TeleEMS: Advanced Tele-Emergency Medical Systems

• TeleEMS is a next-generation tele-emergency system that integrates real-time multimodal communications and analytics to enable remote, continuous clinical care.
• It utilizes advanced architectures including mobile sensors, edge computing, and D2D networks to achieve low latency and robust coverage in diverse environments.
• System validations demonstrate high diagnostic concordance and energy-efficient performance, while challenges in scalability, privacy, and network variability remain.

TeleEMS refers to next-generation Tele-Emergency Medical Services systems that leverage real-time multimodal communications, advanced analytics, and dynamic network-aware architectures to enable remote, continuous, and actionable clinical care outside traditional hospital settings. TeleEMS architectures integrate mobile audio, video, physiological sensor data, and low-latency networking to bridge pre-hospital patient locations (homes, incident scenes, ambulances) with remote physicians, specialists, and dispatchers, optimizing both clinical workflow and patient outcomes under stringent reliability and timeliness constraints.

1. System Architectures and Communication Backbones

TeleEMS systems implement distributed pipelines comprising heterogeneous client devices and edge/server analytics. Architectures typically include:

• TeleEMS Client: Deployed on smartphones, smart glasses (e.g., Google Glass Enterprise II for EMTs), or dispatcher desktops, clients capture and transmit live video, audio, and sensor data. Clients support dynamic session joins for multi-party collaboration among bystanders, EMTs, and dispatchers.
• TeleEMS Server: Operates at the network edge (e.g., data center or cellular core) to assure low end-to-end latency and disaster resilience. It hosts integrated analytics pipelines and manages real-time inference tasks.
• EMS-Stream Backbone: An open B5G-aligned multi-party streaming system based on Janus WebRTC+RTP, offering sub-second latency, cross-device bidirectional AV flow, and zero-copy RTP forwarding into analytics modules. The backbone supports plugin extensibility for future analytics (such as additional physiological metrics or environmental monitoring) (Jin et al., 18 Nov 2025).

Beyond-5G (B5G) adaptations utilize device-to-device (D2D) multi-hop overlays to extend network coverage up to several hundred meters beyond cell edges during disaster scenarios or infrastructure degradation. D2D protocols dynamically elect relay nodes and cluster heads based on SINR, residual energy, and topological considerations, optimizing on network-wide energy efficiency and latency (AL-Mansor et al., 2022).

2. Multimodal Data Acquisition and Remote Monitoring

TeleEMS platforms incorporate multiple physiological and environmental sensing modalities:

• Wireless Body Area Networks (BANs): Sensor nodes (e.g., ECG, EEG electrodes) transmit pre-filtered analog signals over low-power RF links to receiving stations, where digitized data are processed, denoised (e.g., 0.5–40 Hz FIR band-pass for ECG, 7–13 Hz RC+FIR for EEG), and abnormalities detected. Alerts are delivered to physicians via GSM/SMS with latencies of 4–8 s and reliability of ~99% in controlled tests. Amplitude error for ECG parameters is maintained below 15% relative to clinical reference devices (GP et al., 2014).
• Ambulance Telemetry: Advanced 12-lead ECG systems, vital sign monitors, and real-time pre-filtering and packetization transmit clinical-grade waveforms via dual-mode (ADSL + 3G/4G) networks to secure data centers. Signal processing conforms to IEC 60601-2-25 for bandwidth and measurement error. Automated measurement and visualization (e.g., Pan-Tompkins QRS detection, onset/offset temporal identification, multi-lead alignment, and ST-segment deviation) facilitate rapid diagnosis (e.g., STEMI) and pre-hospital thrombolysis authorization (Asl et al., 2018).
• Live Video/Audio and Multimodal Inference: Scene-based video from smartphones or head-worn camera (e.g., EMT glasses) is analyzed using rPPG for facial heart rate (6 s windows, 0.75–2.5 Hz filtering, TSCAN model). Live audio feeds are transcribed (via Whisper-medium) and parsed for key symptoms using domain-specialized LLMs (e.g., EMSLlama, leveraging LoRA adapters on Llama-3-8B). Analytics modules output State Awareness in the form of normalized symptoms, protocols, medication recommendations, and procedural guidance in real time, overlaid on client interfaces (Jin et al., 18 Nov 2025).

3. Dynamic Routing, Bandwidth Management, and Optimization

Rural and disaster deployments of TeleEMS systems are constrained by highly variable, route-dependent bandwidth ranging from multiparty 4G/5G down to zero-coverage. TeleEMS addresses these constraints with:

• Route-Aware Scheduling: Ambulance routing is formalized as an NP-hard resource-constrained shortest-path problem on a directed graph representing intersections and road segments. Each edge is annotated by travel time and a binary coverage flag derived from real-world cellular profiling every 4 s. Physicians set disease-specific trade-off weights (α), maximum total and continuous coverage break durations (D₁, D₂). K-shortest-paths heuristic enumerates feasible routes, filtering on communication outage intervals and re-ranking by a convex objective mixing total travel time and aggregated outage (Hosseini et al., 2017). Empirical results favor the longer, higher coverage route (e.g., 47 min / 92% coverage) for ischemic stroke, and the shorter, lower coverage route (35 min / 70% coverage) for hemorrhagic presentations.
• Physiology-Aware Adaptive Streaming: Bandwidth allocation to concurrent clinical multimedia (e.g., vitals, video, imaging) is optimized at each decision epoch by maximizing weighted sum utilities, where weights are assigned based on the patient’s real-time physiological state. The framework enforces hard constraints on total measured downlink bandwidth, stream-specific minima, and codec-imposed maxima. Real-time ambulance route profiling data (median DL 0.50–0.92 Mbps, 15–25% outage rate) inform the mapping of clinical priorities to available network resources (Hosseini et al., 2017).
• Energy-Efficient Multi-hop Networking: D2D relays and cluster heads judiciously balance coverage, reliability (target PER ≥ 99.999%, latency < 10 ms), and energy expenditure. The energy efficiency of each hop and the entire chain is formalized, with observed end-to-end gains up to 32% compared to infrastructure-only or baseline D2D benchmarks. Dynamic reassignment of relays/CHs enables real-time adaptation to node failure or depletion (AL-Mansor et al., 2022).

