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Boy K, von Rohr S, May S, Kuhn S, Schett G, Labinsky H, Knitza J, Muehlensiepen F. Pre-assessment of patients with suspected axial spondyloarthritis combining student-led clinics and telemedicine: a qualitative study. Rheumatol Int 2024; 44:663-673. [PMID: 38289350 PMCID: PMC10914903 DOI: 10.1007/s00296-023-05522-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 12/15/2023] [Indexed: 03/06/2024]
Abstract
OBJECTIVE Patients referred to rheumatologists are currently facing months of inefficient waiting time due to the increasing demand and rising workforce shortage. We piloted a pre-assessment of patients with suspected axial spondyloarthritis (axSpA) combining student-led clinics and telemedicine (symptom assessment, symptom monitoring and at-home capillary self-sampling) to improve access to rheumatology care. The aim of this study was to explore (1) current challenges accessing axSpA care and (2) patients' first-hand experiences. METHODS Embedded within a clinical trial, this study was based on qualitative interviews with patients with suspected axSpA (n = 20). Data was analysed via qualitative content analysis. RESULTS Student-led clinics were perceived as high-quality care, comparable to conventional rheumatologist-led visits. Patients expressed that their interactions with the students instilled a sense of trust. History-taking and examinations were perceived as comprehensive and meticulous. Telehealth tools were seen as empowering, offering immediate and continuous access to symptom assessment at home. Patients reported a lack of specificity of the electronic questionnaires, impeding accurate responses. Patients requested a comments area to supplement questionnaire responses. Some patients reported receiving help to complete the blood collection. CONCLUSION Patients' access to rheumatology care is becoming increasingly burdensome. Pre-assessment including student-led clinics and telemedicine was highly accepted by patients. Patient interviews provided valuable in-depth feedback to improve the piloted patient pathway.
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Affiliation(s)
- Katharina Boy
- Center for Health Services Research, Faculty of Health Sciences Brandenburg, Brandenburg Medical School Theodor Fontane, Seebad 82/83, 15562, Rüdersdorf Bei Berlin, Germany.
| | - Sophie von Rohr
- Department of Internal Medicine 3, Rheumatology and Immunology Friedrich, Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Susann May
- Center for Health Services Research, Faculty of Health Sciences Brandenburg, Brandenburg Medical School Theodor Fontane, Seebad 82/83, 15562, Rüdersdorf Bei Berlin, Germany
| | - Sebastian Kuhn
- Institute for Digital Medicine, University Hospital of Giessen and Marburg, Philipps University Marburg, Marburg, Germany
| | - Georg Schett
- Department of Internal Medicine 3, Rheumatology and Immunology Friedrich, Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Hannah Labinsky
- Department of Internal Medicine 3, Rheumatology and Immunology Friedrich, Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Department of Internal Medicine 2, Rheumatology/Clinical Immunology, University Hospital Würzburg, Würzburg, Germany
| | - Johannes Knitza
- Department of Internal Medicine 3, Rheumatology and Immunology Friedrich, Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Institute for Digital Medicine, University Hospital of Giessen and Marburg, Philipps University Marburg, Marburg, Germany
- AGEIS, Université Grenoble Alpes, Grenoble, France
| | - Felix Muehlensiepen
- Center for Health Services Research, Faculty of Health Sciences Brandenburg, Brandenburg Medical School Theodor Fontane, Seebad 82/83, 15562, Rüdersdorf Bei Berlin, Germany
- AGEIS, Université Grenoble Alpes, Grenoble, France
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Sellin J, Pantel JT, Börsch N, Conrad R, Mücke M. [Short paths to diagnosis with artificial intelligence: systematic literature review on diagnostic decision support systems]. Schmerz 2024; 38:19-27. [PMID: 38165492 DOI: 10.1007/s00482-023-00777-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/24/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND Rare diseases are often recognized late. Their diagnosis is particularly challenging due to the diversity, complexity and heterogeneity of clinical symptoms. Computer-aided diagnostic aids, often referred to as diagnostic decision support systems (DDSS), are promising tools for shortening the time to diagnosis. Despite initial positive evaluations, DDSS are not yet widely used, partly due to a lack of integration with existing clinical or practice information systems. OBJECTIVE This article provides an insight into currently existing diagnostic support systems that function without access to electronic patient records and only require information that is easily obtainable. MATERIALS AND METHODS A systematic literature search identified eight articles on DDSS that can assist in the diagnosis of rare diseases with no need for access to electronic patient records or other information systems in practices and hospitals. The main advantages and disadvantages of the identified rare disease diagnostic support systems were extracted and summarized. RESULTS Symptom checkers and DDSS based on portrait photos and pain drawings already exist. The degree of maturity of these applications varies. CONCLUSION DDSS currently still face a number of challenges, such as concerns about data protection and accuracy, and acceptance and awareness continue to be rather low. On the other hand, there is great potential for faster diagnosis, especially for rare diseases, which are easily overlooked due to their large number and the low awareness of them. The use of DDSS should therefore be carefully considered by doctors on a case-by-case basis.
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Affiliation(s)
- Julia Sellin
- Institut für Digitale Allgemeinmedizin, Universitätsklinikum RWTH Aachen, Aachen, Deutschland.
