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Interviews with experts in rare diseases for the development of clinical decision support system software - a qualitative study

BACKGROUND: Patients with rare diseases (RDs) are often diagnosed too late or not at all. Clinical decision support systems (CDSSs) could support the diagnosis in RDs. The MIRACUM (Medical Informatics in Research and Medicine) consortium, which is one of four funded consortia in the German Medical I...

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Autores principales: Schaaf, Jannik, Prokosch, Hans-Ulrich, Boeker, Martin, Schaefer, Johanna, Vasseur, Jessica, Storf, Holger, Sedlmayr, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7493382/
https://www.ncbi.nlm.nih.gov/pubmed/32938448
http://dx.doi.org/10.1186/s12911-020-01254-3
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author Schaaf, Jannik
Prokosch, Hans-Ulrich
Boeker, Martin
Schaefer, Johanna
Vasseur, Jessica
Storf, Holger
Sedlmayr, Martin
author_facet Schaaf, Jannik
Prokosch, Hans-Ulrich
Boeker, Martin
Schaefer, Johanna
Vasseur, Jessica
Storf, Holger
Sedlmayr, Martin
author_sort Schaaf, Jannik
collection PubMed
description BACKGROUND: Patients with rare diseases (RDs) are often diagnosed too late or not at all. Clinical decision support systems (CDSSs) could support the diagnosis in RDs. The MIRACUM (Medical Informatics in Research and Medicine) consortium, which is one of four funded consortia in the German Medical Informatics Initiative, will develop a CDSS for RDs based on distributed clinical data from ten university hospitals. This qualitative study aims to investigate (1) the relevant organizational conditions for the operation of a CDSS for RDs when diagnose patients (e.g. the diagnosis workflow), (2) which data is necessary for decision support, and (3) the appropriate user group for such a CDSS. METHODS: Interviews were carried out with RDs experts. Participants were recruited from staff physicians at the Rare Disease Centers (RDCs) at the MIRACUM locations, which offer diagnosis and treatment of RDs. An interview guide was developed with a category-guided deductive approach. The interviews were recorded on an audio device and then transcribed into written form. We continued data collection until all interviews were completed. Afterwards, data analysis was performed using Mayring’s qualitative content analysis approach. RESULTS: A total of seven experts were included in the study. The results show that medical center guides and physicians from RDC B-centers (with a focus on different RDs) are involved in the diagnostic process. Furthermore, interdisciplinary case discussions between physicians are conducted. The experts explained that RDs exist which cannot be fully differentiated, but rather described only by their overall symptoms or findings: diagnosis is dependent on the disease or disease group. At the end of the diagnostic process, most centers prepare a summary of the patient case. Furthermore, the experts considered both physicians and experts from the B-centers to be potential users of a CDSS. The experts also have different experiences with CDSS for RDs. CONCLUSIONS: This qualitative study is a first step towards establishing the requirements for the development of a CDSS for RDs. Further research is necessary to create solutions by also including the experts on RDs.
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spelling pubmed-74933822020-09-16 Interviews with experts in rare diseases for the development of clinical decision support system software - a qualitative study Schaaf, Jannik Prokosch, Hans-Ulrich Boeker, Martin Schaefer, Johanna Vasseur, Jessica Storf, Holger Sedlmayr, Martin BMC Med Inform Decis Mak Research Article BACKGROUND: Patients with rare diseases (RDs) are often diagnosed too late or not at all. Clinical decision support systems (CDSSs) could support the diagnosis in RDs. The MIRACUM (Medical Informatics in Research and Medicine) consortium, which is one of four funded consortia in the German Medical Informatics Initiative, will develop a CDSS for RDs based on distributed clinical data from ten university hospitals. This qualitative study aims to investigate (1) the relevant organizational conditions for the operation of a CDSS for RDs when diagnose patients (e.g. the diagnosis workflow), (2) which data is necessary for decision support, and (3) the appropriate user group for such a CDSS. METHODS: Interviews were carried out with RDs experts. Participants were recruited from staff physicians at the Rare Disease Centers (RDCs) at the MIRACUM locations, which offer diagnosis and treatment of RDs. An interview guide was developed with a category-guided deductive approach. The interviews were recorded on an audio device and then transcribed into written form. We continued data collection until all interviews were completed. Afterwards, data analysis was performed using Mayring’s qualitative content analysis approach. RESULTS: A total of seven experts were included in the study. The results show that medical center guides and physicians from RDC B-centers (with a focus on different RDs) are involved in the diagnostic process. Furthermore, interdisciplinary case discussions between physicians are conducted. The experts explained that RDs exist which cannot be fully differentiated, but rather described only by their overall symptoms or findings: diagnosis is dependent on the disease or disease group. At the end of the diagnostic process, most centers prepare a summary of the patient case. Furthermore, the experts considered both physicians and experts from the B-centers to be potential users of a CDSS. The experts also have different experiences with CDSS for RDs. CONCLUSIONS: This qualitative study is a first step towards establishing the requirements for the development of a CDSS for RDs. Further research is necessary to create solutions by also including the experts on RDs. BioMed Central 2020-09-16 /pmc/articles/PMC7493382/ /pubmed/32938448 http://dx.doi.org/10.1186/s12911-020-01254-3 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Schaaf, Jannik
Prokosch, Hans-Ulrich
Boeker, Martin
Schaefer, Johanna
Vasseur, Jessica
Storf, Holger
Sedlmayr, Martin
Interviews with experts in rare diseases for the development of clinical decision support system software - a qualitative study
title Interviews with experts in rare diseases for the development of clinical decision support system software - a qualitative study
title_full Interviews with experts in rare diseases for the development of clinical decision support system software - a qualitative study
title_fullStr Interviews with experts in rare diseases for the development of clinical decision support system software - a qualitative study
title_full_unstemmed Interviews with experts in rare diseases for the development of clinical decision support system software - a qualitative study
title_short Interviews with experts in rare diseases for the development of clinical decision support system software - a qualitative study
title_sort interviews with experts in rare diseases for the development of clinical decision support system software - a qualitative study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7493382/
https://www.ncbi.nlm.nih.gov/pubmed/32938448
http://dx.doi.org/10.1186/s12911-020-01254-3
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