Impact of Machine Learning-based Clinician Decision Support Algorithms in Perioperative Care (IMAGINATIVE)

March 29, 2023 updated by: Singapore General Hospital

Impact of Machine Learning-based Clinician Decision Support Algorithms in Perioperative Care - A Randomized Control Trial (IMAGINATIVE Trial)

Predicting surgical risks are important to patients and clinicians for shared decision making process and management plan. The study team aim to conduct a hybrid type 1 effectiveness implementation study design. A Randomized Controlled Trial where participants undergoing surgery In Singapore General Hospital (SGH) will be allocated in 1:1 ratio to CARES-guided (unblinded to risk level) or to unguided (blinded to risk level) groups. All participants undergoing elective surgeries in SGH will be considered eligible for enrolment into the study. For elective surgeries, the participants will mainly be recruited from Pre-admission Centre. The outcome of this study will help patients and clinicians make better decisions together. Firstly, the deployment of the CARES model in a live clinical environment could potentially reduce postoperative complications and improve the quality of surgical care provision. The findings from this study would allow fine-tuning of CARES as well as further deployment of additional risk models for specific complications other than Mortality and ICU stay. This in turn would translate to better health for the surgical population and improved cost-effectiveness. This is significant as the surgical population is expected to continuously grow due to improved access to care, better technologies and the aging population. Secondly, IMAGINATIVE will be instrumental in improving our understanding of the deployment strategies for AI/ML predictive models in healthcare. Models such as CARES could be the standard of care in the future if proven to improve the health outcomes of patients. As model deployments are costly and can be disruptive to the EMR processes, this study would be the initial spark for future deployment and health services research focusing on improving the value of these model deployments.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Study Type

Interventional

Enrollment (Anticipated)

9200

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • Singapore
      • Singapore, Singapore
        • Singapore General Hospital
        • Sub-Investigator:
          • Ecosse Lamoureux, PHD
        • Contact:
        • Principal Investigator:
          • Hairil Rizal Abdullah, MMED
        • Sub-Investigator:
          • Elaine Lum, PHD
        • Sub-Investigator:
          • Nan Liu, PHD
        • Sub-Investigator:
          • Mengling Feng, PHD
        • Sub-Investigator:
          • Jacqueline Sim Xiu Ling, MBBS
        • Sub-Investigator:
          • Brian Goh Kim Poh, MBBS
        • Sub-Investigator:
          • Gek Hsiang Lim, MSC
        • Sub-Investigator:
          • Marcus Ong Eng Hock, MPH

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Description

Inclusion Criteria:

  1. Patients >=21 Years old
  2. Patients going for elective surgery

For semi-structured interview:

1. Any clinician or nurse that used CARES during the research trial

Exclusion Criteria:

  1. Patients with reduced mental capacity
  2. Patients who are unable to give consent

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Other
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Active Comparator: CARES-guided Group
The Intervention
Other: CARES-guided Group
Participants randomised to the CARES-guided arm will have their CARES-score calculated and entered into the Pre-Anesthesia Assessment electronic form within the Electronic Medical Records (EMR). This score and its relevant advisories will be prominently displayed on this electronic form. (Participants on this arm will receive this intervention in addition to the routine practice).
No Intervention: Non CARES-Guided Group
The control - Participants randomized to the control arm will continue to have their routine Pre-Anesthesia Assessment on the electronic form, without the CARES calculator calculations, as per current practice

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Change in perioperative mortality rates
Time Frame: Five years
To assess the effectiveness of the Machine Learning Clinical Decision Support (ML-CDS). Hypothesis: The CARES-guided group will have a 30% relative reduction in one-year mortality rate due to the increased clinician awareness of the risks.
Five years

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Change in potentially avoidable planned ICU admission after surgery
Time Frame: Five years
To assess the effectiveness of the ML-CDS algorithm in optimizing ICU bed utilization, which is an important and costly hospital resource Hypothesis: There will be a 25% relative reduction in the potentially avoidable planned ICU admission after surgery in the CARES-guided group
Five years

Other Outcome Measures

Outcome Measure
Measure Description
Time Frame
Shift in adoption rate of CARES's CDS recommendations among anesthesiologists, intensivists, surgeons and nurses
Time Frame: Five years
To assess adoption and acceptability, and to understand user experience and concerns regarding an ML based prediction application designed to improve patient safety in a clinical setting. Hypothesis: There is high adoption of CARES's CDS recommendations among anesthesiologists, intensivists, surgeons and nurses respectively.
Five years

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Singapore General Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Anticipated)

May 1, 2023

Primary Completion (Anticipated)

July 1, 2027

Study Completion (Anticipated)

December 1, 2027

Study Registration Dates

First Submitted

March 14, 2023

First Submitted That Met QC Criteria

March 29, 2023

First Posted (Actual)

April 12, 2023

Study Record Updates

Last Update Posted (Actual)

April 12, 2023

Last Update Submitted That Met QC Criteria

March 29, 2023

Last Verified

March 1, 2023

More Information

Terms related to this study

Other Study ID Numbers

  • IMAGINATIVE Trial

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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