- Zentrum für Seltene Erkrankungen Aachen (ZSEA), Universitätsklinikum RWTH Aachen, Aachen, Deutschland.
| | - Jean Tori Pantel
- Institut für Digitale Allgemeinmedizin, Universitätsklinikum RWTH Aachen, Aachen, Deutschland
- Zentrum für Seltene Erkrankungen Aachen (ZSEA), Universitätsklinikum RWTH Aachen, Aachen, Deutschland
| | - Natalie Börsch
- Institut für Digitale Allgemeinmedizin, Universitätsklinikum RWTH Aachen, Aachen, Deutschland
- Zentrum für Seltene Erkrankungen Aachen (ZSEA), Universitätsklinikum RWTH Aachen, Aachen, Deutschland
| | - Rupert Conrad
- Klinik für Psychosomatische Medizin und Psychotherapie, Universitätsklinikum Münster, Münster, Deutschland
| | - Martin Mücke
- Institut für Digitale Allgemeinmedizin, Universitätsklinikum RWTH Aachen, Aachen, Deutschland
- Zentrum für Seltene Erkrankungen Aachen (ZSEA), Universitätsklinikum RWTH Aachen, Aachen, Deutschland
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Knitza J, Kuhn S. [Digital rheumatology]. Inn Med (Heidelb) 2023; 64:1023-1024. [PMID: 37843578 DOI: 10.1007/s00108-023-01605-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/19/2023] [Indexed: 10/17/2023]
Abstract
Chronic inflammatory rheumatic diseases mostly run an undulating course and with unspecific symptoms. The initial clarification and timely initiation of treatment are challenging, which is additionally exacerbated by the lack of specialized physicians. Digital approaches, including artificial intelligence (AI), should be of assistance and enable an improved, personalized and needs-based treatment; however, the evidence is currently still very limited. This article provides a compact overview of the current state of digital rheumatology.
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Affiliation(s)
- Johannes Knitza
- Institut für Digitalisierung in der Medizin, Universitätsklinikum Gießen und Marburg, Philipps-Universität Marburg, Baldingerstr., 35043, Marburg, Deutschland.
| | - Sebastian Kuhn
- Institut für Digitalisierung in der Medizin, Universitätsklinikum Gießen und Marburg, Philipps-Universität Marburg, Baldingerstr., 35043, Marburg, Deutschland
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Marco-Ruiz L, Bønes E, de la Asunción E, Gabarron E, Aviles-Solis JC, Lee E, Traver V, Sato K, Bellika JG. Combining multivariate statistics and the think-aloud protocol to assess Human-Computer Interaction barriers in symptom checkers. J Biomed Inform 2017; 74:104-122. [PMID: 28893671 DOI: 10.1016/j.jbi.2017.09.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2017] [Revised: 08/28/2017] [Accepted: 09/04/2017] [Indexed: 10/18/2022]
Abstract
Symptom checkers are software tools that allow users to submit a set of symptoms and receive advice related to them in the form of a diagnosis list, health information or triage. The heterogeneity of their potential users and the number of different components in their user interfaces can make testing with end-users unaffordable. We designed and executed a two-phase method to test the respiratory diseases module of the symptom checker Erdusyk. Phase I consisted of an online test with a large sample of users (n=53). In Phase I, users evaluated the system remotely and completed a questionnaire based on the Technology Acceptance Model. Principal Component Analysis was used to correlate each section of the interface with the questionnaire responses, thus identifying which areas of the user interface presented significant contributions to the technology acceptance. In the second phase, the think-aloud procedure was executed with a small number of samples (n=15), focusing on the areas with significant contributions to analyze the reasons for such contributions. Our method was used effectively to optimize the testing of symptom checker user interfaces. The method allowed kept the cost of testing at reasonable levels by restricting the use of the think-aloud procedure while still assuring a high amount of coverage. The main barriers detected in Erdusyk were related to problems understanding time repetition patterns, the selection of levels in scales to record intensities, navigation, the quantification of some symptom attributes, and the characteristics of the symptoms.
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Affiliation(s)
- Luis Marco-Ruiz
- Norwegian Centre for E-health Research, University Hospital of North Norway, P.O. Box 35, N-9038 Tromsø, Norway; Department of Clinical Medicine, Faculty of Health Sciences, UIT The Arctic University of Norway, 9037 Tromsø, Norway.
| | - Erlend Bønes
- Norwegian Centre for E-health Research, University Hospital of North Norway, P.O. Box 35, N-9038 Tromsø, Norway
| | - Estela de la Asunción
- Norwegian Centre for E-health Research, University Hospital of North Norway, P.O. Box 35, N-9038 Tromsø, Norway
| | - Elia Gabarron
- Norwegian Centre for E-health Research, University Hospital of North Norway, P.O. Box 35, N-9038 Tromsø, Norway; Department of Clinical Medicine, Faculty of Health Sciences, UIT The Arctic University of Norway, 9037 Tromsø, Norway
| | - Juan Carlos Aviles-Solis
- Department of Community Medicine, Faculty of Health Sciences, UIT The Arctic University of Norway, 9037 Tromsø, Norway
| | - Eunji Lee
- SINTEF, Forskningsveien 1, 0373 Oslo, Norway
| | - Vicente Traver
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
| | - Keiichi Sato
- Institute of Design, Illinois Institute of Technology, 565 West Adams Street, Chicago, IL 60661, United States; Department of Computer Science, UIT The Arctic University of Norway, 9037 Tromsø, Norway
| | - Johan G Bellika
- Norwegian Centre for E-health Research, University Hospital of North Norway, P.O. Box 35, N-9038 Tromsø, Norway; Department of Clinical Medicine, Faculty of Health Sciences, UIT The Arctic University of Norway, 9037 Tromsø, Norway
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