relying on manual field inspections and delayed reporting after visible damage has already occurred. Limited early warning capability restricts timely intervention, leading to widespread crop damage, substantial yield losses, and increased reliance on broad spectrum chemical pesticides. The absence of predictive, data driven tools hampers the ability of authorities and farmers to anticipate outbreaks and apply targeted integrated pest management measures at an early stage.

Garis Asas (Keadaan Semasa)

Rice BLB infections have caused up to 70% yield losses, with outbreaks covering more than 80% of planted fields in Sekinchan
Bacterial panicle blast has caused yield reductions of approximately 50% in multiple Malaysian granaries.
Oil palm bagworm outbreaks cause 43–47% yield losses with 10–50% foliage defoliation-inducing crop decline.
Economic losses exceed RM 100 million annually, with some estates experiencing up to 40% decline over two years.
Historical surveillance data recorded infestations across 63,955 ha of plantation land, highlighting scale and frequency of outbreaks.

Hasil Yang Diingini (Keadaan Masa Depan)

Bacterial panicle blast has caused yield reductions of approximately 50% in multiple Malaysian granaries.
Oil palm bagworm outbreaks cause 43–47% yield losses with 10–50% foliage defoliation-inducing crop decline.
Improve average yield for key crops (rice, oil palm, vegetables) by ≥ 15%.
Achieve ≥ 70% adoption rate among participating farmers in pilot regions.

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Farm Operation & Coordination for Consistent Crop Quality and Yield

Ditutup

Kerajaan

Pertanian dan Keselamatan Makanan

31 startups applied

Tarikh Tutup :31/03/2026

Lihat Butiran

Mohon Sekarang

Farm Operation & Coordination for Consistent Crop Quality and Yield

Pertanian dan Keselamatan Makanan

Konteks & Kepentingan

Malaysia’s average paddy yield (recent official series) is around 4.0 tonnes/ha (depends on region and irrigation), with variability between granary and non-granary areas. The government’s SMART Large-Scale Paddy (SMART SBB) programme is explicitly targeting much higher yields — the programme aims to expand Sekinchan-style smart paddy cultivation and has cited targets of ~7 t/ha in programme communications. The Auditor-General’s 2022 report (reported in press) found that almost one in four paddy farmers earned less than RM600/month, linking low incomes to programme shortfalls and productivity issues. National agricultural statistics show the importance and scale of paddy and other crops in Malaysia’s crop portfolio (paddy ~0.5 million ha; oil palm much larger at ~5.8 million ha), underscoring why improving farm operations has national impact.

Pernyataan Masalah

How might we build a coordinated farm-operations system (sensors, automation, AI decision support and shared operational workflows) that enforces consistent control of irrigation, nutrient delivery and soil conditions — so farmers achieve consistent, higher-quality yields aligned with SMART programme targets?

Garis Asas (Keadaan Semasa)

Yield gap vs target: national/average paddy yields (≈4.0 t/ha) remain materially below SMART SBB targets (~7 t/ha).
Uneven performance across granaries: some granary areas (MADA/IADA) perform better (multiple reports show 5+ t/ha locally), but many non-granary areas lag — illustrating inconsistent parameter control.
Low farmer incomes tied to productivity shortfalls: Auditor-General / press coverage shows ~25% of paddy farmers earn below RM600/month — a signal that current farm operations and coordination aren’t delivering expected returns.
Limited precision-agriculture uptake: academic and government reviews show adoption of precision farming is growing but remains limited by cost, knowledge and infrastructure barriers. (MARDI / research literature on precision agriculture in Malaysia).

Hasil Yang Diingini (Keadaan Masa Depan)

Uneven performance across granaries: some granary areas (MADA/IADA) perform better (multiple reports show 5+ t/ha locally), but many non-granary areas lag — illustrating inconsistent parameter control.
Low farmer incomes tied to productivity shortfalls: Auditor-General / press coverage shows ~25% of paddy farmers earn below RM600/month — a signal that current farm operations and coordination aren’t delivering expected returns.
Operational coverage: integrate farm-level dashboards for MOA monitoring across selected granary pilots (target: dashboards covering 70–100% of pilot hectares).

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Aqua Water Quality: Real-Time Monitoring & Automation to Prevent Fish Mortality

