ArticleBMC medical informatics and decision making2021
Evaluation of a clinical decision support system for rare diseases: a qualitative study.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Development of the ERATbi App, a Clinical Decision Support System for Early Recovery After Traumatic Brain Injury in the ICU: Usability Study.JMIR human factors · 2026Article
- Evaluating algorithmic approaches to rare disease case-finding: a retrospective validation study using electronic health records.Orphanet journal of rare diseases · 2026Article
- The impact of rare diseases on the quality of life in paediatric patients: current status.Frontiers in public health · 2025Review
- Usability assessments in clinical decision support systems.Northern clinics of Istanbul · 2025Article
- Evaluating and Enhancing the Fitness-for-Purpose of Electronic Health Record Data: Qualitative Study on Current Practices and Pathway to an Automated Approach Within the Medical Informatics for Research and Care in University Medicine Consortium.JMIR medical informatics · 2024Article
- Gynaecological Artificial Intelligence Diagnostics (GAID) GAID and Its Performance as a Tool for the Specialist Doctor.Healthcare (Basel, Switzerland) · 2024Article
- Web-based eHealth Clinical Decision Support System as a tool for the treat-to-target management of patients with systemic lupus erythematosus:BMJ health & care informatics · 2023Article
- Requirements analysis for an AI-based clinical decision support system for general practitioners: a user-centered design process.BMC medical informatics and decision making · 2023Article
- Ten Topics to Get Started in Medical Informatics Research.Journal of medical Internet research · 2023Article
- Systematic review of the performance evaluation of clinicians with or without the aid of machine learning clinical decision support system.Health and technology · 2023Review
- A Visual Analytic Tool (VIADS) to Assist the Hypothesis Generation Process in Clinical Research: Mixed Methods Usability Study.JMIR human factors · 2023Article
- Design, implementation, and evaluation of the computer-aided clinical decision support system based on learning-to-rank: collaboration between physicians and machine learning in the differential diagnosis process.BMC medical informatics and decision making · 2023Article
- Usability of the IDDEAS prototype in child and adolescent mental health services: A qualitative study for clinical decision support system development.Frontiers in psychiatry · 2023Article
- Evaluation of an Electronic Medical Record Module for Nursing Documentation in Paediatric Palliative Care: Involvement of Nurses with a Think-Aloud Approach.International journal of environmental research and public health · 2022Observational
- Participatory Design of a Medication Module in an Electronic Medical Record for Paediatric Palliative Care: A Think-Aloud Approach with Nurses and Physicians.Children (Basel, Switzerland) · 2022Article
- [What is confirmed in the diagnostics of autoinflammatory fever diseases?]Der Internist · 2021Review
- [se-atlas.de-Medical care atlas for people with rare diseases].Der Internist · 2021Review
- Participatory Design of an Electronic Medical Record for Paediatric Palliative Care: A Think-Aloud Study with Nurses and Physicians.Children (Basel, Switzerland) · 2021Article
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Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRare 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.
methodsWe 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).
resultsA 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.
conclusionsThis 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.
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