Sessions

The integration of design thinking into medical informatics education is essential for development of innovative and effective health information technologies in our work domain. Design thinking is a problem-solving approach which focuses on understanding complex user needs and creating innovative solutions befitting those complex needs. It is a widely used approach for developing user-centered products and services, and distils creative methodologies to improve usability and effectiveness of health information systems.
This workshop discusses use cases for design thinking in medical informatics education, provides an overview of the lessons learned of incorporating design thinking into the curriculum, and will challenge the audience to experiment with two of the design thinking phases. The workshop also outlines the implications of design thinking for medical informatics educators, students and our work field, and provides suggestions for incorporating design thinking into existing medical informatics programs.

Introduction to the session "Northern Excellence in Online Records" and short presentation of the NORDeHEALTH project.

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. 

The TEHDAS Joint Action carried out country visits to map the state-of-play of national health data management systems and their preparedness to join a future EHDS for secondary use. The presentation will present the findings of 12 country visits carried out between December 2021 and December 2022, focusing on different themes (e.g., data sources, access procedures, data governance), with a focus on the overall preparedness and the needs and expectations of countries to be able to join the EHDS

OpenDataPsy offers a set of ready-to-use standardized datasets for research in psychiatry.

In this session, we describe the methodology for the NORDeHEALTH 2022 Patient Survey and present an overview of the data collected in Sweden, Norway, Finland, and Estonia, respectively. 

A demonstration of an open source software prototype for improving infection control in hospitals called Smart Infection Control System (SmICS). It provides services for tracing contacts between patients, showing in-depth details for microbiological findings, patients movements and wards and offers epidemiological curves without having a vendor-dependent data storage within the application, because it is fully based upon an open interoperability standard (openEHR).

Patient-accessible electronic health records (PAEHR) have the potential to empower patients, but their impact may vary between psychiatric and somatic patients due to unique sensitivities in medical notes. In Norway, all hospitals have implemented PAEHR since 2015. Despite ongoing debate, further research is needed on user experiences and perceptions in general health care, particularly psychiatry. In this lecture, we will explore the following topics from Norway’s perspective: 1) how the use of PAEHR differs between psychiatric and somatic patients, 2) the serious mistakes patients have perceived and their impact on each group, and 3) the implications for practice in the era of transparent EHR and new ways of thinking.

Digital health technologies (DHTs) have a great potential to help citizens and health professionals address preventable risk factors associated with diseases and illnesses that shorten human lives. DHTs can facilitate early detection, treatment, management of chronic conditions and support healthy ageing. DHTs also make it possible to shift from reactive to active and holistic delivery of health care services by empowering citizens in accessing their personal health data, monitoring, and managing their own health, based on their own preferences.

Age-related macular degeneration (AMD) is a major cause of blindness in the West. This study utilizes spectral domain optical coherence tomography (SD-OCT) for retinal imaging and deep learning for analysis. A convolutional neural network (CNN) was trained on 1300 expert-annotated SD-OCT scans to identify AMD biomarkers. Transfer learning improved the CNN's performance using weights from a separate classifier. The model accurately detects and segments AMD biomarkers in OCT scans, potentially assisting in patient prioritization and reducing ophthalmologists' workloads.

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.