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

Not Applicable

Not yet recruiting

• Conditions: Machine Learning
• Interventions: Other: CARES-guided Group

• Registration Number: NCT05809232
• Lead Sponsor: Singapore General Hospital

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.

Detailed Description

Not available

Study Timeline

• Status Verified: 2023-03
• Study First Submitted: 2023-03-14
• Study First Submitted QC: 2023-03-29
• Last Update Submitted: 2023-03-29
• Study First Posted: 2023-04-12
• Last Update Posted: 2023-04-12
• Study Start: 2023-05
• Primary Completion: 2027-07
• Study Completion: 2027-12

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 9200

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 & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Arm && Interventions

Group	Intervention	Description
CARES-guided Group	CARES-guided Group	The Intervention

Primary Outcome Measures

Name	Time	Method
Change in perioperative mortality rates	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.

Secondary Outcome Measures

Name	Time	Method
Change in potentially avoidable planned ICU admission after surgery	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

Trial Locations

Locations (1)

Singapore General Hospital; 🇸🇬 Singapore, Singapore