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
