Sessions

The Centre for Preventive Care is a speculative design project exploring the future of healthcare. In november 2022 visitors were invited to Södersjukhuset in Stockholm to experience a healthcare visit in the future and to reflect upon pros and cons of a healthcare system build on prediction and prevention. 

EHDS (European Health Data Space) proposes a common European legislation which among other things ensures access to health data for secondary use.In this presentation, we look at the informatics associated with secondary use of health data, established standards and techniques for making data available for secondary use (including OMOP), international real world examples and how healthcare organizations can benefit internally from tools for secondary use.

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

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.

Artificial intelligence (AI) has great potential to transform healthcare, making it more efficient, equitable, and safe. There are hundreds of AI-based tools available on the market today, yet few get adopted into clinical practice because of numerous barriers and challenges. This talk provides an overview of these hurdles and suggests 26 considerations synthesized from the literature, interviews, and workshop with stakeholders who have experience with the process of trying to adopt AI-based tools into clinical practice. We believe that as the barriers and challenges to AI adoption are addressed and overcome, AI will inevitably have a lasting impact on improving healthcare for all.

In this seminar, Michelle will give an overview of human-centered Artificial Intelligence at AstraZeneca's Digital Health Research & Development.

A practical approach to innovating and transforming healthcare through collaboration and acceleration of external companies and partners within Ramsay Santé 

Successful digitalization requires bold and courageous leadership – with extensive skills in change management and fully understanding Return on Investment (ROI) – “bang for the buck”. This lecture will address the challenges to all healthcare leaders in digitalization, with the objective that all decision making must be based on facts to ensure that a change always is an improvement.

Nursing practice is transforming at pace to lead improvements in patient outcomes in hospital, primary and community practice. New opportunities are emerging in advanced nursing practice which is a new level of practice defined by knowledge and skills acquisition. There are 4 leadership domains of advanced clinical practice: clinical, professional, health system and health policy leadership. A key component of health systems leadership is driving system change through digital transformation and clinical informatics. The approach to developing advanced clinical practice in east London will be described. Embedded in our approach is the systematic use of our multidisciplinary ‘WeConnect’ digital transformation programme. To implement change and training, a thorough clinical and technical understanding are both needed. This is where the role of the nursing and medical informaticists alongside innovative and engaging advanced clinical practitioners is essential. Key skills for advanced clinical practitioners are the development of autonomous clinical practice based on a foundation of digital skills. These include: use of SNOMED-CT for problem and procedure listing and decision support, analytics and risk based assessments derived from the real world data recorded in the electronic health record. Supporting the journey towards higher levels of digital and SNOMED-CT maturity is a key organisational objective.

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.