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. However, there are commercial translation tools that have shown promising results in the field of medical terminology translation. The aim of this study is to translate selected terms of the Human Phenotype Ontology (HPO) from English into German using commercial translators. Six medical experts evaluated the translation candidates in an iterative process. The results show commercial translators, with DeepL in the lead, provide translations that are positively evaluated by experts. With a broader study scope and additional optimization techniques, commercial translators could support and facilitate the process of translating medical ontologies.

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. Five general practitioners evaluated the prototype in two workshops. The discussion of the prototype resulted in categorized suggestions with key messages for further development of the AI-based clinical decision support system, such as the integration of intelligent parameter requests. The early inclusion of different user feedback facilitated the implementation of a user interface for a user-friendly decision support system.

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. Citizens have the opportunity to discuss the progress, make suggestions regarding the app's design and data entry and contribute to the dissemination of the project. To gather feedback and experiences from the core research team, we performed an online questionnaire regarding the topics "influence and communication", "improvements and learning effect", and "satisfaction". Finally, 9 citizens of the core research team participated. The results show that the citizens are very satisfied with the design of the app, their participation opportunities and the communication in the project.

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. The objective of the project 'Smart physician portal for patients with unclear disease' (SATURN) is to employ a user-centered design process based on the requirements analysis presented in this paper to develop an artificial Intelligence (AI)-based diagnosis support system that specifically addresses the needs of German GPs. METHODS: Requirements analysis for a GP-specific diagnosis support system was conducted in an iterative process with five GPs. First, interviews were conducted to analyze current workflows and the use of digital applications in cases of diagnostic uncertainty (as-is situation). Second, we focused on collecting and prioritizing tasks to be performed by an ideal smart physician portal (to-be situation) in a workshop. We then developed a task model with corresponding user requirements. RESULTS: Numerous GP-specific user requirements were identified concerning the tasks and subtasks: performing data entry (open system, enter patient data), reviewing results (receiving and evaluating results), discussing results (with patients and colleagues), scheduling further diagnostic procedures, referring to specialists (select, contact, make appointments), and case closure. Suggested features particularly concerned the process of screening and assessing results: e.g., the system should focus more on atypical patterns of common diseases than on rare diseases only, display probabilities of differential diagnoses, ensure sources and results are transparent, and mark diagnoses that have already been ruled out. Moreover, establishing a means of using the platform to communicate with colleagues and transferring patient data directly from electronic patient records to the system was strongly recommended. CONCLUSIONS: Essential user requirements to be considered in the development and design of a diagnosis system for primary care could be derived from the analysis. They form the basis for mockup-development and system engineering.

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. METHODS: To address our research questions, we took several steps to engage citizens, especially those affected by rare diseases. This approach included the following methods: pre- and post-survey (questionnaire), two workshops with focus group discussion and a requirements analysis workshop (with user stories). RESULTS: In the pre-survey, citizens suggested 45 different rare diseases and many different disease groups to be considered in the project. Two main project topics (A) "Patient-guided documentation and data collection" (20 votes) and (B) "Exchange of experience and networking" (13 votes) were identified as priorities in the workshops and ranked in the post-survey. The requirements workshop resulted in ten user stories and six initial requirements to be implemented in the digital application. CONCLUSION: Qualitative, citizen science research can be used to collectively identify stakeholder needs, project topics and requirements for a digital application in specific areas, such as rare diseases.

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. Furthermore, we analysed which of the software-systems are publicly available, especially in the field of rare diseases, how citizens can participate using those tools and whether the usability was rated by the participants. To get insights for our project "SelEe (Seltene Erkrankungen bürgerwissenschaftlich erforschen)", which is a citizen science project in rare diseases funded by the Federal Ministry of Education and Research (BMBF), we aimed to identify projects in this research area. We searched PubMed for articles between 2011 and 2021 and performed a title- and abstract screening, as well as a full-text screening. Finally, 12 studies were identified in different research areas like public health, genetic research and infectious diseases. We could not identify any study directly associated with rare diseases. None of the studies investigated usability of those systems. Furthermore, five publicly available citizen science software-systems were identified. Three of them are general systems that allow creating, operating, managing citizen science projects and including citizens in the research process. In further investigations, we will check and compare these systems, if they are appropriate for use in our SelEe-project.

[se-atlas.de-Medical care atlas for people with rare diseases]. / se-atlas.de ­ Versorgungsatlas für Menschen mit Seltenen Erkrankungen.

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. The medical care atlas for rare diseases www.se-atlas.de enables the search for care facilities and patient organizations for specific rare diseases by disease name and presents the search results geographically. It also provides an overview of all German centers for rare diseases, which are a contact point for patients with an unclear diagnosis. The se-atlas serves as a compass through the heterogeneous amount of information on care facilities for rare diseases and provides low-threshold information for a broad user group, from affected persons to members of the medical care team.