Artificial Intelligence, Decision Support Systems, Clinical, Qualitative Research
Conditions
Brief summary
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.
Interventions
OTHERSurvey
Survey to acquire baseline demographic information as well as information regarding professional experience, working environment and attitudes towards artificial intelligence.
OTHERSemi-structured group discussion
Semi-structured group discussion.
Sponsors
Research Foundation Flanders
University Ghent
Study design
Observational model
OTHER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* Medical specialist or specialist in training working in intensive care at the time of the study.
Exclusion criteria
* Age \< 18 yo
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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. | 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 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. |
| Baseline attitudes towards artificial intelligence and big data in medicine | baseline | 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). |
| Identify subdomains of the antimicrobial stewardship cycle with potential for AI/Big data application | through study completion, an average of 1 year | 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. |
| Identify perceived potential benefits and harms when applying AI in the antimicrobial stewardship cycle. | through study completion, an average of 1 year | Identify perceived potential benefits and harms when applying AI in the antimicrobial stewardship cycle through a group discussion. Reporting: frequencies. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Subgroup analysis: age | through study completion, an average of 1 year | Explore if there are variations in the above mentioned outcomes when taking into account the age (years) of the participants. |
| Subgroup analysis: gender | through study completion, an average of 1 year | Explore if there are variations in the above mentioned outcomes when taking into account the gender of the participants. |
| Subgroup analysis: working environment (type of hospital, type of ICU) | through study completion, an average of 1 year | 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. |
| Subgroup analysis: working experience (basic training and clinical experience). | through study completion, an average of 1 year | 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. |
Countries
Belgium
Outcome results
None listed