Ditutup

Kerajaan

Pertanian dan Keselamatan Makanan

31 startups applied

Tarikh Tutup :31/03/2026

Lihat Butiran

Mohon Sekarang

Aqua Water Quality: Real-Time Monitoring & Automation to Prevent Fish Mortality

Pertanian dan Keselamatan Makanan

Konteks & Kepentingan

Malaysia’s aquaculture industry produced 499,556 tonnes of fish in 2022, valued at RM3.8 billion, and contributed around 17% of the national fish supply, according to the Department of Fisheries Malaysia (DOF) Annual Fisheries Statistics 2022. Freshwater species such as tilapia, catfish, and patin, and marine species such as seabass and grouper, dominate Malaysia’s aquaculture sector. However, many farms are operated by small to medium-scale farmers who still rely on manual water-quality testing methods, such as handheld kits and laboratory sampling. Key parameters affecting fish survival include dissolved oxygen (DO), pH, ammonia, turbidity, and temperature. Studies from Universiti Kuala Lumpur (UniKL) highlight that fluctuations in these parameters, especially overnight DO drops, can cause mass fish mortality if not detected early. Manual monitoring is time-consuming and infrequent, with most farmers measuring water quality once daily or weekly. This limits their ability to respond to rapid environmental changes. Research published in the Bulletin of Electrical Engineering and Informatics (2022) confirms that manual testing delays early detection, especially during critical hours at night when DO levels fall sharply. When these fluctuations go unnoticed, they can cause catastrophic losses. Studies in the Journal of Applied Sciences, Engineering and Technology (2024) reported that fish kill events can wipe out 30–50% of total pond biomass within hours.

Pernyataan Masalah

How might we develop a real-time, IoT-based water-quality monitoring and automation system that integrates predictive analytics and automated corrective responses (such as aeration or water exchange) to detect early water-quality fluctuations, reduce fish mortality, and improve aquaculture productivity for Malaysian farmers?

Garis Asas (Keadaan Semasa)

Currently, most aquaculture farms in Malaysia depend on manual water-quality testing, performed once a day or a few times per week. This approach is slow and cannot detect sudden overnight changes in oxygen or ammonia levels. As a result, farmers only notice problems when fish are already stressed or dead, causing significant economic losses.
The Department of Fisheries Malaysia lists over 8,300 registered aquaculture farms, yet only a small fraction use digital sensors or automated monitoring systems. Most lack integrated dashboards or centralized oversight.
Past incidents show that poor water-quality management can lead to 30–50% mortality, especially when DO levels drop below 5 mg/L or ammonia levels spike above 0.02 mg/L. This highlights the urgent need for real-time monitoring and automatic alerts.

Hasil Yang Diingini (Keadaan Masa Depan)

The Department of Fisheries Malaysia lists over 8,300 registered aquaculture farms, yet only a small fraction use digital sensors or automated monitoring systems. Most lack integrated dashboards or centralized oversight.
Past incidents show that poor water-quality management can lead to 30–50% mortality, especially when DO levels drop below 5 mg/L or ammonia levels spike above 0.02 mg/L. This highlights the urgent need for real-time monitoring and automatic alerts.
Increase average yield by 10 – 20 % through timely corrective actions.
Adopt IoT monitoring in ≥ 70 % of pilot farms within two years.

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National Bed Management and Access Block Reduction System

Ditutup

Kerajaan

Kesihatan

31 startups applied

Tarikh Tutup :31/03/2026

Lihat Butiran

Mohon Sekarang

National Bed Management and Access Block Reduction System

Kesihatan

Konteks & Kepentingan

Public hospitals in Malaysia face persistent emergency department overcrowding and access block (patients kept in ED because no inpatient beds are available). This creates prolonged waiting times, ambulance diversions, and delayed admissions with direct consequences on clinical outcomes, mortality risks, and public trust in the health system. National Capacity Pressure: MOH data shows many hospitals operating at or above 100% occupancy at peak times, creating bottlenecks across the care pathway (MIH Megatrends Conference, Oct 2024, CodeBlue). Emergency Overcrowding: High ED arrivals, compounded by low public awareness of what constitutes an emergency, continue to strain frontline staff (NST Online). Discharge Inefficiencies: Slow discharge processes and inadequate post discharge coordination prolong inpatient stays, reducing bed turnover (ResearchGate audits). Geographic Imbalances: Bed to population ratios vary significantly by state (e.g., Melaka 2.21 vs national average 2.01 per 1,000, Bernama), further limiting equitable access. Despite MOH’s live hospital bed utilisation dashboard (data.moh.gov.my), capacity pressures remain acute.

Pernyataan Masalah

How might we build a national bed management and access block reduction system that provides MOH, hospital operations teams, EMS, and patients with real time, actionable visibility of bed availability and discharge pipelines to reduce ED access block, ambulance diversion, and inpatient admission delays while safeguarding clinical safety and privacy?

Garis Asas (Keadaan Semasa)

MOH provides a hospital bed-utilisation dashboard (public reporting) but capacity pressure remains acute at many hospitals; national snapshot data is openly published.
Senior MOH officials have warned that many hospitals are operating beyond nominal capacity — citing that a large share of hospitals have reported bed-occupancy ratios at or above 100% at times (speech at MIH Megatrends conference, Oct 25, 2024)
Emergency departments face routine overcrowding driven by high arrivals and limited inpatient bed turnover; low public awareness of what constitutes an emergency also contributes to ED load.
Inefficient/slow discharge processes and delayed post-discharge coordination prolong length-of-stay (reducing bed turnover), documented in audits and tertiary-hospital studies.
Bed-to-population ratios vary by state (example: Melaka reported ~2.21 beds/1,000 vs national average ~2.01/1,000), indicating geographic imbalances in capacity.

