Artificial IntelligenceKATRINANCT05303025
Qualitative Research Among Physicians and Junior Doctors Into the Preconditions for Implementing a CDSS Based on AI in the ICU
An observational study of Artificial Intelligence and Qualitative Research, run by University Ghent at 3 sites in Belgium.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Artificial Intelligence, Qualitative Research, Decision Support Systems, Clinical
- What is being tested?
- Survey and Semi-structured group discussion
- How many people
- 69 participants
- Where
- 3 sitesBelgium
- Study dates
- 13 Apr 2022to 31 Oct 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Medical specialists or specialists in training working in intensive care at the time of the study.
Who may be able to join
- Medical specialist or specialist in training working in intensive care at the time of the study.
Who cannot join
- Age < 18 yo
Trial locations and contacts
3 sites in 1 country.
Belgium3 sites
OLV Aalst
Aalst
ZNA Ziekenhuizen
Antwerp
Ghent University Hospital
Ghent
About this study
Official 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
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.
- Being tested
- Survey, Semi-structured group discussion
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
SurveyOther
Survey to acquire baseline demographic information as well as information regarding professional experience, working environment and attitudes towards artificial intelligence.
Semi-structured group discussionOther
Semi-structured group discussion.
What is being measured
The primary outcome is the main question the study is designed to answer.
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).
Measured: 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.
Measured: 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.
Measured: 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.
Measured: through study completion, an average of 1 year
Secondary outcomes (4)
Subgroup analysis: age
Explore if there are variations in the above mentioned outcomes when taking into account the age (years) of the participants.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: through study completion, an average of 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted31 Mar 2022
- Study start13 Apr 2022
- Primary completion31 Oct 2022
- Study completion31 Oct 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Artificial IntelligenceStudies of the same condition, open and under way.
- More trials run by University GhentThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Prospective
- Enrolment
- 69 participants (actual)
Sponsor and investigators
- Lead sponsor
- University Ghent
- Sponsor type
- University, hospital or other
- Collaborators
- Research Foundation Flanders
- Responsible party
- Sponsor
- Investigators
- Jan De Waele, MD, PhD, Principal investigator, University Ghent
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05303025
- Sponsor's protocol ID
- BC-9892
- Acronym
- KATRINA
- Status last verified
- Oct 2022
- First submitted
- 22 Feb 2022
- Last updated
- 22 Mar 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: If possible and feasible, anonymised survey data as well as transcript data from the group discussions will be made available on request.
Study at a glance
From the registry record, last updated by the sponsor on 22 Mar 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official 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
- ClinicalTrials.gov ID
- NCT05303025
- Acronym / sponsor ID
- KATRINA / BC-9892
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 69 participants (actual)
- Lead sponsor
- University GhentUniversity, hospital or other
- Collaborators
- Research Foundation Flanders
- Study start
- 13 Apr 2022 (actual)
- Primary completion
- 31 Oct 2022 (actual)
- Study completion
- 31 Oct 2022 (actual)
- First posted
- 31 Mar 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Other
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Medical specialists or specialists in training working in intensive care at the time of the study.
Key inclusion criteria (1 listed)
- Medical specialist or specialist in training working in intensive care at the time of the study.
Key exclusion criteria (1 listed)
- Age < 18 yo
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| SurveySurvey to acquire baseline demographic information as well as information regarding professional experience, working environment and attitudes towards artificial intelligence. | Other | – |
| Semi-structured group discussionSemi-structured group discussion. | Other | – |
Primary outcome
Baseline attitudes towards artificial intelligence and big data in medicine
Time frame: 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
Time frame: 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.
Time frame: 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.
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
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.
4 secondary outcomes
Subgroup analysis: age
Time frame: through study completion, an average of 1 year
Subgroup analysis: gender
Time frame: through study completion, an average of 1 year
Subgroup analysis: working environment (type of hospital, type of ICU)
Time frame: through study completion, an average of 1 year
Subgroup analysis: working experience (basic training and clinical experience).
Time frame: through study completion, an average of 1 year
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Mar 2023.
- Individual participant dataSharing planned: Undecided. If possible and feasible, anonymised survey data as well as transcript data from the group discussions will be made available on request.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Jan De Waele, MD, PhD principal investigator
University Ghent
3 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Artificial Intelligence and Qualitative Research, run by University Ghent at 3 sites in Belgium.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 22 Mar 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in Belgium. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Baseline attitudes towards artificial intelligence and big data in medicine (time frame: baseline). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 Mar 2023.