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. 2024 Aug 15:12:e58548.
doi: 10.2196/58548.

Bridging Real-World Data Gaps: Connecting Dots Across 10 Asian Countries

Guilherme Silva Julian  1 , Wen-Yi Shau  2 , Hsu-Wen Chou  3 , Sajita Setia  4
Affiliations

Bridging Real-World Data Gaps: Connecting Dots Across 10 Asian Countries

Guilherme Silva Julian et al. JMIR Med Inform. .

Abstract

The economic trend and the health care landscape are rapidly evolving across Asia. Effective real-world data (RWD) for regulatory and clinical decision-making is a crucial milestone associated with this evolution. This necessitates a critical evaluation of RWD generation within distinct nations for the use of various RWD warehouses in the generation of real-world evidence (RWE). In this article, we outline the RWD generation trends for 2 contrasting nation archetypes: "Solo Scholars"-nations with relatively self-sufficient RWD research systems-and "Global Collaborators"-countries largely reliant on international infrastructures for RWD generation. The key trends and patterns in RWD generation, country-specific insights into the predominant databases used in each country to produce RWE, and insights into the broader landscape of RWD database use across these countries are discussed. Conclusively, the data point out the heterogeneous nature of RWD generation practices across 10 different Asian nations and advocate for strategic enhancements in data harmonization. The evidence highlights the imperative for improved database integration and the establishment of standardized protocols and infrastructure for leveraging electronic medical records (EMR) in streamlining RWD acquisition. The clinical data analysis and reporting system of Hong Kong is an excellent example of a successful EMR system that showcases the capacity of integrated robust EMR platforms to consolidate and produce diverse RWE. This, in turn, can potentially reduce the necessity for reliance on numerous condition-specific local and global registries or limited and largely unavailable medical insurance or claims databases in most Asian nations. Linking health technology assessment processes with open data initiatives such as the Observational Medical Outcomes Partnership Common Data Model and the Observational Health Data Sciences and Informatics could enable the leveraging of global data resources to inform local decision-making. Advancing such initiatives is crucial for reinforcing health care frameworks in resource-limited settings and advancing toward cohesive, evidence-driven health care policy and improved patient outcomes in the region.

Keywords: Asia; EMR; HTA; electronic medical records; health care databases; health technology assessment; real-world data; real-world evidence.

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Conflict of interest statement

Conflicts of Interest: GSJ, H-WC, and W-YS declare that while being employees of Pfizer, there is no conflict of interest in relation to the work presented in this article. The views and opinions expressed herein are solely those of the author(s) and do not reflect the views or positions of their employers.

Figures

Figure 1
Figure 1
Timeline of HTA development across selected 10 countries in Asia. ACE: Agency for Care Effectiveness; CDE: Centre for Drug Evaluation; DAC: Drug Advisory Committee; HA: Hospital Authority; HITAP: Health Intervention and Technology Assessment Program; HSPI: Health Strategy and Planning Institute; HTA: health technology assessment; HTAIn: Health Technology India; InaHTAC: Indonesian Health Technology and Assessment Committee; MaHTAS: Malaysian Health Technology Assessment; MOH: Ministry of Health; UHC: universal health coverage.
Figure 2
Figure 2
Real-world data landscape for Taiwan (2017-2022). CCCS: cross-country collaboration studies; CER: comparative effectiveness research; EHR: electronic health record; EMR: electronic medical record; SCS: single-country studies.
Figure 3
Figure 3
Real-world data landscape for Singapore (2018-2023). CCCS: cross-country collaboration studies; CER: comparative effectiveness research; EHR: electronic health record; EMR: electronic medical record; SCS: single-country studies; TB: tuberculosis.
Figure 4
Figure 4
Real-world data landscape for India (2017-2022). CCCS: cross-country collaboration studies; CER: comparative effectiveness research; EHR: electronic health record; EMR: electronic medical record; SCS: single-country studies.
Figure 5
Figure 5
Real-world data landscape for Hong Kong (2018-2023). CCCS: cross-country collaboration studies; CER: comparative effectiveness research; EHR: electronic health record; EMR: electronic medical record; SCS: single-country studies.
Figure 6
Figure 6
Real-world data landscape for Malaysia (2018-2023). CCCS: cross-country collaboration studies; CER: comparative effectiveness research; EHR: electronic health record; EMR: electronic medical record; SCS: single-country studies.
Figure 7
Figure 7
Real-world data landscape for Thailand (2017-2022). CCCS: cross-country collaboration studies; CER: comparative effectiveness research; COOL-AF: Cohort of Antithrombotic Use and Optimal International Normalized Ratio Levels in Patients with Atrial Fibrillation; EHR: electronic health record; EMR: electronic medical record; SCS: single-country studies.
Figure 8
Figure 8
Real-world data landscape for Indonesia (2018-2023). CCCS: cross-country collaboration studies; CER: comparative effectiveness research; EHR: electronic health record; EMR: electronic medical record; SCS: single-country studies.
Figure 9
Figure 9
Real-world data landscape for Pakistan (2018-2023). CCCS: cross-country collaboration studies; CER: comparative effectiveness research; EHR: electronic health record; EMR: electronic medical record; SCS: single-country studies.
Figure 10
Figure 10
Real-world data landscape for Vietnam (2018-2023). CCCS: cross-country collaboration studies; CER: comparative effectiveness research; EHR: electronic health record; EMR: electronic medical record; SCS: single-country studies.
Figure 11
Figure 11
Real-world data landscape for Philippines (2018-2023). CCCS: cross-country collaboration studies; CER: comparative effectiveness research; EHR: electronic health record; EMR: electronic medical record; SCS: single-country studies.
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