Qualitative Research Among Physicians and Junior Doctors Into the Preconditions for Implementing a CDSS Based on AI in the ICU (KATRINA)
Qualitative Research Among Physicians and Junior Doctors Into the Preconditions for Implementing a Clinical Decision Support System (CDSS) Based on Artificial Intelligence (AI) in the ICU
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Actual)
Enrollment
Contacts and Locations
Study Locations
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Aalst, Belgium
- OLV Aalst
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Antwerpen, Belgium
- ZNA Ziekenhuizen
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Ghent, Belgium
- Ghent University Hospital
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Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
Inclusion Criteria:
- Medical specialist or specialist in training working in intensive care at the time of the study.
Exclusion Criteria:
- Age < 18 yo
Study Plan
How is the study designed?
Design Details
- Observational Models: Other
- Time Perspectives: Prospective
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Baseline attitudes towards artificial intelligence and big data in medicine
Time Frame: baseline
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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).
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baseline
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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.
Time Frame: through study completion, an average of 1 year
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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.
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through study completion, an average of 1 year
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Identify subdomains of the antimicrobial stewardship cycle with potential for AI/Big data application
Time Frame: through study completion, an average of 1 year
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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.
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through study completion, an average of 1 year
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Identify perceived potential benefits and harms when applying AI in the antimicrobial stewardship cycle.
Time Frame: through study completion, an average of 1 year
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Identify perceived potential benefits and harms when applying AI in the antimicrobial stewardship cycle through a group discussion.
Reporting: frequencies.
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through study completion, an average of 1 year
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Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Subgroup analysis: age
Time Frame: through study completion, an average of 1 year
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Explore if there are variations in the above mentioned outcomes when taking into account the age (years) of the participants.
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through study completion, an average of 1 year
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Subgroup analysis: gender
Time Frame: through study completion, an average of 1 year
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Explore if there are variations in the above mentioned outcomes when taking into account the gender of the participants.
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through study completion, an average of 1 year
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Subgroup analysis: working environment (type of hospital, type of ICU)
Time Frame: through study completion, an average of 1 year
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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.
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through study completion, an average of 1 year
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Subgroup analysis: working experience (basic training and clinical experience).
Time Frame: through study completion, an average of 1 year
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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.
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through study completion, an average of 1 year
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Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Investigators
Investigators
- Principal Investigator: Jan De Waele, MD, PhD, University Ghent
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Actual)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Other Study ID Numbers
Other Study ID Numbers
- BC-9892
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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