Diversity and inclusion: A hidden additional benefit of Open Data

Abstract

Abstract The recent imperative by the National Institutes of Health to share scientific data publicly underscores a significant shift in academic research. Effective as of January 2023, it emphasizes that transparency in data collection and dedicated efforts towards data sharing are prerequisites for translational research, from the lab to the bedside. Given the role of data access in mitigating potential bias in clinical models, we hypothesize that researchers who leverage open-access datasets rather than privately-owned ones are more diverse. In this brief report, we proposed to test this hypothesis in the transdisciplinary and expanding field of artificial intelligence (AI) for critical care. Specifically, we compared the diversity among authors of publications leveraging open datasets, such as the commonly used MIMIC and eICU databases, with that among authors of publications relying exclusively on private datasets, unavailable to other research investigators (e.g., electronic health records from ICU patients accessible only to Mayo Clinic analysts). To measure the extent of author diversity, we characterized gender balance as well as the presence of researchers from low- and middle-income countries (LMIC) and minority-serving institutions (MSI). Our comparative analysis revealed a greater contribution of authors from LMICs and MSIs among researchers leveraging open critical care datasets than among those relying exclusively on private data resources. The participation of women was similar between the two groups, albeit slightly larger in the former. Notably, although over 70% of all articles included at least one author inferred to be a woman, less than 25% had a woman as a first or last author. Importantly, we found that the proportion of authors from LMICs was substantially higher in the treatment than in the control group (10.1% vs. 6.2%, p<0.001), including as first and last authors. Moreover, we found that the proportion of US-based authors affiliated with a MSI was 1.5 times higher among articles in the treatment than in the control group, suggesting that open data resources attract a larger pool of participants from minority groups (8.6% vs. 5.6%, p<0.001). Thus, our study highlights the valuable contribution of the Open Data strategy to underrepresented groups, while also quantifying persisting gender gaps in academic and clinical research at the intersection of computer science and healthcare. In doing so, we hope our work points to the importance of extending open data practices in deliberate and systematic ways. Author Summary In light of the significance of data access to the mitigation of bias in clinical models, we hypothesize that researchers who leverage existing open-access datasets rather than privately-owned ones are more diverse. In this brief report, we propose to test this hypothesis in the transdisciplinary and expanding field of artificial intelligence for critical care. Specifically, we compare the diversity among authors of publications leveraging open datasets, such as the commonly used MIMIC and eICU databases, with that among authors of publications relying exclusively on private datasets, unavailable to other research investigators. To measure the extent of author diversity, we characterize gender balance, geographic diversity (i.e., the number of countries with which authors are affiliated and the income categories these countries map to), and the presence of researchers from minority-serving institutions located in the United States. Furthermore, we comment on the challenges of increasing the participation of researchers from underrepresented groups and suggest changes that can be made to the current Open Data strategy to enhance representation in authorship in the next decade. By evaluating the association between data accessibility and author diversity, our study pinpoints actionable steps that the broader field of clinical AI can take to foster inclusion in the scientific community and mitigate blind spots in data preparation and/or model development.

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