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Impact and Safety of AI in Decision Making in the ICU: a Simulation Experiment

NCT05495438 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 38

Last updated 2023-02-27

No results posted yet for this study

Summary

The impact of deploying artificial intelligence (AI) in healthcare settings in unclear, in particular with regards to how it will influence human decision makers. Previous research demonstrated that AI alerts were frequently ignored (Kamal et al., 2020 ) or could lead to unexpected behaviour with worsening of patient outcomes (Wilson et al., 2021 ). On the other hand, excessive confidence and trust placed in the AI could have several adverse consequences including ability to detect harmful AI decisions, leading to patient harm as well as human deskilling. Some of these aspects relate to automation bias.

In this simulation study, the investigators intend to measure whether medical decisions in areas of high clinical uncertainty are modified by the use of an AI-based clinical decision support tool. How the dose of intravenous fluids (IVF) and vasopressors administered by doctors in adult patients with sepsis (severe infection with organ failure) in the ICU), changes as a result of disclosing the doses suggested by a hypothetical AI will be measured. The area of sepsis resuscitation is poorly codified, with high uncertainty leading to high variability in practice. This study will not specifically mention the AI Clinician (Komorowski et al., 2018). Instead, the investigators will describe a hypothetical AI for which there is some evidence of effectiveness on retrospective data in another clinical setting (e.g. a model that was retrospectively validated using data from a different country than the source data used for model training) but no prospective evidence of effectiveness or safety. As such, it is possible for this hypothetical AI to provide unsafe suggestions. The investigators will intentionally introduce unsafe AI suggestions (in random order), to measure the sensitivity of our participants at detecting these.

Conditions

Interventions

OTHER

Hypothetical AI

n/a - There is no intervention. Clinicians will review the suggestions of a hypothetical AI

Sponsors & Collaborators

  • University of York

    collaborator OTHER

  • Imperial College London

    lead OTHER

Principal Investigators

  • Matthieu Komorowski, MD, PhD · Imperial College London

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2022-07-22
Primary Completion
2022-10-31
Completion
2022-10-31

Countries

  • United Kingdom

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05495438 on ClinicalTrials.gov