scholarly journals Recommended Practices for Computerized Clinical Decision Support and Knowledge Management in Community Settings: A Qualitative Study

2013 ◽  
pp. 197-236
Keyword(s):  
Knowledge Management ◽  
Decision Support ◽  
Qualitative Study ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Community Settings ◽  
Recommended Practices

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 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 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.

A pilot study of distributed knowledge management and clinical decision support in the cloud

2013 ◽  
Vol 59 (1) ◽  
pp. 45-53 ◽  
Author(s):  
Brian E. Dixon ◽  
Linas Simonaitis ◽  
Howard S. Goldberg ◽  
Marilyn D. Paterno ◽  
Molly Schaeffer ◽  
...  
Keyword(s):  
Knowledge Management ◽  
Decision Support ◽  
Pilot Study ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Distributed Knowledge
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