Clinical Scorecard: Utilization of Machine Learning Technologies in Emergency Department Settings: A Comprehensive Review
At a Glance
Category
Detail
Condition
Emergency department operational and clinical challenges
Key Mechanisms
Machine learning models leveraging flexible, nonparametric algorithms to capture complex patterns for improved prediction
Target Population
Patients of all ages, genders, and ethnicities presenting to emergency departments
Care Setting
Emergency department settings
Key Highlights
Emergency departments face overcrowding, prolonged patient stays, and extended wait times exacerbated by primary care erosion and COVID-19 impacts.
Traditional clinical decision tools in EDs are static and limited, whereas machine learning offers dynamic, potentially more accurate predictive capabilities.
Systematic review focuses on ML models' clinical outcomes (mortality, treatment decisions, disposition) and operational outcomes (wait times, length of stay, cost reduction).
Guideline-Based Recommendations
Diagnosis
Utilize ML models to enhance diagnostic accuracy in emergency department settings.
Incorporate ML tools that capture complex variable interactions beyond traditional statistical models.
Management
Apply ML predictions to support clinical decision-making, triage, and treatment optimization in EDs.
Leverage ML insights to reduce patient wait times and improve disposition decisions.
Monitoring & Follow-up
Continuously evaluate ML model performance pre- and post-implementation to ensure clinical and operational effectiveness.
Monitor ML impact on mortality, length of stay, and ED-associated costs.
Risks
Recognize limitations and challenges in ML model implementation, including validity over time and usability in high-pressure ED environments.
Patient & Prescribing Data
Human patients presenting to emergency departments for any reason, across all demographics
ML models assist in predicting clinical outcomes and operational efficiencies, potentially guiding treatment and disposition decisions.
Clinical Best Practices
Engage multidisciplinary teams for ML model development, validation, and implementation in EDs.
Use rigorous systematic review and data extraction methods to assess ML model evidence.
Prioritize ML models that demonstrate improvements in both clinical outcomes and operational metrics.
by Banafshe Hosseini, Atushi Patel, Megan Landes, Samuel Vaillancourt, Muhammad Mamdani, Kevin Maruthananth, Neha Matharu, Zuha Pathan, Krishihan Sivapragasam, Onlak Ruangsomboon, Becky Skidmore, Andrew D Pinto