4. Real-Time Analytics, LLM-Driven Communication Enhancement, and Dispatcher Support

TeleEMS workflows integrate advanced automated analytics to address both clinical and communications bottlenecks:

• LLM-Based Speech Reconstruction and RAG: VoIP speech is transcribed via AssemblyAI, then processed with a Retrieval-Augmented Generation module (BART fine-tuned for emergency call data). The system is robust to packet losses and jitter, filling contextually appropriate gaps in incomplete utterances using TF-IDF+FAISS retrieval of prior transcripts. Severity scores and prioritization leverage a composite of rule-based keyword presence, DistilBERT emotion analysis, and contextual knowledge. Calls are dynamically ranked and queued based on composite priority scores (Venkateshperumal et al., 9 Dec 2024).
• Metrics and Impact: The end-to-end LLM-based pipeline reduces dispatcher assessment time by >50%, improves conceptual precision by ~40%, and achieves 100% conceptual match on benchmarks relative to baseline systems without LLM reconstruction. End-to-end latency for analytics and UI delivery is measured at 0.3–0.5 s per 5 s audio chunk. For video-based symptom and protocol inference, exact-match for EMSLlama is 0.89 vs. 0.57 for GPT-4o, and real-time fusion of vitals+text outperforms unimodal inference in both accuracy and variance (Jin et al., 18 Nov 2025).

5. Quality-of-Service Guarantees, System Evaluation, and Clinical Integration

TeleEMS reliability and scalability are supported by:

• Empirical Wireless Profiling: Route-dependent bandwidth statistics (mean/median, empirical outage rates) are collected with Android-based profilers sampling all major US carriers every 4 s. These inform dynamic QoS decision-making, including pre-transport route selection and bandwidth allocation. Packet loss, jitter, and fallback performance (e.g., to SMS/SAT at <50 kbps) are quantified and mitigated via dynamic redundancy and prioritization schemes (Hosseini et al., 2017).
• Clinical System Validation: In Tehran EMS deployment (45 MI cases, 5 units), 96% data transmission success, ~6 s mean latency, and >94% diagnostic concordance with in-hospital ECGs were observed (Asl et al., 2018). The RF BAN system demonstrated <15% error in ECG features, 12–8 dB SNR improvement with digital FIR denoising, and >95% sensitivity to synthetic arrhythmias (GP et al., 2014). Video analytics pipelines maintained sub-second AV latencies even during network stress testing (Jin et al., 18 Nov 2025).

6. Limitations, Challenges, and Prospective Directions

Several limitations are recognized in the current generation of TeleEMS:

• Coverage Modeling: Binary (covered/uncovered) mapping may obscure the effects of low-bandwidth degradations; true multi-rate models and multi-network aggregation (cellular, SAT, WiFi) are needed for patient-level utility maximization (Hosseini et al., 2017).
• Scalability and Robustness: Field deployments are pending for most recent video analytics and D2D routing solutions; variabilities in ambient conditions (lighting, noise), device heterogeneity, and energy constraints require further investigation (Jin et al., 18 Nov 2025, AL-Mansor et al., 2022).
• Privacy and Regulatory: HIPAA/GDPR compliance, data integrity, mutual authentication, and auditability of clinical interactions are essential for central platform adoption (Asl et al., 2018, Venkateshperumal et al., 9 Dec 2024).
• Extensibility: Integration with EHR (e.g., HL7/FHIR), AI-based decision support (e.g., STEMI detection, arrhythmia alerting), and future multi-modal relay (drone, air ambulance) are active research domains (Hosseini et al., 2017, Asl et al., 2018).

7. Clinical and Operational Implications

TeleEMS platforms shift the paradigm of emergency care delivery by enabling:

• Pre-arrival clinical intervention: Bystander and EMTs receive real-time, context-adaptive instructions powered by automated analytics (e.g., cardiac arrest protocol selection, medication dosage).
• Reduced time-to-treatment: Early diagnosis and data-driven triage allow for earlier specialist engagement and potentially improved patient survival, particularly for STEMI, stroke, and seizure cases.
• Resilient operations beyond the hospital: D2D protocols, multi-path routing, and agent-based bandwidth allocation underpin operation in partial or full infrastructure outages, extending coverage into rural and disaster-affected zones (AL-Mansor et al., 2022, Jin et al., 18 Nov 2025).
• Integration with dispatcher and clinical workflows: Embedded GUI modules expose configurable priorities (α, D₁, D₂), deliver streaming analytics, and support real-time rerouting and adaptive resource allocation (Hosseini et al., 2017, Hosseini et al., 2017).

Collectively, TeleEMS platforms synthesize advances in multimodal sensing, deep learning, network optimization, and clinical software to deliver a scalable, resilient, and semi-autonomous pre-hospital care architecture. Current research focuses on real-world pilot deployment, expansion of analytics modules, and rigorous outcome evaluation to validate and refine TeleEMS in diverse operational environments.