Sessions

Healthcare longitudinal data offers endless opportunities such as diagnostics, and prognostics, to care personalization and improving healthcare quality and efficiency. These can be achieved through Artificial Intelligence (AI), data science, and in specific machine learning (ML). However, lack of inscrutability of the AI/ML-based techniques has hampered pervasive AI in healthcare systems. In this talk, we will introduce graph-based approaches for collecting, storing, visualizing, analyzing, and modeling healthcare data, and discuss how they outperform the other available approaches. We will also discuss challenges around health data and how synthetic data can mitigate them.

A lecture that is 100% generated and held by an AI? A few years ago that might have sounded like a science fiction scenario, but with the current tools such as Replika, GPT-3, DALL-E, MidJourney and more, AI content is being produced in a very high speed. In this lecture, a combination of AI techniques will be used to generate a lecture, fully held by the AI avatar ANDI about what the various algorithms think about the advancement of AI in health care for diagnosis, treatment and support. 

How can we improve care and make it more equal through a better and more efficient use of health data? This session, organized by Novartis and EU project EDAH will cover perspectives from patients, caregivers, and regions.Future opportunities and challenges with an increased use of health data will be addressed as well as what message to bring to the ongoing policy work within EHDS from the different user’s perspective.

Artificial intelligence (AI) has been used to support decision making in the mental health domain for both healthcare professionals and patients on different levels in the healthcare systems and self-care services. AI has been included in several health applications such as predicting mental health problems, diagnostics, treatment plans, workflows, and other duties that require decision support. Understanding the use of AI tools and how it is applied for support decision making in the mental health would contribute in improving the design, development and implementation in practice. Further knowledge will also help benefit shaping person-centered care approaches when using AI tools by understanding the dynamics used nowadays in support decision making and how to include patients in the development of future processes and tools,It is unclear what types of decisions that AI tools most commonly participate in supporting in mental health, how these tools are applied, and whether its implementation is engaging patients in the process of decision making or not, this uncertainty may contribute in shifting the decision- making process towards more technology polarization and far from patient centeredness. Therefore, a scoping review is conducted to map the characteristics of research done on AI tools for supporting decision making in the mental health field to contribute to the body of knowledge regarding how decisions are supported. In the proposed lecture we will present our findings regarding the use of AI in mental health and reflections on support decision making.

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.

The application of artificial intelligence (AI) in healthcare is at an early stage and research has shown difficulties with actual implementation in practice. Welcome to participate in an exciting workshop where you are introduced to a methodological and newly developed framework for supporting implementation processes in clinical practice.

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.

Artificial intelligence (AI) can potentially contribute to solutions to some of the major challenges facing healthcare systems around the world. However, the development of new AI applications does not guarantee that they will be routinely used in clinical practice. It is well established in implementation and innovation research that the introduction of new technology is associated with various forms of obstacles and resistance. This contributes to delayed and variable application and a high risk of losing the potential value they can bring to the healthcare system in terms of quality, safety, accessibility and optimization. In this presentation, we provide an overview of state of the art regarding the implementation of AI applications in healthcare today. We also suggest ways forward, how to develop successful strategies for implementation and work structured with implementation processes.

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.

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.