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CC BY-NC-ND 4.0 · Yearb Med Inform 2022; 31(01): 121-130
DOI: 10.1055/s-0042-1742511
Section 2: Cancer Informatics
Survey

Clinical Informatics Approaches to Understand and Address Cancer Disparities

Tafadzwa L. Chaunzwa‡   *
1   Department of Radiation Oncology, Dana-Farber Brigham Cancer Center, Harvard Medical School, Boston, MA, USA
2   Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
,
Maria Quiles del Rey‡   *
1   Department of Radiation Oncology, Dana-Farber Brigham Cancer Center, Harvard Medical School, Boston, MA, USA
,
Danielle S. Bitterman
1   Department of Radiation Oncology, Dana-Farber Brigham Cancer Center, Harvard Medical School, Boston, MA, USA
2   Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
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Summary

Objectives: Disparities in cancer incidence and outcomes across race, ethnicity, gender, socioeconomic status, and geography are well-documented, but their etiologies are often poorly understood and multifactorial. Clinical informatics can provide tools to better understand and address these disparities by enabling high-throughput analysis of multiple types of data. Here, we review recent efforts in clinical informatics to study and measure disparities in cancer.

Methods: We carried out a narrative review of clinical informatics studies related to cancer disparities and bias published from 2018-2021, with a focus on domains such as real-world data (RWD) analysis, natural language processing (NLP), radiomics, genomics, proteomics, metabolomics, and metagenomics.

Results: Clinical informatics studies that investigated cancer disparities across race, ethnicity, gender, and age were identified. Most cancer disparities work within clinical informatics used RWD analysis, NLP, radiomics, and genomics. Emerging applications of clinical informatics to understand cancer disparities, including proteomics, metabolomics, and metagenomics, were less well represented in the literature but are promising future research avenues. Algorithmic bias was identified as an important consideration when developing and implementing cancer clinical informatics techniques, and efforts to address this bias were reviewed.

Conclusions: In recent years, clinical informatics has been used to probe a range of data sources to understand cancer disparities across different populations. As informatics tools become integrated into clinical decision-making, attention will need to be paid to ensure that algorithmic bias does not amplify existing disparities. In our increasingly interconnected medical systems, clinical informatics is poised to untap the full potential of multi-platform health data to address cancer disparities.

Keywords

Healthcare disparities - cancer - clinical informatics - big data - algorithms

* Contributed equally




Publikationsverlauf

Artikel online veröffentlicht:
04. Dezember 2022

© 2022. 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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