Development and expert inspections of the user interface for a primary care decision support system.

Background: General practitioners play a unique key role in diagnosing patients with unclear diseases. Decision support systems in primary care can assist with diagnosis provided that they are efficient and user-friendly.

Objectives: The objective of this study is to develop a high-fidelity prototype of the user interface of a clinical decision support system for primary care, particularly for diagnosis support in unclear diseases, using expert inspections at an early stage of development to ensure a high level of usability.

Rare Diseases in Citizen Science - Preliminary Experiences in Developing a Personal Health App.

SelEe is a German citizen science project aiming to develop a smartphone app for a patient-managed record. The goal is to study rare diseases with the support of interested citizens and people affected by rare diseases. We established a core research team, including professional researchers (leading the project) and citizens.

Initial User-Centred Design of an AI-Based Clinical Decision Support System for Primary Care.

A clinical decision support system based on different methods of artificial intelligence (AI) can support the diagnosis of patients with unclear diseases by providing tentative diagnoses as well as proposals for further steps. In a user-centred-design process, we aim to find out how general practitioners envision the user interface of an AI-based clinical decision support system for primary care. A first user-interface prototype was developed using the task model based on user requirements from preliminary work.

Translation of Ontological Concepts from English into German Using Commercial Translation Software and Expert Evaluation.

Medical ontologies are mostly available in English. This presents a language barrier that is a limitation in research and automated processing of patient data. The manual translation of ontologies is complex and time-consuming.

Requirements analysis for an AI-based clinical decision support system for general practitioners: a user-centered design process.

Background: As the first point of contact for patients with health issues, general practitioners (GPs) are frequently confronted with patients presenting with non-specific symptoms of unclear origin. This can result in delayed, prolonged or false diagnoses. To accelerate and improve the diagnosis of diseases, clinical decision support systems would appear to be an appropriate tool.

Identifying project topics and requirements in a citizen science project in rare diseases: a participative study.

Background: Due to their low prevalence (< 5 in 10,000), rare diseases are an important area of research, with the active participation of those affected being a key factor. In the Citizen Science project "SelEe" (Researching rare diseases in a citizen science approach), citizens collaborate with researchers using a digital application, developed as part of the project together with those affected, to answer research questions on rare diseases. The aim of this study was to define the rare diseases to be considered, the project topics and the initial requirements for the implementation in a digital application.

Citizen Science in Human Medicine and the Use of Software-Systems: A Rapid Scoping Review.

Citizen science allows involving interested citizen in the entire research process in science. In the past, various citizen science projects have been performed in different research fields, especially in human medicine. We conducted a rapid scoping review to determine which citizen projects in human medicine already used software-based systems to engage citizens in the research process.

[se-atlas.de-Medical care atlas for people with rare diseases].

In the European Union a disease is classified as rare if it affects no more than 5 out of 10,000 people. Currently, there are more than 6000 rare diseases, consisting of a large and heterogeneous number of different diseases that are complex in their symptomatology, multidimensional and therefore difficult to classify in everyday medical practice. This complicates the diagnosis and treatment as well as finding a suitable contact person, as there are only a few experts for each individual rare disease.