About this trial
Clinical rounds in the intensive care unit (ICU) involve substantial manual documentation. Retrieving the correct protocol text and structuring notes at the bedside is time-consuming and may contribute to variation in documentation quality. Modern artificial intelligence (AI) can help structure existing information and automate protocol look-ups within a restricted, manually selected document set.
The tool evaluated in this study acts as an AI-based informational assistant for clinicians. It (1) pre-populates a standardized physical-exam and daily-rounds format, (2) prepares a concise ICU course/overview using predefined formatting, and (3) retrieves relevant passages from protocols to enable rapid consistency checks by the clinician.
The AI-based informational assistant does not provide treatment recommendations or patient-specific advice; all outputs require clinician verification and clinical responsibility remains with the physician.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
ICU physician (nurse practicioner, resident, or staff intensivist) at the Erasmus MC.
Signed informed-consent for study participation.
Disqualifiers
None
Trial population
ICU physicians (nurse practicioners, residents, and staff intensivists) working at the Adult ICU of Erasmus MC.
Trial design
Cohort
Prospective
Treatments tested in this trial
Not listed
Trial groups
Trial outcomes
Primary outcomes
Implementation outcomes acceptability, appropriateness, and feasibility
The mean scores and standard deviations of the 5-point Likert scale (1 = strongly disagree, 2 = disagree, 3 = undecided, 4 = agree, 5 = strongly agree) closed-ended questions of the survey on the physicians' perspectives will be calculated. Standardised questionnaires AIM, IAM and FIM are used.
Secondary outcomes
Perceived time saved when using the AI-based informational assistant during ICU rounds
The mean scores and standard deviations of the 5-point Likert scale closed-ended questions of the survey on the physicians' perceptions on retrieval speed.
Task-based efficiency, including time to (i) produce a structured rounds note and (ii) retrieve relevant protocol text
Timed predefined ICU round documentation tasks with and without AI-based informational assistant. Time difference will be calculated.
Perceived usefulness, clarity, and trustworthiness
The mean scores and standard deviations of the 5-point Likert scale (1 = strongly disagree, 2 = disagree, 3 = undecided, 4 = agree, 5 = strongly agree) closed-ended questions of the survey on the physicians' perspectives will be calculated.
Adoption and use, including frequency of use, retention over time, and interaction patterns (e.g., number/type of edits, use cases, feature use)
Adoption will be determined by frequency of use (interactions per participant per week) and retention (continued use over time), expressed as counts and proportions. Fidelity will be determined by the misusage per participant, reported as counts and proportions. Adoption and fidelity will be aggregated at both participant and cohort level. Interaction logs will be used to characterize use patterns, including number and type of edits, use cases and feature usage.
Locations
Sponsors and contacts
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Willemijn Berkhout
Lead sponsor
Erasmus Medical Center
Sponsor institution
This trial is not recruiting at the moment. You can still explore other options: