Modeling IN out-of-hospital emergency medical services—a scoping review of approaches and applications

Introduction Out-of-Hospital Emergency Medical Services (OHEMS) play a critical role in providing timely care for patients experiencing acute medical emergencies. Traditionally focused on rapid response and transport, OHEMS are increasingly evolving toward a more comprehensive role within the healthcare system. This scoping review aims to map existing modeling approaches, identify research streams and to examine whether recent developments in practice can also be found in the modeling literature. Methods Following the PRISMA extension for Scoping Reviews, a structured search was conducted in PubMed, Google Scholar, Semantic Scholar, and IEEE. We included English-language case studies and review articles published between 2010 and 2024 that addressed modeling, optimization, forecasting, or decision-support approaches in out-of-hospital emergency medical services. Studies were classified using the Emergency Care Pathway framework, with additional extraction of methodological approach, data source, geographical context, performance indicators and implementation perspective. Results We included 150 methodological case studies and 33 review papers. Location and relocation problems were the most frequently addressed topics, and response time served as the primary model performance evaluation indicator. The analyzed literature showed increasing methodological diversification, including simulation-optimization, online and real-time optimization, decision support systems, machine learning-based forecasting, and GIS-supported spatial analysis. In contrast, workload-aware models and equity-oriented approaches have also emerged, although they remain less prominent. A limited but growing number of scholars provide model code to support reproducibility and practical uptake. Yet, cooperation between modelers and practice remains limited. Regarding changes in the provision of OHEMS care, results of this study indicate that these have not yet been reflected by modelers. Discussion Evidence by this study indicates that OHEMS modeling has evolved from predominantly static location and coverage models toward more dynamic, data-driven, real time decision support and to some extent implementation-oriented approaches. However, many models still conceptualize OHEMS primarily as a rapid response and transport system. Derived from the review findings, we propose the incorporation of a more dimensional framework into modeling approaches that considers finance and public health structure as well as expected quality. Further, based on the notion of Right Time, Right Care, and Right Place, implementing clinically meaningful time metrics, alternative response options, workload and staff constraints, and care pathways beyond transport to hospital may prove useful.