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

NCT05495438 | Completed

• 1 other identifier: 22CX7592
• Study Type: observational
• Target: 38
• Locations: 1 country
• Sites: 1

Brief 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

Sepsis

Keywords

Artificial Intelligence

Outcome Measures

Primary Outcomes (1)

• Influence of AI on ICU Clinicians

  Influence of AI on ICU Clinicians, this will be divided into the following categories: overall and stratified by safe/unsafe, junior/senior and positive/negative attitude towards AI.

  3 months

Secondary Outcomes (4)

• Participants' characteristics

  3 months

• Trust in AI

  3 months

• Confidence in participants' decisions

  3 months

• Proportion of time with attention on AI explanation

  3 months

Study Arms (1)

ICU Clinicians

Other: Hypothetical AI

Interventions

Hypothetical AI OTHER

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

ICU Clinicians

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Junior (senior house officer) or senior (registrar/fellow/consultant) ICU doctor

You may qualify if:

• Junior (senior house officer) or senior (registrar/fellow/consultant) ICU doctor

Sponsors & Collaborators

• Imperial College London: lead
• University of York: collaborator

Study Sites (1)

Imperial College Hospitals NHS Trust

London, W2 1PG, United Kingdom

Location

MeSH Terms

Conditions

Sepsis

Condition Hierarchy (Ancestors)

Infections; Systemic Inflammatory Response Syndrome; Inflammation; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Matthieu Komorowski, MD, PhD

  Imperial College London

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 4, 2022
• First Posted: August 10, 2022
• Study Start: July 22, 2022
• Primary Completion: October 31, 2022
• Study Completion: October 31, 2022
• Last Updated: February 27, 2023

Record last verified: 2023-02

Data Sharing

• IPD Sharing: Will not share

Locations

GB (1)