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Yu-Ting Lin, Big Data Center, China Medical University Hospital, China Medical University, Taichung,Taiwan
Ya-Chi Lin, Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan
Hung-Lin Chen, Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan
Che-Chen Lin, Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan
Min-Yen Wu, Big Data Center, China Medical University Hospital, China Medical University, Taichung,Taiwan
Sheng-Hsuan Chen, Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan
Zi-Han Lin, Big Data Center, China Medical University Hospital, China Medical University, Taichung, 9 Taiwan
Yi-Ching Chang, Big Data Center, China Medical University Hospital, China Medical University, Taichung, 9 Taiwan
Chuan-Hu Sun, Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan
Sheng-Ya Lu, Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan
Min-Yu Chiang, Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan
Hui-Chao Tsai, Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan
Mei-Ju Shih, Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan
David Ray Chang, Division of Nephrology, Department of Internal Medicine, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan
Fuu-Jen Tsai, Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan
Hsiu-Yin Chiang, Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan
Chin-Chi Kuo, Big Data Center, China Medical University Hospital, China Medical University, Taichung, TaiwanFollow

Abstract

In the past two decades, healthcare organizations have transitioned from the early stages of digitization and digitalization to a more comprehensive process of digital transformation, a shift significantly accelerated by the advent of artificial intelligence (AI). Consequently, the development of high-quality clinical data warehouses, derived from electronic health records (EHRs) and enriched with multidomain data, such as genomics, proteomics, and Internet of Things (IoT) information, has become essential for the creation of the modern patient digital twin (PDT). This approach is critical for leveraging AI in the evolving landscape of clinical practice. Leading medical centers and healthcare institutions have adopted this model, as summarized in this review.

Since 2020, China Medical University Hospital (CMUH) has been constructing its data ecosystem by integrating EHRs with extensive genomic databases. This initiative has led to the development of a data service platform, the ignite Hyper-intelligence (iHi®) platform. The iHi platform serves as a case study exemplifying the workflow of the smart data chip, which facilitates the deep cleaning and reliable de-identification of clinical data while incorporating analytical platforms related to genomics and the microbiome to enhance insight extraction processes. The ability to predict complex interactions and disease trajectories among PDTs, digital counterparts of healthcare professionals, and virtual socioeconomic environments will be pivotal in advancing personalized healthcare and optimizing patient outcomes. Future challenges will involve the unification of cross-institutional data platforms and ensuring the interoperability of AI inferences—key factors that will define the next era of AI-driven healthcare.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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