Artificial Intelligence Clinical Trial
— KATRINAOfficial title:
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
NCT number | NCT05303025 |
Other study ID # | BC-9892 |
Secondary ID | |
Status | Completed |
Phase | |
First received | |
Last updated | |
Start date | April 13, 2022 |
Est. completion date | October 31, 2022 |
Verified date | October 2022 |
Source | University Ghent |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
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.
Status | Completed |
Enrollment | 69 |
Est. completion date | October 31, 2022 |
Est. primary completion date | October 31, 2022 |
Accepts healthy volunteers | Accepts Healthy Volunteers |
Gender | All |
Age group | 18 Years and older |
Eligibility | Inclusion Criteria: - Medical specialist or specialist in training working in intensive care at the time of the study. Exclusion Criteria: - Age < 18 yo |
Country | Name | City | State |
---|---|---|---|
Belgium | OLV Aalst | Aalst | |
Belgium | ZNA Ziekenhuizen | Antwerpen | |
Belgium | Ghent University Hospital | Ghent |
Lead Sponsor | Collaborator |
---|---|
University Ghent | Research Foundation Flanders |
Belgium,
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | 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). | baseline | |
Primary | 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. | through study completion, an average of 1 year | |
Primary | 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. | through study completion, an average of 1 year | |
Primary | 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. | through study completion, an average of 1 year | |
Secondary | Subgroup analysis: age | Explore if there are variations in the above mentioned outcomes when taking into account the age (years) of the participants. | through study completion, an average of 1 year | |
Secondary | Subgroup analysis: gender | Explore if there are variations in the above mentioned outcomes when taking into account the gender of the participants. | through study completion, an average of 1 year | |
Secondary | 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. | through study completion, an average of 1 year | |
Secondary | 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. | through study completion, an average of 1 year |
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