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Completed

NCT Number: NCT05303025

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

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Key information

Conditions

Artificial Intelligence Decision Support Systems, Clinical Qualitative Research

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

OLV Aalst, Aalst, Belgium

Loading trial locations.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Medical specialist or specialist in training working in intensive care at the time of the study.

Exclusion criteria

  • Age < 18 yo

Treatment and study plan

Survey

Other

Survey to acquire baseline demographic information as well as information regarding professional experience, working environment and attitudes towards artificial intelligence.

Semi-structured group discussion

Other

Semi-structured group discussion.

Primary outcomes

  1. 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).

  2. 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.

  3. 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.

  4. 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.

Secondary outcomes

  1. Subgroup analysis: age

    Time frame: 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.

  2. Subgroup analysis: gender

    Time frame: 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.

  3. Subgroup analysis: working environment (type of hospital, type of ICU)

    Time frame: 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.

  4. Subgroup analysis: working experience (basic training and clinical experience).

    Time frame: 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.

Sponsors and collaborators

Lead sponsor

University Ghent

Other

Collaborators

  • Research Foundation Flanders

Registry information

Official study 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

Acronym: KATRINA

Important dates

Study start
2022
Primary completion
2022
Study completion
2022
First posted
Mar 31, 2022
Registry last updated
Mar 22, 2023

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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