OBJECTIVE: Developing equitable, sustainable informatics solutions is key to scalability and long-term success for projects in the global health informatics (GHI) domain. This paper presents key strategies for incorporating principles of health equity in the GHI project lifecycle. MATERIALS AND METHODS: The American Medical Informatics Association (AMIA) GHI Working Group organized a collaborative workshop at the 2023 AMIA Annual Symposium that included the presentation of five case studies of how principles of health equity have been incorporated into projects situated in low-and-middle-income countries and with Indigenous communities in the U.S. and best practices for operationalizing these principles into other informatics projects. RESULTS: We present five principles: (1) Inclusion and Participation in Ethical, Sustainable Collaborations; (2) Engaging Community-Based Participatory Research Approaches; (3) Stakeholder Engagement; (4) Scalability and Sustainability; (5) Representation in Knowledge Creation, along with strategies that informatics researchers may use to incorporate these principles into their work. DISCUSSION: Presented case studies and subsequent focus groups yielded key concepts and strategies to promote health equity that may be operationalized across GHI projects. CONCLUSION: Equitable, sustainable, and scalable GHI projects require intentional integration of community and stakeholder perspectives in project development, implementation, and knowledge creation processes.

Glioblastoma is the most common malignant brain tumor with a poor survival rate due to its high intra- and intertumor heterogeneity. The current heterogeneity determination is based on a microscopic analysis of Hematoxilyn and Eosinstained tumor slides carried out by experienced neuropathologists. There is no standardized procedure yet, to quantify heterogeneity, though. With the hypothesis that the amount of heterogeneity impacts overall survival, we aim to develop an objective method to capture heterogeneity. We were able to successfully implement an initial Machine Learning classification model for determining heterogeneity. However, the available dataset was insufficient to train a resilient and stable system. Therefore, we propose an architecture for a semi-automatic data collection and preprocessing framework easing the collection of large quantities of required data. While there exists a multitude of frameworks tackling parts of the tumor research area, no simple ready-to-use solution is present for the easy collection of tumor data in daily clinical routine, especially for high-resolution pathological images. We plan to implement the proposed architecture at the University Hospital Augsburg, Germany in 2023. The dataset created in this process will then be used as a resilient basis for heterogeneity classification and for analyzing glioblastomas in general.

Health data are being generated in large quantities. However, these data are rarely used beyond their primary purpose in comparison to other industries. Main barriers are privacy and ethical concerns and the fact that medical data are scattered across institutions. The study investigated the availability and utilization of public use files (PUF) of primary research data as well as ethical and legal barriers limiting the donation of medical research data for secondary use in preparation for the development of a process model to foster medical data donation. First, the availability and utilization of PUFs from primary research data were investigated through a selective literature review. Secondly, ethical and legal barriers that are limiting the donation of medical research data for secondary use were investigated. A simplified process model was developed based on an initial literature review. The process model was used to identify questions that need to be addressed to facilitate data donation. Solutions to address the identified questions will be developed through another review of the literature and expert interviews. An enhanced data donation process model will be developed and refined through a second iteration. The review of PUFs has shown that the availability and utilization of research data through public use files for secondary use is limited. As a means to facilitate and foster secondary data use, the researchers advocate for a simplified data donation process for medical research data. The results of the first step of the project showed that several ethical, legal, and social-technical questions need to be addressed to implement a simplified data donation process. The project s next steps will include the development of solutions to those barriers will be developed and included in an improved concept of a data donation process model.