Clinical trial NCT05809232: Impact of Machine Learning-based Clinician Decision Support Algorithms in Perioperative Care
Official title: Impact of Machine Learning-based Clinician Decision Support Algorithms in Perioperative Care - A Randomized Control Trial (IMAGINATIVE Trial)
Study type: INTERVENTIONAL
Overall status: NOT_YET_RECRUITING
Phase: NA
Conditions: Machine Learning
Lead sponsor: Singapore General Hospital
Sponsor class: OTHER
First posted: 2023-04-12
Enrollment: 9200
Sex: ALL
Minimum age (years): 21
Maximum age (years): 100
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: OTHER

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.


Eligibility criteria: 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

Primary outcomes:
measure: Change in perioperative mortality rates
timeFrame: Five years
Secondary outcomes:
measure: Change in potentially avoidable planned ICU admission after surgery
timeFrame: Five years
Other outcomes:
measure: Shift in adoption rate of CARES's CDS recommendations among anesthesiologists, intensivists, surgeons and nurses
timeFrame: Five years
Locations:
city: Singapore
country: Singapore
facility: Singapore General Hospital
Study officials:
References: