Sessions

Separate registration required: https://www.mie2023.org/tutorials, Transitioning from data to a clinical decision support system able tobenefit patients involves a plethora of perspectives within the following domains:clinical, legal, technical, managerial, and system development. This requires multiplepartners such as healthcare providers, universities, authorities and technologyproviders to join forces. In this tutorial the partners Region Halland, SahlgrenskaUniversity Hospital, Swedish Authority for Privacy Protection, Halmstad University,AI Sweden, HallandiaV, and the Netherlands eScience Center will give theirperspectives and deep-dive into several of the challenges and possibilities of the realizationof Clinical Decisions Support Systems (CDSS), for the specific use-caseof heart failure readmission prediction.

Separate registration required: https://www.mie2023.org/tutorials, openEHR consists of open specifications and clinical models for building healthcare and welfare information systems. This Master Class presents the problems openEHR addresses and how openEHR addresses them using its reference model and the clinical models that consist of archetypes and templates. It will further be explained how openEHR relates to other standards, how openEHR can be localised and implemented and how the organisation openEHR International and its surrounding community work.

The European Digital Innovation Hub (EDIH) project Health Data Sweden (HDS), initiated in January 2023, aims to support this development by creating a more effective access to services to regarding digitalisation of the health care system both regionally and nationally, through the use of health data. The HDS connects 17 partners from all over Sweden, who currently offer services in health data and related fields such as digital care and e-health, to various target groups and stakeholders. HDS will also connect with other EDIHs throughout Europe, to transfer knowledge and share best practices concerning the use of health data and related fields, in order to improve healthcare on the EU level.In this panel, we will present some of the main challenges and needs in the health data context identified by the partners behind EDIH-HDS, and discuss how the project will address these obstacles. The five main work packages (the focus of WP1 is primarily administrative) will provide the structure for the presentation. In order to engage the audience and stimulate feedback and discussion, the panel will contain interactive elements throughout.

Remarkable progress in artificial intelligence (AI) algorithms performance, and the fast growth in “real world” data available in electronic form generate high hopes for healthcare quality, efficiency, and accessibility improvements. But this game changing progress also causes growing concerns about the effects of growing AI use and its unintended, unanticipated, or even intentionally unethical consequences. Numerous issues and limitations of the algorithms and data used in healthcare and beyond have become more visible, and several organizations and researchers have proposed advice and guidelines to help address these concerns, issues, and limitations. Principles of Responsible AI are now promoted by several important organizations and stakeholders in the AI industry, but there is a need to move these principles towards practical realization and application in real-world scenarios. This panel will address several key aspects of responsible AI in health: explainability and interpretability; bias and fairness; reliability, reusability, and efficiency; privacy and confidentiality protection.

Deep learning models for radiology are typically deployed either through cloud-based platforms, through on-premises infrastructures, or though heavyweight viewers. This tends to restrict the audience of deep learning models to radiologists working in state-of-the-art hospitals, which raises concerns about the democratization of deep learning for medical imaging, most notably in the context of research and education. We show that complex deep learning models can be applied directly inside Web browsers, without resorting to any external computation infrastructure, thanks to the use of WebAssembly, and we release our code as free and open-source software. This opens the path to the use of teleradiology solutions as an effective way to distribute, teach, and evaluate deep learning architectures.

Separate registration required: https://www.mie2023.org/tutorials,With this tutorial, participants will gain a detailed insight into the Data Sharing Framework (DSF). The open source DSF enables users to execute biomedical research and healthcare delivery processes across organizations, and the tutorial will demonstrate this with examples from the German Medical Informatics Initiative (MII) funded by the Federal Ministry of Education and Research (BMBF). The tutorial will cover fundamental concepts of distributed processes, the DSFs architecture and key standards such as HL7 FHIR and BPMN 2.0. Participants will have the opportunity to gain hands-on experience with the DSF by working with different processes in a lab setting. Technical aspects such as authentication and authorization will be covered, as well as guidance on using the DSF for other use cases. This tutorial is designed for those involved in distributed research projects, including project members and software developers, as well as individuals interested in multi-organizational research projects.Additional information: https://dsf.dev/tutorials/MIE2023.html

Equal access to health data is far from reality today and there is a lack of comparison between countries. To motivate, understand and increase progress, we start from the position that highlighting current status, sharing evidence related to practices and policies, and identifying major enablers and obstacles across national and regional boundaries is necessary. Therefore, we have invited panel participants to introduce five different countries, representing five different stages of and approaches to patients’ ORA implementation.