Improving identification and healthcare for patients with Inherited Cancer Syndromes: Evidence-based EMR implementation using a web-based computer platform

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PROJECT ABSTRACT Hereditary cancers are prevalent and cause high lifetime risk of cancer. Early identification of at-risk individuals is imperative to prevent and intercept cancer, but hereditary cancer risk screening lags behind guidelines - a care gap that is wider for medically marginalized communities. Transgender, gender diverse, and sex diverse (TGSD) patients represent one such marginalized community who could benefit from better cancer care, including hereditary cancer risk screening. Phenotypic sex, chromosomal sex, current and past organ/tissue inventory, and hormonal milieu impact risk assessment and care for hereditary cancer syndromes. These elements have unique implications for TGSD individuals. While electronic apps represent an important strategy to reduce health disparities by systematizing risk assessment, these apps collect sex-related variables that are used in risk calculations; culturally incompetent, and thus inaccurate, collection of this data could cause participants to receive an incorrect risk assessment result, ultimately impacting downstream care. The parent grant of the proposed project, the Family History and Cancer Risk Study (FOREST), has implemented a risk assessment app, called MeTree, to increase systematization of risk assessment. MeTree collects sex- and gender-related data, and the sex-related data element drives risk assessment result return. However, following preliminary implementation of Me Tree with just over 300 participant responses, we have found inconsistency of the interpretation of the sex-related field among TGSD individuals, leading to sex-related responses that, based on medical record review, do not correspond with expected response domains for this question. This is likely due to question and response wording and options. Very few measures have been designed with community engagement of TGSD individuals, and no such measures have been designed for genetic risk assessment. We propose engagement with TGSD participants and community members to integrate participant feedback directly into a redesign of the Me Tree sex- and gender-related questions and response options. In semi-structured qualitative interviews with TGSD FOREST participants exposed to MeTree, we will seek to understand participant interpretations of these questions, data validity, and collect participant ideas for respectful and accurate redesign. Using rapid initial analyses of these interviews, we will incorporate changes into a new model for presentation in community engagement panels with panelists who have not been exposed to Me Tree within the study. In these panels, we will engage in co-design with TGSD community members to generate a final Me Tree model that respectfully and accurately collects elements needed for risk assessment. The entire research study will be led by transgender researchers, giving them a unique relational perspective that will facilitate addressing cisnormative assumptions present in current sex- and gender-related data models. Final analyses will be published, and changes incorporated into the MeTree app.