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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.
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| Measure | Description | Time Frame |
|---|---|---|
| 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. | Before integration of the AI-based informational assistant and 4-, 8-, and 12-weeks after integration. |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | 12-weeks after integration of the AI-based informational assistant . |
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Inclusion Criteria:
Exclusion Criteria:
- Physicians not expected to work on the ICU during the study period will not be approached.
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ICU physicians (nurse practicioners, residents, and staff intensivists) working at the Adult ICU of Erasmus MC.
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| 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. |
| Before integration of the AI-based informational assistant and 12-weeks after integration |
| 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. | Before integration of the AI-based informational assistant and during the 12-weeks utilization. |
| 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. | During the 12-weeks utilization of the AI-based informational assistant. |
| Technical output quality | Outputs is reviewed on accuracy, recall, precision, groundedness, contextual usefulness, and hallucination presence. Reported as counts and proportions. | Before integration of the AI-based informational assistant and during the 12-weeks utilization. |
| Trust in the system, perceived workload, and task satisfaction | 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 S-TIAS and NASA-TLX are used. | 12-weeks after integration of the AI-based informational assistant. |