The goal of this study is to explore the different attitudes and preconditions of potential end-users (doctors \& physicians in training) required to achieve successful clinical implementation of models based on artificial intelligence (i.e. both machine learning and knowledge-driven techniques) as clinical decision support software.
Study Type
OBSERVATIONAL
Enrollment
69
Survey to acquire baseline demographic information as well as information regarding professional experience, working environment and attitudes towards artificial intelligence.
Semi-structured group discussion.
OLV Aalst
Aalst, Belgium
ZNA Ziekenhuizen
Antwerp, Belgium
Ghent University Hospital
Ghent, Belgium
Baseline attitudes towards artificial intelligence and big data in medicine
Baseline attitudes towards artificial intelligence and big data in medicine will be collected through an online survey where participants will score their agreement with certain statements on a 6-point likert scale (Possible choices: Strongly agree - Agree - Neutral - Disagree - Totally Disagree - Not applicable).
Time frame: baseline
Identify subdomains of the antimicrobial stewardship cycle with potential for AI/Big data application
Identify subdomains of the antimicrobial stewardship cycle for which participants think AI/Big data might be of use through a group discussion/interview. Reporting: frequencies.
Time frame: through study completion, an average of 1 year
Identify perceived potential benefits and harms when applying AI in the antimicrobial stewardship cycle.
Identify perceived potential benefits and harms when applying AI in the antimicrobial stewardship cycle through a group discussion. Reporting: frequencies.
Time frame: through study completion, an average of 1 year
Identify prerequisites that need to be fulfilled when AI/Big data based clinical decision support systems are used bedside from the viewpoint of the participants.
Identify prerequisites that need to be fulfilled when AI/Big data based clinical decision support systems are used bedside and identify the most important ones for different aspects of the antimicrobial stewardship cycle from the viewpoint of the participants through a group discussion. Reporting: frequencies.
Time frame: through study completion, an average of 1 year
Subgroup analysis: age
Explore if there are variations in the above mentioned outcomes when taking into account the age (years) of the participants.
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Time frame: through study completion, an average of 1 year
Subgroup analysis: gender
Explore if there are variations in the above mentioned outcomes when taking into account the gender of the participants.
Time frame: through study completion, an average of 1 year
Subgroup analysis: working environment (type of hospital, type of ICU)
Explore if there are variations in the above mentioned outcomes when taking into account the working environment (University hospital vs non University hospital, small size hospital vs large size hospital, type of ICU (medical, surgery, mixed ICU, intermediate care)) - data which is collected in the baseline questionnaire) of the participants.
Time frame: through study completion, an average of 1 year
Subgroup analysis: working experience (basic training and clinical experience).
Explore if there are variations in the above mentioned outcomes when taking into account the working experience (type of basic training (anesthesiology, internal medicine, surgery, other), clinical experience (years) - data which is collected in the baseline questionnaire) of the participants.
Time frame: through study completion, an average of 1 year