Machine learning assisted differentiation of low acuity patients at dispatch: The MADLAD randomized controlled trial

Clinical Snapshot

Key Findings

• P Value: p = 0.047

• Effect Size: 68.3% vs 62.5% correct prioritization (5.8% absolute difference)

• Primary Outcome: Correct dispatch prioritization based on NEWS 2 scores

• Nnt Or Sensitivity: NNT = 17 (number of dispatch decisions needed to achieve one additional correct prioritization)

• Confidence Interval: 95% CI [1.00, 1.63]

Clinical Application

Requires integration of ML systems into existing dispatch protocols and staff training, with ongoing validation and maintenance needs Potentially relevant to Australian ambulance services, though would require validation within local dispatch protocols and integration with existing triage systems used by state ambulance services Emergency medical dispatch centers managing resource-constrained situations with low-priority ambulance calls

Abstract

BACKGROUND: Resource Constrained Situations (RCS) at Emergency Medical Dispatch centers where there are more patients requiring an ambulance than there are available ambulances are common. Machine Learning (ML) techniques offer a promising but largely untested approach to assessing relative risks among these patients. The study aims to establish whether the provision of ML-based risk scores predicting patient outcomes improves the ability of dispatchers to identify patients at high risk for deterioration in RCS and dispatch the first available ambulance to them. METHODS AND FINDINGS: We performed a parallel-group, randomized trial of adult patients assessed by a dispatch nurse at two study sites in Sweden as requiring a low-priority ambulance response in RCS. Patients were randomized 1:1 to be prioritized with the aid of an ML-based risk assessment tool, or per current clinical practice. The primary outcome was defined in terms of whether the first available ambulance was sent to the patient with the highest National Early Warning Score (NEWS 2) based on subsequently collected vital signs. A total of 1,245 RCS were included in the study. In the intervention arm, 68.3% of RCS were assessed correctly per the primary outcome versus 62.5% in the control group, corresponding to an odds ratio of 1.28 (95% CI [1.00, 1.63], p = 0.047). This study was limited to only patients determined to require a low-priority ambulance response in two Swedish regions, and was underpowered for the primary outcome due to a smaller than expected sample size. CONCLUSION: This study suggests that clinical ML-based decision support tools may have the ability to influence care provider decisions and improve their capacity to rapidly differentiate between high- and low-risk patients at dispatch. Further research should establish the suitability of these tools in larger cohorts, for patients with both higher- and lower-levels of priority, and in other settings. The trial was registered at ClinicalTrials.gov (NCT04757194).

References

1. 1.Spangler, D. N., Morelli, S., Smekal, D., Edmark, L., & Blomberg, H. (2026). Machine learning assisted differentiation of low acuity patients at dispatch: The MADLAD randomized controlled trial. PLoS Medicine. https://doi.org/10.1007/s11023-012-9281-3