AI-based Informational Assistant for Automated Point-of-care Documentation and Protocol Retrieval

Evaluation of an AI-based Informational Assistant for Automated Point-of-care Documentation and Protocol Retrieval in the Intensive Care Unit

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Artificial Intelligence; Usability

• Study Type: Observational
• Enrollment (Estimated): 25

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: N/A

• Sampling Method: Non-Probability Sample

Study Population

ICU physicians (nurse practicioners, residents, and staff intensivists) working at the Adult ICU of Erasmus MC.

Description

Inclusion Criteria:

• ICU physician (nurse practicioner, resident, or staff intensivist) at the Erasmus MC.
• Signed informed-consent for study participation.

Exclusion Criteria:

- Physicians not expected to work on the ICU during the study period will not be approached.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	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.

Secondary Outcome Measures

Outcome Measure	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 .
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.

Collaborators and Investigators

• Sponsor: Willemijn Berkhout

Study record dates

Study Major Dates

• Study Start (Estimated): July 1, 2026
• Primary Completion (Estimated): October 1, 2026
• Study Completion (Estimated): December 1, 2026

Study Registration Dates

• First Submitted: March 18, 2026
• First Submitted That Met QC Criteria: March 23, 2026
• First Posted (Actual): March 25, 2026

Study Record Updates

• Last Update Posted (Actual): March 25, 2026
• Last Update Submitted That Met QC Criteria: March 23, 2026
• Last Verified: March 1, 2026

More Information

Terms related to this study

• Other Study ID Numbers: 15243

Drug and device information, study documents

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