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

项目摘要 遗传性癌症是普遍存在的，并导致终生患癌症的高风险。早期识别高危个体对于预防和拦截癌症至关重要，但遗传性癌症风险筛查落后于指南--这一护理差距在医学边缘化社区更为广泛。跨性别、性别多样性和性别多样性（TGSD）患者代表了这样一个边缘化群体，他们可以从更好的癌症护理中受益，包括遗传性癌症风险筛查。表型性别，染色体性别，当前和过去的器官/组织库存，和激素环境影响遗传性癌症综合征的风险评估和护理。这些因素对TGSD个体有独特的影响。虽然电子应用程序代表了通过系统化风险评估来减少健康差异的重要策略，但这些应用程序收集了用于风险计算的性别相关变量;文化上不称职，因此不准确，收集这些数据可能会导致参与者收到不正确的风险评估结果，最终影响下游护理。拟议项目的母基金，家族史和癌症风险研究（FOREST），已经实施了一个名为MeTree的风险评估应用程序，以提高风险评估的系统化。MeTree收集与性和性别有关的数据，与性有关的数据要素推动风险评估结果的返回。然而，初步实施我树刚刚超过300参与者的反应，我们发现不一致的解释与性别有关的领域之间的TGSD个人，导致与性别有关的反应，根据医疗记录的审查，不符合预期的响应域这个问题。这可能是由于问题和回答的措辞和选项。很少有措施已被设计与社区参与的TGSD个人，并没有这样的措施已被设计用于遗传风险评估。我们建议与TGSD参与者和社区成员合作，将参与者的反馈直接整合到Me Tree与性和性别相关的问题和回答选项的重新设计中。在对接触MeTree的TGSD FOREST参与者进行的半结构化定性访谈中，我们将寻求了解参与者对这些问题的解释，数据有效性，并收集参与者的想法，以便进行尊重和准确的重新设计。使用这些访谈的快速初步分析，我们将把变化纳入一个新的模型，在社区参与小组中与研究中没有接触过Me Tree的小组成员进行演示。在这些小组中，我们将与TGSD社区成员共同设计，以生成最终的Me Tree模型，该模型尊重并准确地收集风险评估所需的元素。整个研究将由跨性别研究人员领导，为他们提供一个独特的关系视角，这将有助于解决当前性和性别相关数据模型中存在的顺式规范假设。最终分析将被发布，并将更改纳入MeTree应用程序。

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.