Refine
Year of publication
- 2021 (3) (remove)
Document Type
- Article (3)
Has Fulltext
- yes (3)
Is part of the Bibliography
- no (3)
Keywords
- Rare diseases (3) (remove)
Institute
- Medizin (3)
Background: Rare Diseases (RDs) are difficult to diagnose. Clinical Decision Support Systems (CDSS) could support the diagnosis for RDs. The Medical Informatics in Research and Medicine (MIRACUM) consortium developed a CDSS for RDs based on distributed clinical data from eight German university hospitals. To support the diagnosis for difficult patient cases, the CDSS uses data from the different hospitals to perform a patient similarity analysis to obtain an indication of a diagnosis. To optimize our CDSS, we conducted a qualitative study to investigate usability and functionality of our designed CDSS. Methods: We performed a Thinking Aloud Test (TA-Test) with RDs experts working in Rare Diseases Centers (RDCs) at MIRACUM locations which are specialized in diagnosis and treatment of RDs. An instruction sheet with tasks was prepared that the participants should perform with the CDSS during the study. The TA-Test was recorded on audio and video, whereas the resulting transcripts were analysed with a qualitative content analysis, as a ruled-guided fixed procedure to analyse text-based data. Furthermore, a questionnaire was handed out at the end of the study including the System Usability Scale (SUS). Results: A total of eight experts from eight MIRACUM locations with an established RDC were included in the study. Results indicate that more detailed information about patients, such as descriptive attributes or findings, can help the system perform better. The system was rated positively in terms of functionality, such as functions that enable the user to obtain an overview of similar patients or medical history of a patient. However, there is a lack of transparency in the results of the CDSS patient similarity analysis. The study participants often stated that the system should present the user with an overview of exact symptoms, diagnosis, and other characteristics that define two patients as similar. In the usability section, the CDSS received a score of 73.21 points, which is ranked as good usability. Conclusions: This qualitative study investigated the usability and functionality of a CDSS of RDs. Despite positive feedback about functionality of system, the CDSS still requires some revisions and improvement in transparency of the patient similarity analysis.
Background: About 30 million people in the EU and USA, respectively, suffer from a rare disease. Driven by European legislative requirements, national strategies for the improvement of care in rare diseases are being developed. To improve timely and correct diagnosis for patients with rare diseases, the development of a registry for undiagnosed patients was recommended by the German National Action Plan. In this paper we focus on the question on how such a registry for undiagnosed patients can be built and which information it should contain. Results: To develop a registry for undiagnosed patients, a software for data acquisition and storage, an appropriate data set and an applicable terminology/classification system for the data collected are needed. We have used the open-source software Open-Source Registry System for Rare Diseases (OSSE) to build the registry for undiagnosed patients. Our data set is based on the minimal data set for rare disease patient registries recommended by the European Rare Disease Registries Platform. We extended this Common Data Set to also include symptoms, clinical findings and other diagnoses. In order to ensure findability, comparability and statistical analysis, symptoms, clinical findings and diagnoses have to be encoded. We evaluated three medical ontologies (SNOMED CT, HPO and LOINC) for their usefulness. With exact matches of 98% of tested medical terms, a mean number of five deposited synonyms, SNOMED CT seemed to fit our needs best. HPO and LOINC provided 73% and 31% of exacts matches of clinical terms respectively. Allowing more generic codes for a defined symptom, with SNOMED CT 99%, with HPO 89% and with LOINC 39% of terms could be encoded. Conclusions: With the use of the OSSE software and a data set, which, in addition to the Common Data Set, focuses on symptoms and clinical findings, a functioning and meaningful registry for undiagnosed patients can be implemented. The next step is the implementation of the registry in centres for rare diseases. With the help of medical informatics and big data analysis, case similarity analyses could be realized and aid as a decision-support tool enabling diagnosis of some undiagnosed patients.
Eine Erkrankung zählt in der Europäischen Union zu den Seltenen Erkrankungen (SE), wenn diese nicht mehr als 5 von 10.000 Menschen betrifft. Derzeit existiert mit mehr als 6000 SE eine sowohl große als auch heterogene Menge an unterschiedlichen Krankheitsbilder, die in ihrer Symptomatik komplex, vielschichtig und damit im medizinischen Alltag schwierig einzuordnen sind. Dies erschwert Diagnosefindung und Behandlung sowie das Auffinden eines passenden Ansprechpartners, da es nur wenige Experten für jede einzelne SE gibt. Der medizinische Versorgungsatlas für Seltene Erkrankungen www.se-atlas.de ermöglicht anhand von Erkrankungsnamen die Suche nach Versorgungseinrichtungen und Selbsthilfeorganisationen zu bestimmten SE und stellt die Suchergebnisse geografisch dar. Ebenso gibt er einen Überblick über alle deutschen Zentren für SE, die eine Anlaufstelle für betroffene Personen mit unklarer Diagnose darstellen. Der se-atlas dient als Kompass durch die heterogene Menge an Informationen über Versorgungseinrichtungen für SE und stellt niederschwellig Informationen für eine breite Nutzergruppe von Betroffenen bis hin zu Mitgliedern des medizinischen Versorgungsteams bereit.