Clinical Informatics Approaches to Understand and Address Cancer Disparities

International Journal of Engineering Pedagogy

article

Tafadzwa L. Chaunzwa¹  Maria Quiles del Rey¹  Danielle S. Bitterman¹ 

[1] Department of Radiation Oncology, Dana-Farber Brigham Cancer Center, Harvard Medical School;Artificial Intelligence in Medicine ,(AIM) Program, Mass General Brigham, Harvard Medical School

关键词: Healthcare disparities;    cancer;    clinical informatics;    big data;    algorithms;   

DOI  :  10.1055/s-0042-1742511

来源: International Society for Engineering Education (IGIP), Kassel University Press

【 摘 要 】

Objectives: Disparities in cancer incidence and outcomes acrossrace, ethnicity, gender, socioeconomic status, and geography arewell-documented, but their etiologies are often poorly understoodand multifactorial. Clinical informatics can provide tools tobetter understand and address these disparities by enablinghigh-throughput analysis of multiple types of data. Here, wereview recent efforts in clinical informatics to study and measuredisparities in cancer.Methods: We carried out a narrative review of clinical informaticsstudies related to cancer disparities and bias published from2018-2021, 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 cancerdisparities across race, ethnicity, gender, and age were identified.Most cancer disparities work within clinical informatics used RWDanalysis, NLP, radiomics, and genomics. Emerging applicationsof clinical informatics to understand cancer disparities, includingproteomics, metabolomics, and metagenomics, were less wellrepresented in the literature but are promising future researchavenues. 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 beenused to probe a range of data sources to understand cancerdisparities across different populations. As informatics toolsbecome integrated into clinical decision-making, attention willneed to be paid to ensure that algorithmic bias does not amplifyexisting disparities. In our increasingly interconnected medicalsystems, clinical informatics is poised to untap the full potentialof multi-platform health data to address cancer disparities.

【 授权许可】

Unknown