Qualitative Research Among Physicians and Junior Doctors Into the Preconditions for Implementing a CDSS Based on AI in the ICU

Study Overview
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Brief Summary
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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.

Official Title
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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

Conditions
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Artificial Intelligence, Qualitative Research, Decision Support Systems, Clinical

Intervention / Treatment
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* Other: Survey
* Other: Semi-structured group discussion


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Medical specialist or specialist in training working in intensive care at the time of the study. Exclusion Criteria: Age < 18 yo

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Intervention/Treatment |
| --- |
|Other: Survey|Survey to acquire baseline demographic information as well as information regarding professional experience, working environment and attitudes towards artificial intelligence.|
|Other: Semi-structured group discussion|Semi-structured group discussion.|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 
| 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 | 
| 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 | 
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 
| 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 | 
| 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 | 
| 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 |