Continuous multimodal data supply chain and expandable clinical decision support for oncology
Open accessAbstract
The study introduces a clinical decision support system (CDSS) developed at a single academic cancer center, integrating real-time clinical, genomic, and imaging data for over 170,000 patients across 11 cancer types. We have developed the Yonsei Cancer Data Library (YCDL) data integration framework to continuously collect and update multimodal datasets comprising over 800 features per case. Quality control measures, using 143 logical comparisons, addressed missing data and outliers, achieving median accuracies of 92.6% for surgical and 98.7% for molecular pathology. An Extract-Transform-Load (ETL) process with natural language processing transformed unstructured data, enabling survival analyses stratified by tumor stage, which revealed significant stage-dependent differences. The CDSS dashboard visualizes patient trajectories and key milestones. User feedback from oncology professionals showed strong acceptance, with satisfaction scores exceeding 4 out of 5. This framework demonstrates the potential of multimodal data integration to enhance clinical decision-making and patient outcomes, with future research needed to validate its generalizability and scalability.
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Obtaining sufficient paired multimodal data in healthcare is challenging due to linking distinct modality-specific resources to the same patient or clinical encounter and inherent data fragmentation in healthcare systems.
“In healthcare, obtaining sufficient paired data is challenging due to the complexities associated with linking distinct modality-specific resources to the same patient or clinical encounter26, coupled with inherent data fragmentation in healthcare systems27.”
From MoMA: a mixture-of-multimodal-agents architecture for enhancing clinical prediction modelling (2025) · cites this paper as [27]
The Methods (Development of multimodal data supply chain; page unavailable) describe matching source data using patient and encounter identifiers, and the Discussion says manually collating many features is challenging. These passages support the difficulty of assembling linked patient data, but do not establish that obtaining sufficient paired multimodal data is difficult specifically because of that linkage. The fragmentation clause has a separate citation in the citing sentence and is not assessed here. (LLM, conf=0.78)
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MoMA: a mixture-of-multimodal-agents architecture for enhancing clinical prediction modelling
Gao, J., Rahman, M., Caskey, J., Oguss, M., O’Rourke, A., Brown, R., Stey, A., Mayampurath, A., Churpek, M. M., Chen, G., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02219-4 · Cited by 2 · see citing works
“In healthcare, obtaining suf fi cient paired data is challenging due to the complexities associated with linking distinct modality-speci fi c resources to the same patient or clinical encounter 26 , coupled with inherent data fragmentation in healthcare systems 27 .”
- Authors
- Chang, J. S., Kim, H., Baek, E. S., Choi, J. E., Lim, J. S., Kim, J. S., & Shin, S. J.
- Affiliations
- Yonsei University (KR)
- Corresponding author
- Kim, Jin Sung
- Shin, Sang Joon
- Venue
- npj Digital Medicine
- Published
- 2025-02-27
- Volume
- 8
- Issue
- 1
- Pages
- 128-128
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC11868524
- OpenAlex
- W4408061886
- Cited by
- 26
- References
- 41
- Citations by year
- 2026: 12 · 2025: 14
- FWCI
- 20.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- npj Digital Medicine (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Cancer Center (2020-data-we08)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- clinical decision support systems, oncology, multimodal data integration, genomic data, medical imaging data, natural language processing, unstructured clinical data, survival analysis, tumor stage, data pipelines, user acceptance
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