Hasil Yang Diingini (Keadaan Masa Depan)

Senior MOH officials have warned that many hospitals are operating beyond nominal capacity — citing that a large share of hospitals have reported bed-occupancy ratios at or above 100% at times (speech at MIH Megatrends conference, Oct 25, 2024)
Emergency departments face routine overcrowding driven by high arrivals and limited inpatient bed turnover; low public awareness of what constitutes an emergency also contributes to ED load.
Increase effective bed turnover rate so that hospital bed occupancy is kept within safe operational thresholds (e.g., avoid sustained >100% occupancy).
Improve patient satisfaction for ED/admission flow to >85% for assisted admissions.

Tutup

Mohon

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Real-Time Healthcare Capacity & Patient Flow Intelligence System

Ditutup

Kerajaan

Kesihatan

31 startups applied

Tarikh Tutup :31/03/2026

Lihat Butiran

Mohon Sekarang

Real-Time Healthcare Capacity & Patient Flow Intelligence System

Kesihatan

Konteks & Kepentingan

Malaysia’s public hospitals face recurring capacity pressure, emergency department crowding, and inefficient patient flow that delay care, increase patient risk, and reduce system resilience during surges. The Ministry of Health publishes daily hospital bed utilisation data showing the availability and strain of critical and non critical beds, but visibility alone has not eliminated access block and throughput challenges. National Data: MOH’s daily hospital reporting highlights utilisation rates, yet many hospitals continue to report occupancy levels above 80% and at times exceed 100% (CodeBlue). Geographic Imbalances: Public datasets reveal uneven bed distribution across states, creating regional disparities in access (data.gov.my). Clinical Risk: Studies show that ED overcrowding and access block correlate with worse outcomes, including higher mortality and increased in-ED cardiac arrests, threatening time sensitive care (PMC). Innovation Feasibility: Local pilots using IoT and BLE based real time patient tracking have shown measurable improvements in patient turnaround time and operational visibility (hefjournal.org). The challenge is not just visibility of capacity but enabling actionable, real time intelligence that can coordinate discharges, transfers, and ambulance utilisation.

Pernyataan Masalah

How might we build a national, standards based real time capacity and patient flow intelligence system that integrates hospital telemetry, operational workflows, and ambulance status, and leverages analytics and lightweight sensing, so that MOH and hospital teams can proactively manage capacity, reduce ED access block, and safely shorten non clinical delays to admission and discharge?

Garis Asas (Keadaan Semasa)

MOH publishes a Hospital Bed Utilisation dashboard with daily bed usage for critical and non-critical care, indicating ongoing attention to capacity metrics but persistent operational pressure.
Public datasets show hospital bed counts by state and hospital type, revealing uneven geographic distribution of beds that contributes to regional capacity imbalances.
Media and official reporting have documented hospitals operating at high utilisation (many hospitals reporting ≥80% occupancy at times) and specific hospitals exceeding 100% utilisation during surges. This correlates with ambulance diversions and access-block in high-pressure regions (e.g., Klang Valley, selected tertiary hospitals).
Peer-reviewed studies in the region show ED overcrowding and access-block are associated with worse outcomes (higher mortality, more in-ED cardiac arrests) and threaten quality of time-sensitive care — reinforcing the safety imperative of improving flow.
Pilot research in Malaysia demonstrates feasibility and benefits of IoT/BLE-based real-time location or patient-tracking POCs to reduce turnaround time and help operations teams monitor queues and patient movement. These pilots show measurable improvements in patient turnaround when integrated into workflows.

Hasil Yang Diingini (Keadaan Masa Depan)

Public datasets show hospital bed counts by state and hospital type, revealing uneven geographic distribution of beds that contributes to regional capacity imbalances.
Media and official reporting have documented hospitals operating at high utilisation (many hospitals reporting ≥80% occupancy at times) and specific hospitals exceeding 100% utilisation during surges. This correlates with ambulance diversions and access-block in high-pressure regions (e.g., Klang Valley, selected tertiary hospitals).
Bed turnover efficiency improved (target: measurable increase in daily effective admissions per bed without compromising clinical outcomes).
Clinician & operational adoption: ≥75% of wards in pilot hospitals use the system’s bed/flow workflows daily; clinician SUS >70.

Tutup

Mohon

Solve a Problem Form

Notify

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Startup / Company Name

Contact Person Name

Phone/Mobile Number

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Technology Enabler(s)

AWS

Microsoft (Azure)

Google Cloud

Snowflake

AMD

Cisco

Apple

OpenAI

NVIDIA

Other

Innovation Area

Data

IoT

Cybersecurity

Cloud

GovTech

Sustainability

Fintech

HealthTech

Mobility

Other

Upload Proposal / Pitch Deck

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I agree to the Terms & Conditions and understand that participation in GII challenges is part of Malaysia's pilot innovation program, and credits will only be awarded upon validated submission. *

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