Sessions

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.

The Health Data Guide is intended to provide an overview of managing health data effectively so that the goals of improving the health of individuals can be more easily realized. Part One should provide a brief overview of the health data area and insights into a series of typical hurdles to overcome and ditches to avoid. It should provide the newcomer to the area with sufficient orientation to facilitate the discussion with various experts and partners. The more detailed Part Two is under development.

An interactive workshop to get familiar with the Social Return on Investment (SROI) methodology. The attendees will learn the basics about the SROI methodology and how this methodology is used within the European RE-SAMPLE project to ensure impact of the digital innovation for every stakeholder (societal impact). Next to this, the attendees will, by means of a group discussion, form a first opinion about the SROI methodology.

With more than a decade of supporting its customers in their healthcare data standardization efforts, B2i Healthcare publicly launched the Snowray Terminology Service to a global audience. This demonstration will introduce the most important functionalities to future users. Users are welcome to follow along with a free account.During the demonstration, we will cover the processes of creating and publishing new standard resources, including Code Systems, Value Sets and Concept Maps.

The smart data extractor is a semi-automatic data extraction software to assist clinician in the completion of case report forms.It is a pipeline build on top of Dr Warehouse, the Necker (ap-hp, France) data warehouse.It is based on thesauri and regular expression.It saves time and error and it is clinician friendly and easy to use.

The presentation will introduce a Nordic research network in health and welfare technology (HWT). The research network has around 80 members from all the Nordic countries and aims to consolidate knowledge, research, higher education, and experiences in health and welfare technology with a user perspective. The purpose of the presentation is to inform about ongoing activities and to link more researchers to the network. The network also wants to be a contributor with knowledge about HWT for the authorities in the Nordic countries.

The momentum around openEHR is on the rise. And we will remember the year 2022 as the year when all the big players in the healthcare landscape have embraced openEHR as the standard for data persistence. Microsoft, Dedalus, EY, and many many more have joined the openEHR community and as EY stated in its latest document, openEHR is the only international open health data standard that can cater for the complexity of health semantics and is designed for persistence.
In the session, we will hear it from the co-chair of the openEHR International, an organisation that is actively promoting lifelong, patient‑centric shared health records, future‑proof data, and clinical process support. Tomaž Gornik is an openEHR visionary who has been promoting openEHR and other open standards for more than 20 years, and who has a proven track record of delivering innovative turn-key solutions, as well as state-of-the-art technology, architectures, and processes.
What are the benefits that the openEHR standard and a modular ecosystem based on open data bring? What can we learn from openEHR cases across Europe? How can we build on the momentum of this year and what will the future in digital healthcare bring? In his lecture, Gornik will answer all of these questions, present the concrete use-cases and lessons learned with real-world deployments, and try to explain how could openEHR benefit the healthcare system of Sweden.

This study has the overall aim to study physicians’ experiences of VCs, here focusing on their suggestions for future improvements. Twenty-two semi-structured interviews were performed with physicians working for an online healthcare provider in Sweden, and analyzed through inductive content analysis. Two themes emerged related to desired future improvements of VCs; blended care and technical innovation.

The purpose of the study is to evaluate whether clinician’s acknowledgement and adherence to Clinical Best Practice Advisories (BPA) system’s alerts improve the outcome of patients with chronic diabetes. 

Electronic health record (EHR) systems have faced criticism and debate as these systems can be complex and difficult for users to navigate, particularly for those who are not trained in their use. This can lead to errors and inefficiencies, negatively impacting patient care and user satisfaction.By contextualizing criticism and transforming it into constructive feedback, system developers can gain valuable insights and use this feedback to make reasonable improvements. Transforming criticism into constructive feedback requires contextual understanding for helping to identify specific areas where improvements may be needed. In this presentation, some typical examples are given about acquiring more concrete and actionable feedback on situations where users have been disappointed or when they felt that something could be working in a better way.One of the key benefits of contextual user feedback is that it allows healthcare organizations to gather a wide range of perspectives on the EHR system to prioritize improvements. Systematic collection of user feedback also helps organizations to identify areas where additional training or support may be needed.