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How AWS Cloud is transforming global smart healthcare and trusted research environments
Global experts from healthcare, cloud technology, and biomedical data convened to explore cloud-driven transformation in smart healthcare. Speakers from AWS, BC Platforms, Yonsei University Health System, and the National Health Research Institutes shared insights on cloud infrastructure, interoperability standards, and Trusted Research Environments (TREs). On December 3, they emphasized that connected, secure, and interoperable data ecosystems are essential for scaling precision medicine worldwide.
Cloud adoption accelerates smart healthcare transformation
The conversation opened with the fundamental need for robust infrastructure. Rich Fuh, Public Sector Lead at AWS Taiwan, emphasized that the launch of the new AWS Asia Pacific (Taipei) Region this year changes the equation for local healthcare providers.
“This new Region provides local data-center capabilities, directly addressing data residency concerns that have historically slowed cloud adoption in regulated industries,” Fuh noted.
The stakes are high. Fuh revealed that AWS now supports 80% of the world’s biotech unicorns and 19 of the top 20 pharmaceutical companies. With a committed investment of USD 5 billion in Taiwan, AWS is actively collaborating with domestic institutions. This include Academia Sinica and Asia University, to bolster the “Healthy Taiwan” initiative. The message was clear: cloud adoption is not merely IT modernization; it is the prerequisite for participating in the global bio-economy.
Dr. Charlie Lee from AWS Genomics Industry Lead for Asia-Pacific and Japan said his work in Singapore, Thailand, and the UK revealed clear challenges for precision medicine. He noted medical institutions generate over 50 PB of data each year, with 97% unused due to unstructured formats.
He recommended building data lakes to unify structured and unstructured data for advanced analytics, machine learning, and precision-medicine programs.
The Asian Data Paradox: burden vs. benefit
The Precision Era of medicine could add an extra 11 million years of life for patients in Asia Pacific over the next decade. But this requires a shift from volume to value, connecting biobanks, genomics, and claims data into a unified ecosystem.
Andrew O’Brien
However, infrastructure alone cannot solve the region’s unique epidemiological crisis. Andrew O’Brien, Senior Vice President for APAC and Japan at BC Platforms, utilized the forum to present a stark reality he calls the “Asian Data Paradox.”
According to data presented by O’Brien, the Asia-Pacific region carries the world’s heaviest disease burden. However, it remains critically overlooked in global pharmaceutical research. Drug discovery is still overwhelmingly biased toward Western datasets, creating a therapeutic efficacy gap for Asian populations.
O’Brien pointed to compelling disease-specific statistics that necessitate a regional approach:
- Hepatitis B & Liver Cancer: Approximately 75% of global cases—nearly 500,000 annual deaths—occur in Asia.
- Nasopharyngeal Carcinoma: 70% of new global cases are concentrated in East and Southeast Asia.
- IgA Nephropathy: In China, up to 45% of kidney biopsies show this condition, the highest prevalence worldwide, yet it remains neglected in international trial designs.
- Thalassemia: Despite 50,000 children born with severe forms annually in South Asia, new therapies face delays due to under-representation in clinical trials.
“The Precision Era of medicine could add an extra 11 million years of life for patients in Asia Pacific over the next decade,” O’Brien stated, projecting a potential economic impact of USD 450 billion. “But this requires a shift from volume to value, connecting biobanks, genomics, and claims data into a unified ecosystem.”
The last mile challenge: From claims to clinical depth
The challenge lies in the ‘last mile’ evidence gap. We need to bridge the disconnect between administrative claims data and deep clinical depth—specifically lab results, cancer staging, and biomarkers
Andrew O’Brien
Taiwan is uniquely positioned to lead this shift, but significant hurdles remain. While the National Health Insurance Research Database (NHIRD) offers one of the world’s richest longitudinal population datasets, O’Brien and local experts argued that it is no longer sufficient on its own.
“Taiwan has a global-class foundation with the NHIRD and strong government leadership,” O’Brien explained. “However, the challenge lies in the ‘last mile’ evidence gap. We need to bridge the disconnect between administrative claims data and deep clinical depth—specifically lab results, cancer staging, and biomarkers.”
Currently, much of this granular data is siloed within individual hospitals or purchased for specific, low-ROI studies. To unlock high-value precision oncology such as generating the Real-World Evidence (RWE) required for high-cost cancer drug reimbursement. Taiwan must integrate these disparate data sources into a secure, federated system such as AWS.
Build Trusted Research Environments to solve
This integration is where the concept of the TRE takes center stage. Moving away from the traditional model where data is copied and sent to researchers, a TRE allows analysis to occur within a secure enclave, protecting patient privacy while enabling complex computation.
Dr. Yi-Hsin Connie Yang, a researcher at the National Health Research Institutes (NHRI), discussed the development of HARBOR, a TRE platform built in partnership with BC Platforms.
“In the past, researchers often had to travel to physically secure rooms to access sensitive health data, which severely bottlenecked innovation,” Dr. Yang noted. “We are moving toward a digital framework based on the UK’s ‘Five Safes’ model: Safe People, Safe Projects, Safe Settings, Safe Data, and Safe Output.”
Yang added that NHRI is already exploring cross-border collaborations, including discussions with Singapore’s A*STAR, and evaluating certification pathways to standardize Taiwan’s growing TRE ecosystem.
Smart healthcare regional case study: The Korean hybrid model
Providing a roadmap for what this future might look like, Dr. You Seng Chan of Yonsei University shared Korea’s aggressive approach to data lakes and AI as well as smart healthcare.
Dr. Chan highlighted the concept, mentioned by Lee, that medical institutions generate over 50 petabytes of data annually. To combat this, Yonsei implemented a hybrid-cloud architecture. They link on-premise data lakes to AWS via VPN. This created a “hospital zone” that allows for cloud scalability without exposing sensitive data to public networks.
The results have been tangible. Dr. Chan introduced Y-KNOT, a bilingual Large Language Model (LLM) trained on Yonsei’s internal clinical data. Unlike generic commercial models. Y-KNOT is integrated directly into clinical workflows to generate discharge summaries.
“On-premise GPUs simply can no longer meet the demands of modern AI training,” Dr. Chan admitted. “Cloud elasticity is essential for processing the sheer volume of genomic and phenotypic data required for next-generation care.”
Interoperability, governance and PPPs
Yueh-Ping Liu , Director-General of the Department of Medical Affairs at the Ministry of Health and Welfare (MOHW), reinforced the government’s commitment. Citing past Build-Operate-Transfer (BOT) successes, she affirmed that the ministry would continue to foster public-private partnerships（PPPs. This ensure these digital advancements translate into real clinical impact.
As the forum drew to a close, the narrative for Taiwan was clear. The hardware is arriving, researchers are finding the disease targets, and the regulatory frameworks are maturing. The next chapter involves the difficult but high-reward work of harmonization—building the secure, interoperable pipes that will allow Taiwan’s data to not only improve local care but to reshape the global understanding of Asian biology.
This article by Steven Chung originally appeared in GeneOnline: How AWS Cloud Is Transforming Global Smart Healthcare and Trusted Research Environments.
Thank you to GeneOnline and Steven Chung.
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