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CC BY-NC-ND 4.0 · Yearb Med Inform 2023; 32(01): 104-110
DOI: 10.1055/s-0043-1768721
Section 2: Cancer Informatics
Survey

Clinical Informatics Approaches to Facilitate Cancer Data Sharing

Sanjay Aneja
1   Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT, USA
2   Center for Outcomes Research and Evaluation at Yale, New Haven, CT, USA
3   Department of Bioinformatics and Data Science, Yale School of Medicine, New Haven, CT, USA
,
Arman Avesta
1   Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT, USA
2   Center for Outcomes Research and Evaluation at Yale, New Haven, CT, USA
,
Hua Xu
3   Department of Bioinformatics and Data Science, Yale School of Medicine, New Haven, CT, USA
,
Lucila Ohno Machado
3   Department of Bioinformatics and Data Science, Yale School of Medicine, New Haven, CT, USA
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Summary

Objectives: Despite growing enthusiasm surrounding the utility of clinical informatics to improve cancer outcomes, data availability remains a persistent bottleneck to progress. Difficulty combining data with protected health information often limits our ability to aggregate larger more representative datasets for analysis. With the rise of machine learning techniques that require increasing amounts of clinical data, these barriers have magnified. Here, we review recent efforts within clinical informatics to address issues related to safely sharing cancer data.

Methods: We carried out a narrative review of clinical informatics studies related to sharing protected health data within cancer studies published from 2018-2022, with a focus on domains such as decentralized analytics, homomorphic encryption, and common data models.

Results: Clinical informatics studies that investigated cancer data sharing were identified. A particular focus of the search yielded studies on decentralized analytics, homomorphic encryption, and common data models. Decentralized analytics has been prototyped across genomic, imaging, and clinical data with the most advances in diagnostic image analysis. Homomorphic encryption was most often employed on genomic data and less on imaging and clinical data. Common data models primarily involve clinical data from the electronic health record. Although all methods have robust research, there are limited studies showing wide scale implementation.

Conclusions: Decentralized analytics, homomorphic encryption, and common data models represent promising solutions to improve cancer data sharing. Promising results thus far have been limited to smaller settings. Future studies should be focused on evaluating the scalability and efficacy of these methods across clinical settings of varying resources and expertise.

Keywords

Data sharing - federated learning - encryption - common data models - cancer


Publication History

Article published online:
06 July 2023

© 2023. IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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