Genetic testing is saving lives by allowing patients to better understand and manage their heritable risk for cancer and other disorders. However, its effectiveness is limited by the large number of Variants of Uncertain Significance (VUS), or variants that lack sufficient evidence for robust clinical interpretation. Interpretation of cancer variants typically requires analysis of highly sensitive case-level patient data, yet this data is often inaccessible. Furthermore, due to a lack of equitable representation in genetic data sets, health disparities can present via disproportionately high VUS rates amongst underserved patient populations. Issues of inaccessible data, when compounded with the unique struggles of underserved groups, have thus created significant barriers to advancing genetic testing as an effective and equitable tool for cancer prevention. Federated analysis using container methods has been recognized as a feasible solution for issues of patient data access. Container methods ?bring the code to the data? to analyze information locally. This analysis generates useful, de-identified summary statistics that can be shared to support clinical variant interpretation without exposing or transferring sensitive patient-level data. However, preliminary exploration of these methods have revealed more issues beyond patient privacy: concerns such as data governance, ownership, rights of access, regulatory policies, and economic incentive require further study. Certain topics can also be particularly salient amongst groups that are historically underserved. If left unexamined, these issues could interfere with the ethical, effective, and equitable implementation of container methods. Developing and designing federated analysis with container methods poses a unique opportunity to perform a case study of contentious topics in patient data access while testing the pragmatic feasibility of such methods. In order to survey the social, ethical, and regulatory landscape of container methods, this study will perform qualitative research with specified organizations. Specifically, existing protocols within healthcare institutions will be explored through semi-structured interviews with key informants from the organization. Interviews and surveys will also be performed to explore the attitudes and concerns of advocacy groups and patient populations. The study will be inclusive of at least one historically underserved group who may have uniquely contentious relationships to both health systems and data sharing initiatives. Research outcomes will include qualitative data that characterizes the explicit barriers to using container methods, as well as preliminary guidelines that can be used by various parties to advance data sharing efforts. Finally, these findings will also offer a detailed case study of how container methods, in general, might provide unique solutions to problems posed by highly sensitive, yet scientifically valuable, patient data.
Development of precision medicine requires the ability to share patient-level information from ethnically-diverse populations, but patient-level information is private and difficult to share due to numerous ethical, legal and social factors. Federated analysis with container methods can alleviate privacy concerns by ?bringing the code to the data?, analyzing the data within its secure home environment to generate the summary information that is essential for research, yet many concerns remain that reduce the potential for this form of data sharing. In this work, we will survey the social, ethical, and regulatory landscape of container methods via qualitative research with key informants from healthcare institutions and patient advocacy groups, to analyze their concerns so that they may be addressed more effectively.