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.

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

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).

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. 

The automatic de-identification of clinical narrative text offers efficient patient data privacy protection and eases reuse of clinical data. CliniDeID applies an ensemble method combining deep and shallow machine learning with rule-based algorithms and is released as free and open-source solution to de-identify unstructured clinical text with high accuracy. It was recently evaluated with a selection of clinical text corpora and reached high sensitivity and positive predictive value.

The MIE conference has always been a hub for innovation and cutting-edge technology. This year was no exception several demonstrators showcase their unique solutions to healthcare problems. Use the opportunity to take a closer look at some of the most striking demonstrators present at the conference, highlighting their diversity and the potential impact they could have on various healthcare-related challenges