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OBJECTIVES/GOALS: The Association of Clinical and Translational Science (ACTS) chartered a Diversity, Equity, and Inclusion (DEI) Committee to prioritize activities to support, develop and report on metrics for measuring progress toward DEI goals. This poster aims to describe the formative process toward prioritizing DEI in society’s efforts. METHODS/STUDY POPULATION: In 2021, the ACTS chartered the DEI Committee. Two ACTS Board of Directors members chair the committee, and members represent 10 academic institutions committed to prioritizing DEI. Members participated in a Human-Centered Design process to develop the committee’s mission, goals, and activities. The committee determined areas of opportunity for ACTS to increase DEI by identifying challenges to support DEI in clinical and translational science. The chairs facilitated a discussion using Mural to foster an interactive strategy to engage members in conversations that respected individual experiences, promoted a discussion of actions that ACTS as a society, and determining metrics for measuring DEI. RESULTS/ANTICIPATED RESULTS: We plan to present the committee’s efforts to create the ACTS mission statement and strategies to priority to DEI. We will describe current and future activities to engage historically underrepresented members and academic institutions in ACTS programs. Based on the committee’s work, ACTS has taken systematic approaches toward social justice and is beginning to determine new ways of engaging members. DISCUSSION/SIGNIFICANCE: Scientific societies that prioritize DEI increases equity and belonging across members. ACTS is at the forefront of advocacy, policy, and social justice effort. The DEI committee is positioned to aid ACTS in increasing DEI across the clinical and translational science spectrum.

Machine learning (ML) provides the ability to examine massive datasets and uncover patterns within data without relying on a priori assumptions such as specific variable associations, linearity in relationships, or prespecified statistical interactions. However, the application of ML to healthcare data has been met with mixed results, especially when using administrative datasets such as the electronic health record. The black box nature of many ML algorithms contributes to an erroneous assumption that these algorithms can overcome major data issues inherent in large administrative healthcare data. As with other research endeavors, good data and analytic design is crucial to ML-based studies. In this paper, we will provide an overview of common misconceptions for ML, the corresponding truths, and suggestions for incorporating these methods into healthcare research while maintaining a sound study design.