scholarly journals Evaluation of a clinical decision support system for rare diseases: a qualitative study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jannik Schaaf ◽  
Martin Sedlmayr ◽  
Brita Sedlmayr ◽  
Hans-Ulrich Prokosch ◽  
Holger Storf
Keyword(s):  
Decision Support ◽  
Qualitative Study ◽  
Clinical Decision Support ◽  
Rare Diseases ◽  
Qualitative Content Analysis ◽  
Clinical Decision ◽  
Similarity Analysis ◽  
Difficult Patient ◽  
Study Results ◽  
System Usability Scale

Abstract 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.

scholarly journals Evaluation of a Clinical Decision Support System for Rare Diseases - A Qualitative Study

2020 ◽  
Author(s):  
Jannik Schaaf ◽  
Martin Sedlmayr ◽  
Brita Sedlmayr ◽  
Hans-Ulrich Prokosch ◽  
Holger Storf
Keyword(s):  
Decision Support ◽  
Qualitative Study ◽  
Clinical Decision Support ◽  
Rare Diseases ◽  
Qualitative Content Analysis ◽  
Clinical Decision ◽  
Similarity Analysis ◽  
University Hospitals ◽  
Difficult Patient ◽  
System Usability Scale

Abstract BackgroundRare Diseases (RDs) are difficult to diagnose. Clinical Decision Support Systems (CDSS) could support the diagnosis for RDs. The MIRACUM (Medical Informatics in Research and Medicine) consortium developed a CDSS for RDs based on distributed clinical data from ten 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 in order to obtain an indication of a diagnosis. To optimize our CDSS, we conducted this qualitative study to investigate the usability of the CDSS with its functionality and information included. Methods A Thinking Aloud Test (TA-Test) was performed with RDs experts recruited from Rare Diseases Centres (RDCs) at the MIRACUM locations which were specialized in the diagnosis and treatment of RDs.An instruction sheet with tasks was prepared that the participants should perform with the CDSS during the study. Participants were asked to share any thoughts about the CDSS. The TA-Test was recorded on audio and video. A questionnaire was handed out at the end of the study including the System Usability Scale (SUS). Afterwards, the data was analysed with the qualitative content analysis according to Mayring, which includes a category-guided deductive approach.ResultsA total of eight experts were included in the study since eight MIRACUM locations have established an RDC.The results show that more detailed information about the patients, such as descriptive attributes or findings, are needed. The given functionality of the CDSS was rated positively, such as the function for the overview of similar patients and medical history. However, there is a lack of transparency regarding the results of the CDSS patient similarity analysis. The participants stated that the system should present exactly which symptoms, diagnosis etc. have matched. Regarding usability, the CDSS received a score of 73.21 points according to the SUS, which is ranked as a good usability.ConclusionsThis qualitative study investigated the usability of a CDSS of RDs. Despite the promising results, the CDSS still needs some revisions before use in clinical practice, e.g. by improving the transparency of the patient similarity analysis.

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

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Jannik Schaaf ◽  
Hans-Ulrich Prokosch ◽  
Martin Boeker ◽  
Johanna Schaefer ◽  
Jessica Vasseur ◽  
...  
Keyword(s):  
Decision Support ◽  
Qualitative Study ◽  
Medical Informatics ◽  
Clinical Decision Support ◽  
Rare Diseases ◽  
Qualitative Content Analysis ◽  
Medical Center ◽  
Clinical Decision ◽  
Diagnostic Process ◽  
University Hospitals

Abstract 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.

scholarly journals Interviews with experts in Rare Diseases for the development of Clinical Decision Support System software - a qualitative study

2020 ◽  
Author(s):  
Jannik Schaaf ◽  
Hans-Ulrich Prokosch ◽  
Martin Boeker ◽  
Johanna Schaefer ◽  
Jessica Vasseur ◽  
...  
Keyword(s):  
Decision Support ◽  
Qualitative Study ◽  
Medical Informatics ◽  
Clinical Decision Support ◽  
Rare Diseases ◽  
Qualitative Content Analysis ◽  
Medical Center ◽  
Clinical Decision ◽  
Diagnostic Process ◽  
University Hospitals

Abstract 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.

scholarly journals Interviews with experts in Rare Diseases for the development of Clinical Decision Support System software - a qualitative study

2020 ◽  
Author(s):  
Jannik Schaaf ◽  
Hans-Ulrich Prokosch ◽  
Martin Boeker ◽  
Johanna Schaefer ◽  
Jessica Vasseur ◽  
...  
Keyword(s):  
Decision Support ◽  
Qualitative Study ◽  
Rare Disease ◽  
Medical Informatics ◽  
Clinical Decision Support ◽  
Rare Diseases ◽  
Qualitative Content Analysis ◽  
Clinical Decision ◽  
Diagnostic Process ◽  
Medical Specialties

