OBJECTIVES: Inclusive research methods are critical to promoting diversity in patient-centered research, aligning with regulatory guidance and societal expectations. Traditional demographic data collection in patient‑centered research is limited and lags behind societal advances, which leads to study populations that are inadequately described or under‑researched. The goal of this work was to review current questions and response options (ROs) used to collect demographic data to determine revisions needed to generate more inclusive data overall.

METHODS: A targeted literature search was conducted to explore inclusive data collection resources and their applicability to patient-centered research. Learnings were used to evaluate existing demographic questions and ROs for needed revisions, targeting those that were non‑inclusive and would result in misrepresentative data. ROs that were binary, mutually exclusive, or combined multiple groups, and questions that failed to reflect the patients’ lived experiences were targeted for revision. The process of refining the demographic questions and ROs was iterative based on review, discussion, and literature findings.

RESULTS: Researchers identified questions related to gender, race, ethnicity, and work status as non‑inclusive and revised accordingly. The revised demographic questions collect sex assigned at birth and current gender identity, as these are not interchangeable. Questions and ROs for ethnic identity and racial identity were updated to better reflect changing views around identity by expanding available options. ROs for employment status were changed to sensitively differentiate unemployment status. For most key areas, both questions and ROs were reworded to increase clarity and inclusion.

CONCLUSIONS: This research provides a framework for future research studies when collecting diverse demographic data. This practice should continue to evolve with regulatory and societal guidance. Collecting inclusive demographic data requires not only inclusive questions and ROs, but training on how to administer these questions. Inclusivity results in more robust data, ultimately improving patient‑centered research and providing a framework of accountability.