scholarly journals Architectures Integrating Case-Based Reasoning and Bayesian Networks for Clinical Decision Support

2010 ◽  
pp. 82-91 ◽  
Author(s):  
Tore Bruland ◽  
Agnar Aamodt ◽  
Helge Langseth
Keyword(s):  
Decision Support ◽  
Bayesian Networks ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Case Based Reasoning ◽  
Case Based

scholarly journals The acceptance of clinical decision support systems among clinicians in the treatment of neck and/or back pain

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
S M Jansen-Kosterink ◽  
M Cabrita ◽  
I Flierman
Keyword(s):  
Back Pain ◽  
Decision Support ◽  
Focus Groups ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Case Based Reasoning ◽  
Online Questionnaire

Abstract Background Clinical Decision Support Systems (CDSSs) are computerized systems using case-based reasoning to assist clinicians in making clinical decisions. Despite the proven added value to public health, the implementation of CDSS clinical practice is scarce. Particularly, little is known about the acceptance of CDSS among clinicians. Within the Back-UP project (Project Number: H2020-SC1-2017-CNECT-2-777090) a CDSS is developed with prognostic models to improve the management of Neck and/or Low Back Pain (NLBP). Therefore, the aim of this study is to present the factors involved in the acceptance of CDSSs among clinicians. Methods To assess the acceptance of CDSSs among clinicians we conducted a mixed method analysis of questionnaires and focus groups. An online questionnaire with a low-fidelity prototype of a CDSS (TRL3) was sent to Dutch clinicians aimed to identify the factors influencing the acceptance of CDSSs (intention to use, perceived threat to professional autonomy, trusting believes and perceived usefulness). Next to this, two focus groups were conducted with clinicians addressing the general attitudes towards CDSSs, the factors determining the level of acceptance, and the conditions to facilitate use of CDSSs. Results A pilot-study of the online questionnaire is completed and the results of the large evaluation are expected spring 2020. Eight clinicians participated in two focus groups. After being introduced to various types of CDSSs, participants were positive about the value of CDSS in the care of NLBP. The clinicians agreed that the human touch in NLBP care must be preserved and that CDSSs must remain a supporting tool, and not a replacement of their role as professionals. Conclusions By identifying the factors hindering the acceptance of CDSSs we can draw implications for implementation of CDSSs in the treatment of NLBP.

Improving clinical decision support through case-based data fusion

1999 ◽  
Vol 46 (10) ◽  
pp. 1181-1185 ◽  
Author(s):  
F. Azuaje ◽  
W. Dubitzky ◽  
N. Black ◽  
K. Adamson
Keyword(s):  
Decision Support ◽  
Data Fusion ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Case Based

scholarly journals Bayesian Networks for Clinical Decision Support in Lung Cancer Care

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e82349 ◽  
Author(s):  
M. Berkan Sesen ◽  
Ann E. Nicholson ◽  
Rene Banares-Alcantara ◽  
Timor Kadir ◽  
Michael Brady
Keyword(s):  
Lung Cancer ◽  
Decision Support ◽  
Bayesian Networks ◽  
Clinical Decision Support ◽  
Cancer Care ◽  
Clinical Decision
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