Critical Data Literacy: Addressing race as a variable in a preclinical medical education session: Future Directions

Additional materials and references will be added to the slides for next year’s session.
With the first session delivered, librarians can evaluate the success of the current updates and further develop the curriculum. Additional references and materials will be added to the session to respond to student and librarian feedback. Critical data literacy is a necessity in relation to more than just clinical algorithms. Broader applications and wider integration will be pursued. Librarians will further develop curriculum with an eye towards algorithmic bias, artificial intelligence, data donation, and other emerging issues.  

Beyond these specific considerations, more generally, we hope to 
Examine materials for other librarian-led sessions and courses with a critical lens 
Continue the conversations of critical data literacy, collection, and use among librarians  
Expand critical data literacy instruction to other audiences 
 

Selection of outcomes to track, factors to include

Emphasizing the connection of understanding the originating dataset to applying clinical guidance and algorithms to individual patients, particularly coupled with use of the PICO framework, i.e. consider the population, the risk factors studied, and the outcomes tracked

Churchwell K, Elkind MSV, Benjamin RM, Carson AP, Chang EK, Lawrence W, et al. Call to action: structural racism as a fundamental driver of health disparities: a presidential advisory from the American Heart Association. Circulation [Internet]. 2020 Dec 15 [cited 2021 Apr 14];142(24). Available from: https://www.ahajournals.org/doi/10.1161/CIR.0000000000000936

• "Although not explicitly discussed as such in the statement, the extrapolation of data across ethnic subgroups and the lack of inclusive data collection instruments that can provide disaggregated data demonstrate systemic inequities that can perpetuate health disparities."
• "We will move beyond the study of race as a predictor of cardiovascular and general health to the deeper analysis of structural racism as a specific and fundamental cause of racial and ethnic disparities."
• "The AHA must explore ways to enhance its robust suite of programs in quality improvement, including Get with the Guidelines and Target BP by improving data collection on race, ethnicity, and selected SDOH to drive the elimination of health care and health disparities. "
• "At the same time, the AHA must reconsider when and how to include race/ethnicity and social determinants measures in risk calculators... the rationale for inclusion of race should be made explicit and reviewed by experts in race/ethnicity and medicine"

Implications for AI/ML

Emphasizing the importance of interrogating training data used in AI/ML  

Emphasizing the evolution of algorithms and representation in clinical trials