Abstract Background Patients with a rare disease (RD) are often diagnosed too late or not at all. Clinical decision support systems (CDSSs) could support the diagnostic process in rare diseases (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, (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, from seven of the eight MIRACUM locations which have established an RDC. The results show that administrative staff and physicians from RDC B-centers, representing different medical specialties, are involved in the diagnostic process. The experts cited various software programs used for diagnostic support and considered both physicians and experts from the B-centers to be potential users of a CDSS. Furthermore, 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. Conclusions This qualitative study is a first step towards establishing the requirements for the development of a CDSS for RDs. However, further research is necessary to create solutions by also including the experts on RDs.

scholarly journals Interviews with experts in Rare Diseases for the development of Clinical Decision Support System software - a qualitative study

2020 ◽  
Author(s):  
Jannik Schaaf ◽  
Hans-Ulrich Prokosch ◽  
Martin Boeker ◽  
Johanna Schaefer ◽  
Jessica Vasseur ◽  
...  
Keyword(s):  
Decision Support ◽  
Qualitative Study ◽  
Medical Informatics ◽  
Clinical Decision Support ◽  
Rare Diseases ◽  
Qualitative Content Analysis ◽  
Medical Center ◽  
Clinical Decision ◽  
Diagnostic Process ◽  
Medical Specialties

Abstract BackgroundPatients 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.MethodsInterviews 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.ResultsA total of seven experts were included in the study. The results show that medical center guides and physicians from RDC B-centers are involved in the diagnostic process. B-centers are part of an RDC, representing different medical specialties with a focus on RDs (e.g. rare lung diseases). Furthermore, interdisciplinary case discussions between physicians are conducted to assess patient cases. 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.ConclusionsThis 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.

scholarly journals Barriers to Use of Clinical Decision Support for the Evaluation of Pulmonary Embolism: Results of a Qualitative Study (Preprint)

10.2196/25046 ◽  
2020 ◽  
Author(s):  
Safiya Richardson ◽  
Katherine Dauber-Decker ◽  
Thomas McGinn ◽  
Douglas Barnaby ◽  
Adithya Cattamanchi ◽  
...  
Keyword(s):  
Pulmonary Embolism ◽  
Decision Support ◽  
Qualitative Study ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Barriers To Use

scholarly journals Diagnosis of Rare Diseases: a scoping review of clinical decision support systems

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Jannik Schaaf ◽  
Martin Sedlmayr ◽  
Johanna Schaefer ◽  
Holger Storf
Keyword(s):  
Decision Support ◽  
Data Integration ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Data ◽  
Rare Diseases ◽  
Scoping Review ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

Abstract Background Rare Diseases (RDs), which are defined as diseases affecting no more than 5 out of 10,000 people, are often severe, chronic and life-threatening. A main problem is the delay in diagnosing RDs. Clinical decision support systems (CDSSs) for RDs are software systems to support clinicians in the diagnosis of patients with RDs. Due to their clinical importance, we conducted a scoping review to determine which CDSSs are available to support the diagnosis of RDs patients, whether the CDSSs are available to be used by clinicians and which functionalities and data are used to provide decision support. Methods We searched PubMed for CDSSs in RDs published between December 16, 2008 and December 16, 2018. Only English articles, original peer reviewed journals and conference papers describing a clinical prototype or a routine use of CDSSs were included. For data charting, we used the data items “Objective and background of the publication/project”, “System or project name”, “Functionality”, “Type of clinical data”, “Rare Diseases covered”, “Development status”, “System availability”, “Data entry and integration”, “Last software update” and “Clinical usage”. Results The search identified 636 articles. After title and abstracting screening, as well as assessing the eligibility criteria for full-text screening, 22 articles describing 19 different CDSSs were identified. Three types of CDSSs were classified: “Analysis or comparison of genetic and phenotypic data,” “machine learning” and “information retrieval”. Twelve of nineteen CDSSs use phenotypic and genetic data, followed by clinical data, literature databases and patient questionnaires. Fourteen of nineteen CDSSs are fully developed systems and therefore publicly available. Data can be entered or uploaded manually in six CDSSs, whereas for four CDSSs no information for data integration was available. Only seven CDSSs allow further ways of data integration. thirteen CDSS do not provide information about clinical usage. Conclusions Different CDSS for various purposes are available, yet clinicians have to determine which is best for their patient. To allow a more precise usage, future research has to focus on CDSSs RDs data integration, clinical usage and updating clinical knowledge. It remains interesting which of the CDSSs will be used and maintained in the future.

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