Impact of Machine Learning-based Clinician Decision Support Algorithms in Perioperative Care
IMAGINATIVE
1 other identifier
interventional
9,200
1 country
1
Brief Summary
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for not_applicable
Started May 2023
Longer than P75 for not_applicable
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
March 14, 2023
CompletedFirst Posted
Study publicly available on registry
April 12, 2023
CompletedStudy Start
First participant enrolled
May 1, 2023
CompletedPrimary Completion
Last participant's last visit for primary outcome
July 1, 2027
ExpectedStudy Completion
Last participant's last visit for all outcomes
December 1, 2027
April 12, 2023
March 1, 2023
4.2 years
March 14, 2023
March 29, 2023
Conditions
Outcome Measures
Primary Outcomes (1)
Change in perioperative mortality rates
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 Outcomes (1)
Change in potentially avoidable planned ICU admission after surgery
Five years
Other Outcomes (1)
Shift in adoption rate of CARES's CDS recommendations among anesthesiologists, intensivists, surgeons and nurses
Five years
Study Arms (2)
CARES-guided Group
ACTIVE COMPARATORThe Intervention
Non CARES-Guided Group
NO INTERVENTIONThe 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
Interventions
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).
Eligibility Criteria
You may qualify if:
- Patients \>=21 Years old
- Patients going for elective surgery
- For semi-structured interview:
- \. Any clinician or nurse that used CARES during the research trial
You may not qualify if:
- Patients with reduced mental capacity
- Patients who are unable to give consent
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Singapore General Hospital
Singapore, Singapore
Related Publications (1)
Abdullah HR, Brenda TPY, Loh C, Ong M, Lamoureux E, Lim GH, Lum E. Protocol for the impact of machine learning-based clinician decision support algorithims in perioperative care (IMAGINATIVE) in Singapore general hospital : a large prospective randomised controlled trial. BMJ Open. 2024 Dec 20;14(12):e086769. doi: 10.1136/bmjopen-2024-086769.
PMID: 39806608DERIVED
Central Study Contacts
Study Design
- Study Type
- interventional
- Phase
- not applicable
- Allocation
- RANDOMIZED
- Masking
- NONE
- Purpose
- OTHER
- Intervention Model
- PARALLEL
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
March 14, 2023
First Posted
April 12, 2023
Study Start
May 1, 2023
Primary Completion (Estimated)
July 1, 2027
Study Completion (Estimated)
December 1, 2027
Last Updated
April 12, 2023
Record last verified: 2023-03
Data Sharing
- IPD Sharing
- Will not share