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

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.

Study Overview

• Status: Completed
• Conditions: Artificial Intelligence; Decision Support Systems, Clinical; Qualitative Research
• Intervention / Treatment: Other: Survey; Other: Semi-structured group discussion

• Study Type: Observational
• Enrollment (Actual): 69

Contacts and Locations

Study Locations

• Belgium
  • Aalst, Belgium
    • OLV Aalst
  • Antwerpen, Belgium
    • ZNA Ziekenhuizen
  • Ghent, Belgium
    • Ghent University Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

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

Description

Inclusion Criteria:

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

Exclusion Criteria:

• Age < 18 yo

Study Plan

How is the study designed?

Design Details

• Observational Models: Other
• Time Perspectives: Prospective

What is the study measuring?

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 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
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

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

Collaborators and Investigators

• Sponsor: University Ghent
• Collaborators: Research Foundation Flanders
• Investigators: Principal Investigator: Jan De Waele, MD, PhD, University Ghent

Study record dates

Study Major Dates

• Study Start (Actual): April 13, 2022
• Primary Completion (Actual): October 31, 2022
• Study Completion (Actual): October 31, 2022

Study Registration Dates

• First Submitted: February 22, 2022
• First Submitted That Met QC Criteria: March 21, 2022
• First Posted (Actual): March 31, 2022

Study Record Updates

• Last Update Posted (Actual): March 22, 2023
• Last Update Submitted That Met QC Criteria: March 20, 2023
• Last Verified: October 1, 2022

More Information

Terms related to this study

• Other Study ID Numbers: BC-9892

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED
• IPD Plan Description: If possible and feasible, anonymised survey data as well as transcript data from the group discussions will be made available on request.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No